Categories
Blog Admin

Introduction / Table of Contents

The most recent post is the next post down!

I intend to use this site to document my journey down the path of nerdiness (past, present, and future). I’ve been learning over the years from various sites like what I hope this one becomes, and want to give back. I have a wide variety of topics I’d like to cover. At a minimum, posting about my activities will help me document what I learned to refer back in the future. I’ll also post about projects we do ourselves around the house instead of hiring professionals, saving big $$$$ in the process. Hope you enjoy the journey with me!

Below are some topic I plan on covering (I’ve already done something with every one of these and plan on documenting it):

  1. RTL-SDRs (receiving signals from your electric meter, ADS-B, general radio stuff)
  2. Virtual machines and my homelab setup
  3. Home automation / smart home (Home Assistant, Tasmota, Phillips Hue bulbs, automating various tasks throughout the house)
  4. My mini solar setup (2x300W panels) and not-so-mini battery backup (8x272Ah LiFePO4 batteries – should yield 7ish kWh of storage)
  5. Remote control aircraft running Arduplane with video downlink and two-way telemetry
  6. General computer stuff (building them, what I use mine for, Hyper-V)
  7. Home network (Ubiquiti setup, VLANs, wiring the house with CAT6, IP security cameras on Blue Iris)
  8. Formation of my LLC if anyone wants to hear about that
  9. The wheel options trading strategy
  10. Cryptocurrency (mining focus)
  11. SCADA (my day job)
  12. 3D printing
  13. Engine tuning (for my old WRX and new F-150)
  14. All the cool things you can do with a Raspberry pi and other SBCs
  15. Arduino/ESP32/ESP8266 automation devices
  16. My electric bikes
  17. Microsecond accurate Raspberry Pi NTP appliance using GPS pulse per second (PPS) timing signals
  18. DIY multi-zone sprinkler system install
  19. Drone survey of property
  20. Securing this WordPress site from hackers (Fail2Ban at both WordPress and system service level)
  21. Backing up WordPress sites
  22. General Linux tips/tricks
  23. VPNs (openvpn and wireguard)
Categories
Python XPlane

Coding a wing leveler autopilot in X-Plane with Python

Introduction

Continuing from the first post, where we hooked up X-Plane to our Python code, we will build a wing leveler today. The first post was just about making sure we could get data into and out of X-Plane. Today will add a few features to our XPlane autopilot written in Python.

  • A control loop timer (we will be targeting a loop frequency of 10 Hz, or 10 updates per second)
  • Additional data feeds from X-Plane
  • Two PID controllers, one for roll, one for pitch
  • Some debugging output to keep track of what the PIDs are doing

The full code will be at the end of this post.

Video Link

Python Tutorial: code a wing leveler in X-Plane using PID loops

Contents

  1. Control loop timer/limiter
  2. Obtaining current pitch/roll values from X-Plane
  3. Initializing the PID controllers
  4. Feeding the PID controllers within the control loop
  5. Controlling the aircraft with the new PID output
  6. Monitoring the control loops via debug prints

1 – Control loop timer/limiter

Since we will be targeting a 10 Hz update rate, we need to develop a method to ensure the loop does not run more frequent than once every 100 milliseconds. We do not want the loop running uninhibited, because that will result in variable loop execution times and we like to keep those things constant. It could potentially execute thousands of times per second, which is entirely unnecessary. Most control loop algorithms run in the 10-100 Hz range (10-100 times per second). For reference, my RC plane running ArduPlane on Pixhawk uses 50 Hz as a standard update frequency.

To accomplish this task, we need to set up some timing variables.

First of all, add an import statment for datetime and timedelta:

from datetime import datetime, timedelta

Next, define the timing variables:

update_interval = 0.100 # this value is in seconds. 1/0.100 = 10 which is the update interval in Hz

# start is set to the time the line of code is executed, which is essential when the program started
start = datetime.now()

# last_update needs to be set to start for the first execution of the loop to successfully run.
# alternatively, we could've set start to something in the past.
last_update = start

# last update needs to be defined as a global within the monitor() function:
def monitor():
	global last_update

That handles the variables. Now we need to limit the loop execution. To do so requires wrapping all of the loop code into a new if statement that evaluates the time and only executes if the current time is 100 milliseconds greater than the last loop execution:

# loop is the "while True:" statement
while True:
	# this if statement is evaluated with every loop execution
	if (datetime.now() > last_update + timedelta(milliseconds = update_interval * 1000)):
		# when the if statement evaluates to true, the first thing we'll do is set the last update to the current time so the next iteration fires at the correct time
		last_update = datetime.now()

		# rest of the loop code goes here

2 – Obtaining current roll/pitch values from X-Plane

This task is pretty straightforward. In the first post, the monitorExample.py code included obtaining the position with posi = client.getPOSI(). There are 7 elements returned with that method, and two of them are roll and pitch.

Put the below after the .getPOSI() call.

current_roll = posi[4]
current_pitch = posi[3]

3 – Initializing the PID controllers

First you need to get the PID control file from the Ivmech (Ivmech Mechatronics Ltd.) GitHub page. Direct link PID.py file here. Put the file in the same working directory as everything else then import it with import PID at the top with the rest of the imports.

Then we can initialize the control instances. PID controllers are magic. But they do need to be set with smart values for the initial run. The default values with the PID library are P=0.2, I=0, D=0. This essentially means make the output be 20% of the error between the setpoint and the current value. For example, if the aircraft has a roll of 10 degrees to the left (-10), and the P=0.2, the output from the PID controller will be -2.

When setting PID values, it is almost always a good idea to start small and work your way up. If your gains are too high, you could get giant oscillations and other undesirable behaviors. In the YouTube video, I talk about the various types of PID controllers. They will basically always have a P (proportional) set. Many will also have an I (integral) value set, but not many use the D term (derivative).

Going with the “less is more” truism with PID controllers, I started with a P value of 0.1, and an I value of 0.01 (P/10). The I term (integral) is meant to take care of accumulated errors (which are usually long term errors). An example is your car’s cruise control going up a hill. If your cruise control is set to 65 mph, it will hold 65 no problem on flat roads. If you start going up a hill, the controller will slowly apply more throttle. With an I of 0, your car would never get to 65. It would probably stay around 62 or so. With the integrator going, the error will accumulate and boost the output (throttle) to get you back up to your desired 65. In the linked YouTube video, I show what happens when the I value is set to 0 and why it is necessary to correct long-term errors.

These values (P=0.1, I=0.01, D=0) turned out to work perfectly.

Place the following before the monitor function:

# defining the initial PID values
P = 0.1 # PID library default = 0.2
I = P/10 # default = 0
D = 0 # default = 0

# initializing both PID controllers
roll_PID = PID.PID(P, I, D)
pitch_PID = PID.PID(P, I, D)

# setting the desired values
# roll = 0 means wings level
# pitch = 2 means slightly nose up, which is required for level flight
desired_roll = 0
desired_pitch = 2

# setting the PID set points with our desired values
roll_PID.SetPoint = desired_roll
pitch_PID.SetPoint = desired_pitch

4 – Updating the PID controllers within the control loop

With the PIDs created, they will need to be updated with the new, current pitch and roll values with every loop execution.

Place the following after current_roll and current_pitch are assigned:

roll_PID.update(current_roll)
pitch_PID.update(current_pitch)

5 – Controlling the aircraft with the new PID output

Updating the PID controller instances will generate new outputs. We can use those outputs to set the control surfaces. You can place these two lines directly below the update lines:

new_ail_ctrl = roll_PID.output
new_ele_ctrl = pitch_PID.output

So we have new control surface values – now we need to actually move the control surfaces. This is accomplished by sending an array of 4 floats with .sendCTRL():

# ele, ail, rud, thr. -998 means don't set/change
ctrl = [new_ele_ctrl, new_ail_ctrl, 0.0, -998]
client.sendCTRL(ctrl)

6 – Monitoring the control loops via debug prints

The last bit to tie a bunch of this together is printing out values with every loop execution to ensure things are heading the right direction. We will turn these into graphs in the next post or two.

We will be concatenating strings because it’s easy and we aren’t working with enough strings for it to be a performance problem.

In newer Python versions (3.6+), placing ‘f’ before the quotes in a print statement (f””) means the string is interpolated. This means you can basically put code in the print statement, which makes creating print statements much easier and cleaner.

The first line will print out the current roll and pitch value (below). We are using current_roll and current_pitch interpolated. The colon, then blank space with 0.3f is a string formatter. It rounds the value to 3 decimal places and leaves space for a negative. It results in things being lined up quite nicely.

output = f"current values --    roll: {current_roll: 0.3f},  pitch: {current_pitch: 0.3f}"

The next code statement will add a new line to the previous output, and also add the PID outputs for reference:

output = output + "\n" + f"PID outputs    --    roll: {roll_PID.output: 0.3f},  pitch: {pitch_PID.output: 0.3f}"

The final line will just add another new line to keep each loop execution’s print statements grouped together:

output = output + "\n"

Finally, we print the output with print(output), which will look like this:

loop start - 2021-10-19 14:24:26.208945
current values --    roll:  0.000,  pitch:  1.994
PID outputs    --    roll: -0.000,  pitch:  0.053

Full code of pitch_roll_autopilot.py

import sys
import xpc
import PID
from datetime import datetime, timedelta

update_interval = 0.100 # seconds
start = datetime.now()
last_update = start

# defining the initial PID values
P = 0.1 # PID library default = 0.2
I = P/10 # default = 0
D = 0 # default = 0

# initializing both PID controllers
roll_PID = PID.PID(P, I, D)
pitch_PID = PID.PID(P, I, D)

# setting the desired values
# roll = 0 means wings level
# pitch = 2 means slightly nose up, which is required for level flight
desired_roll = 0
desired_pitch = 2

# setting the PID set points with our desired values
roll_PID.SetPoint = desired_roll
pitch_PID.SetPoint = desired_pitch

def monitor():
	global last_update
	with xpc.XPlaneConnect() as client:
		while True:
			if (datetime.now() > last_update + timedelta(milliseconds = update_interval * 1000)):
				last_update = datetime.now()
				print(f"loop start - {datetime.now()}")

				posi = client.getPOSI();
				ctrl = client.getCTRL();

				current_roll = posi[4]
				current_pitch = posi[3]

				roll_PID.update(current_roll)
				pitch_PID.update(current_pitch)

				new_ail_ctrl = roll_PID.output
				new_ele_ctrl = pitch_PID.output

				ctrl = [new_ele_ctrl, new_ail_ctrl, 0.0, -998] # ele, ail, rud, thr. -998 means don't change
				client.sendCTRL(ctrl)

				output = f"current values --    roll: {current_roll: 0.3f},  pitch: {current_pitch: 0.3f}"
				output = output + "\n" + f"PID outputs    --    roll: {roll_PID.output: 0.3f},  pitch: {pitch_PID.output: 0.3f}"
				output = output + "\n"
				print(output)

if __name__ == "__main__":
	monitor()

Using the autopilot / Conclusion

To use the autopilot, fire up XPlane, hop in a small-ish plane (gross weight less than 10k lb), take off, climb 1000′, then execute the code. Your plane should level off within a second or two in both axis.

Here is a screenshot of the output after running for a few seconds:

Screenshot showing current pitch and roll values along with their respective PID outputs

The linked YouTube video shows the aircraft snapping to the desired pitch and roll angles very quickly from a diving turn. The plane is righted to within 0.5 degrees of the setpoints within 2 seconds.

A note about directly setting the control surfaces

If you are familiar with PIDs and/or other parts of what I’ve discussed here, you’ll realize that we could be setting large values for the control surfaces (i.e. greater than 1 or less than -1). We will address that next post with a normalization function. It will quickly become a problem when a pitch of 100+ is commanded for altitude hold. I have found that XPlane will allow throttle values of more than 100% (I’ve seen as high as ~430%) if sent huge throttle values.

Categories
Python XPlane

Creating an autopilot in X-Plane using Python – part 1

Introduction

Today’s post will take us in a slightly different direction than the last few. Today’s post will be about hooking up some Python code to the X-Plane flight simulator to enable development of an autopilot using PID (proportional-integral-derivative) controllers. I’ve been a fan of flight simulators for quite some time (I distinctly remember getting Microsoft Flight Simulator 98 for my birthday when I was like 8 or 9) but have only recently started working with interfacing them to code. X-Plane is a well-known flight simulator developed by another Austin – Austin Meyer. It is regarded as having one of the best flight models and has tons of options for getting data into/out of the simulator. More than one FAA-certified simulator setups are running X-Plane as the primary driver software.

I got started thinking about writing some code for X-Plane while playing another game, Factorio. I drive a little plane or car in the game to get around my base and I just added a plug-in that “snaps” the vehicle to a heading, which makes it easier to go in straight lines. I thought – “hmm how hard could this be to duplicate in a flight sim?”. So here we are.

This post will get X-Plane hooked up to Python. The real programming will start with the next post.

Video Link

Contents

  1. Download and install X-Plane (I used X-Plane 10 because it uses less resources than X-Plane 11 and we don’t need the graphics/scenery to look super pretty to do coding. It also loads faster.)
  2. Download and install NASA’s XPlaneConnect X-Plane plug-in to X-Plane
  3. Verify the XPlaneConnect plug-in is active in X-Plane
  4. Download sample code from XPlaneConnect’s GitHub page
  5. Run the sample script to verify data is being transmitted from X-Plane via UDP to the XPlaneConnect code

1 – Download and install X-Plane 10 or X-Plane 11

I’ll leave this one up to you. X-Plane 10 is hard to find these days I just discovered. X-Plane 11 is available on Steam for $59.99 as of writing. I just tested and the plug-in/code works fine on X-Plane 11 (but the flight models are definitely different and will need different PID values). My screenshots might jump between the two versions but the content/message will be the same.

2 – Download and install NASA’s XPlaneConnect plug-in

NASA (yes, that NASA, the National Aeronautics and Space Administration) has wrote a bunch of code to interface with X-Plane. They have adapters for C, C++, Java, Matlab, and Python. They work with X-Plane 9, 10, and 11.

  1. Download the latest version from the XPlaneConnect GitHub releases page, 1.3 RC6 as of writing
  2. Open the .zip and place the contents in the [X-Plane directory]/Resources/plugins folder. There are few other folders already present in this directory. Mine looked like this after adding the XPlaneConnect folder:
Screenshot of X-Plane 10 plugins directory with XPlaneConnect folder added
Screenshot of X-Plane 11 plugins directory with XPlaneConnect folder added

3 – Verify XPlaneConnect is active in X-Plane

Now we’ll load up X-Plane and check the plug-ins to verify XPlaneConnect is enabled. Go to the top menu and select Plugins -> Plugin Admin. You should see X-Plane Connect checked in the enabled column:

Screenshot showing XPlaneConnect plug-in active in X-Plane 11
Screenshot showing XPlaneConnect plug-in active in X-Plane 10

4 – Download sample code from XPlaneConnect’s GitHub page

From the Python3 portion of the GitHub code, download xpc.py and monitorExample.py and stick them in your working directory (doesn’t matter where). For me, I just downloaded the entire git structure so the code is at C:\Users\Austin\source\repos\XPlaneConnect\Python3\src:

Screenshot showing xpc.py and monitorExample.py in my working directory

5 – Run sample code to verify data is making it from X-Plane to our code

With X-Plane running with a plane on a runway (or anywhere really), go ahead and run monitorExample.py! I will be using Visual Studio Code to program this XPlane Python autopilot stuff so that’s where I’ll run it from.

You will start seeing lines scroll by very fast with 6 pieces of information – latitude, longitude, elevation (in meters), and the control deflections for aileron, elevator, and rudder (normalized from -1 to 1, with 0 being centered). In the below screenshot, we see a lat/lon of 39.915, -105.128, with an elevation of 1719m. First one to tell me in the comments what runway that is wins internet points!

Screenshot showing Visual Studio Code running monitorExample.py in front of X-Plane 10 and the output scrolling by.

Conclusion

In this post, we have successfully downloaded the XPlaneConnect plug-in, and demonstrated that it can successfully interface with Python code in both X-Plane 10 and X-Plane 11.

Next up is to start controlling the plane with a basic wing leveler. As of writing this post, I have the following completely functional:

  • Pitch / roll hold at reasonable angles (-25 to 25)
  • Altitude set and hold
  • Heading set and hold
  • Airspeed set and hold
  • Navigate directly to a lat/lon point

See you at the next post! Next post – Coding a wing leveler autopilot in X-Plane with Python

Categories
NTP Raspberry Pi

How to update GPSd by building from source

Introduction

I was recently made aware of a bug in GPSd that will result in the time/date jumping backwards 1024 weeks, from October 16, 2021 to Sunday March 3, 2002 for versions 3.20, 3.21, and 3.22. GPSd version 3.22 is currently scheduled to ship with Debian Bullseye, which will be a problem. I use GPSd for my timekeeping interfaces between the source GPS and NTP/Chrony. GPSd version 3.17 is present in the current Raspberry Pi OS (Raspbian) images (based off Debian 9 – Stretch) as well.

Fortunately, it isn’t hard to update to your desired version!

Updating GPSd

The overview for updating GPSd is as follows:

  • Download the desired release with wget (look for >3.23)
  • Uncompress the archive
  • Use scons to build the new binaries
  • Use scons to install the new binaries

So with that out of the way, let’s get started. (The full install script is at the bottom if you just want to jump ahead to that).

You must first ensure you have the required packages to actually build GPSd from source:

sudo apt update
sudo apt install -y scons libncurses-dev python-dev pps-tools git-core asciidoctor python3-matplotlib build-essential manpages-dev pkg-config

Next, we will download the desired version of GPSd. In this case, we will be updating GPSd to version 3.23.1. A full list of the releases can be found here.

wget http://download.savannah.gnu.org/releases/gpsd/gpsd-3.23.1.tar.gz

Extract the files from the .tar.gz archive, and change to the created folder:

tar -xzf gpsd-3.23.1.tar.gz
cd gpsd-3.23.1/

Now we can build the binaries, which will take a few minutes to run:

sudo scons

# some sources say to do a config=force for scons, I found this wasn't necessary
# if you want to use this force argument, below is the required command
# sudo scons --config=force

Last up is to actually install the binaries:

sudo scons install

And with that, you should now have an updated GPSd running version 3.23.1! I rebooted for good measure with sudo reboot.

If you’re interested in a full script to do all this, check this out:

sudo apt update
sudo apt install -y scons libncurses-dev python-dev pps-tools git-core asciidoctor python3-matplotlib build-essential manpages-dev pkg-config
wget http://download.savannah.gnu.org/releases/gpsd/gpsd-3.23.1.tar.gz
tar -xzf gpsd-3.23.1.tar.gz
cd gpsd-3.23.1
sudo scons
sudo scons install
gpsd -V

Verifying you have the update for GPSd

gpsd -V
GPSd version 3.23.1 verified with the command ‘gpsd -V’

References/Sources

Categories
Linux NTP Raspberry Pi

Millisecond accurate Chrony NTP with a USB GPS for $12 USD

Introduction

Building off my last NTP post (Microsecond accurate NTP with a Raspberry Pi and PPS GPS), which required a $50-60 GPS device and a Raspberry Pi (also $40+), I have successfully tested something much cheaper, that is good enough, especially for initial PPS synchronization. Good enough, in this case, is defined as +/- 10 milliseconds.

YouTube Video Link

https://www.youtube.com/watch?v=DVtmDFpWkEs

Microsecond PPS time vs millisecond USB time

How accurate of time do you really need? The last post showed how to get all devices on a local area network (LAN) within 0.1 milliseconds of “real” time. Do you need you equipment to be that accurate to official atomic clock time (12:03:05.0001)? Didn’t think so. Do you care if every device is on the correct second compared to official/accurate time (12:03:05)? That’s a lot more reasonable. Using a u-blox USB GPS can get you to 0.01 seconds of official. The best part about this? The required USB GPS units are almost always less than $15 and you don’t need a Raspberry Pi.

Overview

This post will show how to add a u-blox USB GPS module to NTP as a driver or chrony (timekeeping daemon) as a reference clock (using GPSd shared memory for both) and verify the accuracy is within +/- 10 milliseconds.

Materials needed

  • USB u-blox GPS (VK-172 or GT-U7), GT-U7 preferred because it has a micro-USB plug to connect to your computer. It is important to note that both of these are u-blox modules, which has a binary data format as well as a high default baudrate (57600). These two properties allow for quick transmission of each GPS message from GPS to computer.
  • 15-30 minutes

Steps

1 – Update your host machine and install packages

This tutorial is for Linux. I use Ubuntu so we utilize Aptitude (apt) for package management:

sudo apt update
sudo apt upgrade
sudo rpi-update
sudo apt install gpsd gpsd-clients python-gps chrony

2 – Modify GPSd default startup settings

In /etc/default/gpsd, change the settings to the following:

# Start the gpsd daemon automatically at boot time
START_DAEMON="true"

# Use USB hotplugging to add new USB devices automatically to the daemon
USBAUTO="true"

# Devices gpsd should collect to at boot time.
# this could also be /dev/ttyUSB0, it is ACM0 on raspberry pi
DEVICES="/dev/ttyACM0"

# -n means start listening to GPS data without a specific listener
GPSD_OPTIONS="-n"

Reboot with sudo reboot.

3a – Chrony configuration (if using NTPd, go to 3b)

I took the default configuration, added my 10.98 servers, and more importantly, added a reference clock (refclock). Link to chrony documentation here. Arguments/parameters of this configuration file:

  • 10.98.1.198 is my microsecond accurate PPS NTP server
  • iburst means send a bunch of synchronization packets upon service start so accurate time can be determined much faster (usually a couple seconds)
  • maxpoll (and minpoll, which isn’t used in this config) is how many seconds to wait between polls, defined by 2^x where x is the number in the config. maxpoll 6 means don’t wait more than 2^6=64 seconds between polls
  • refclock is reference clock, and is the USB GPS source we are adding
    • ‘SHM 0’ means shared memory reference 0, which means it is checking with GPSd using shared memory to see what time the GPS is reporting
    • ‘refid NMEA’ means name this reference ‘NMEA’
    • ‘offset 0.000’ means don’t offset this clock source at all. We will change this later
    • ‘precision 1e-3’ means this reference is only accurate to 1e-3 (0.001) seconds, or 1 millisecond
    • ‘poll 3’ means poll this reference every 2^3 = 8 seconds
    • ‘noselect’ means don’t actually use this clock as a source. We will be measuring the delta to other known times to set the offset and make the source selectable.
[email protected]:~ $ sudo cat /etc/chrony/chrony.conf
# Welcome to the chrony configuration file. See chrony.conf(5) for more
# information about usuable directives.
#pool 2.debian.pool.ntp.org iburst
server 10.98.1.1 iburst maxpoll 6
server 10.98.1.198 iburst maxpoll 6
server 10.98.1.15 iburst

refclock SHM 0 refid NMEA offset 0.000 precision 1e-3 poll 3 noselect

Restart chrony with sudo systemctl restart chrony.

3b – NTP config

Similar to the chrony config, we need to add a reference clock (called a driver in NTP). For NTP, drivers are “servers” that start with an address of 127.127. The next two octets tell what kind of driver it is. The .28 driver is the shared memory driver, same theory as for chrony. For a full list of drivers, see the official NTP docs. To break down the server:

  • ‘server 127.127.28.0’ means use the .28 (SHM) driver
    • minpoll 4 maxpoll 4 means poll every 2^4=16 seconds
    • noselect means don’t use this for time. Similar to chrony, we will be measuring the offset to determine this value.
  • ‘fudge 127.127.28.0’ means we are going to change some properties of the listed driver
    • ‘time1 0.000’is the time offset calibration factor, in seconds
    • ‘stratum 2’ means list this source as a stratum 2 source (has to do with how close the source is to “true” time), listing it as 2 means other, higher stratum sources will be selected before this one will (assuming equal time quality)
    • ‘refid GPS’ means rename this source as ‘GPS’
server 127.127.28.0 minpoll 4 maxpoll 4 noselect
fudge 127.127.28.0 time1 0.000 stratum 2 refid GPS

Restart NTPd with sudo systemctl restart ntp.

4 – check time offset via gpsmon

Running gpsmon shows us generally information about the GPS, including time offset. The output looks like the below screenshot. Of importance is the satellite count (on right, more is better, >5 is good enough for time), HDOP (horizontal dilution of precision) is a measure of how well the satellites can determine your position (lower is better, <2 works for basically all navigation purposes), and TOFF (time offset).

gpsmon showing time offset for a USB GPS

In this screenshot the TOFF is 0.081862027, which is 81.8 milliseconds off the host computer’s time. Watch this for a bit – it should hover pretty close to a certain value +/- 10ms. In my case, I’ve noticed that if there are 10 or less satellites locked on, it is around 77ms. If there are 11 or more, it is around 91ms (presumably due to more satellite information that needs to be transmitted).

5 – record statistics for a data-driven offset

If you are looking for a better offset value to put in the configuration file, we can turn on logging from either chrony or NTPd to record source information.

For chrony:

Edit /etc/chrony/chrony.conf and uncomment the line for which kinds of logging to turn on:

# Uncomment the following line to turn logging on.
log tracking measurements statistics

Then restart chrony (sudo systemctl restart chrony) and logs will start writing to /var/log/chrony (this location is defined a couple lines below the log line in chrony.conf):

[email protected]:~ $ ls /var/log/chrony
measurements.log  statistics.log  tracking.log

For NTPd (be sure to restart it after making any configuration changes):

[email protected] ~ % cat /etc/ntp.conf
# Enable this if you want statistics to be logged.
statsdir /var/log/ntpstats/

statistics loopstats peerstats clockstats
filegen loopstats file loopstats type day enable
filegen peerstats file peerstats type day enable
filegen clockstats file clockstats type day enable

Wait a few minutes for some data to record (chrony synchronizes pretty quick compared to NTPd) and check the statistics file, filtered to our NMEA refid:

cat /var/log/chrony/statistics.log | grep NMEA

This spits out the lines that have NMEA present (the ones of interest for our USB GPS). To include the headers to show what each column is we can run

# chrony
cat /var/log/chrony/statistics.log | head -2; cat /var/log/chrony/statistics.log | grep NMEA

# ntp, there is no header info so we can omit that part of the command
cat /var/log/peerstats | grep 127.127.28.0
Screenshot showing chrony statistics for our NMEA USB GPS refclock

NTP stats don’t include header information. The column of interest is the one after the 9014 column. The columns are day, seconds past midnight, source, something, estimated offset, something, something, something. We can see the offset for this VK-172 USB GPS is somewhere around 76-77 milliseconds (0.076-0.077 seconds), which we can put in place of the 0.000 for the .28 driver for NTP and remove noselect.

[email protected] ~ % cat /var/log/ntpstats/peerstats | grep 127.127.28.0
59487 49648.536 127.127.28.0 9014 -0.078425007 0.000000000 7.938064614 0.000000060
59487 49664.536 127.127.28.0 9014 -0.079488544 0.000000000 3.938033388 0.001063537
59487 49680.536 127.127.28.0 9014 -0.079514781 0.000000000 1.938035682 0.000770810
59487 49696.536 127.127.28.0 9014 -0.079772284 0.000000000 0.938092429 0.000808697
59487 49712.536 127.127.28.0 9014 -0.079711708 0.000000000 0.438080791 0.000661032
59487 49728.536 127.127.28.0 9014 -0.075098563 0.000000000 0.188028843 0.004311958

So now we have some data showing the statistics of our NMEA USB GPS NTP source. We can copy and paste this into Excel, run data to columns, and graph the result and/or get the average to set the offset.

screenshot showing chrony/NTP statistics to determine offset

This graph is certainly suspicious (sine wave pattern and such) and if I wasn’t writing this blog post, I’d let data collect overnight to determine an offset. Since time is always of the essence, I will just take the average of the ‘est offset’ column (E), which is 7.64E-2, or 0.0763 seconds. Let’s pop this into the chrony.conf file and remove noselect:

refclock SHM 0 refid NMEA offset 0.0763 precision 1e-3 poll 3

For NTP:

Restart chrony again for the config file changes to take effect – sudo systemctl restart chrony.

6 – watch ‘chrony sources’ or ‘ntpq -pn’ to see if the USB GPS gets selected as the main time source

If you aren’t aware, Ubuntu/Debian/most Linux includes a utility to rerun a command every x seconds called watch. We can use this to watch chrony to see how it is interpreting each time source every 1 second:

# for chrony
watch -n 1 chronyc sources
watching chrony sources

In the above screenshot, we can see that chrony actually has the NMEA source selected as the primary source (denoted with the *). It has the Raspberry Pi PPS NTP GPS ready to takeover as the new primary (denoted with the +). All of the sources match quite closely (from +4749us to – 505us is around 5.2 milliseconds). The source “offset” is in the square brackets ([ and ]).

# for ntp
watch -n 1 ntpq -pn
NTP showing (via ntpq -pn) that the GPS source is 0.738 milliseconds off of the host clock. The ‘-‘ in front of the remote means this will not be selected as a valid time (presumably due to the high jitter compared to the other sources, probably also due to manually setting it to stratum 2).

7- is +/- five millseconds good enough?

For 99% of use cases, yes. You can stop here and your home network will be plenty accurate. If you want additional accuracy, you are in luck. This GPS module also outputs a PPS (pulse per second) signal! We can use this to get within 0.05 millseconds (0.00005 seconds) from official/atomic clock time.

Conclusion

In this post, we got a u-blox USB GPS set up and added it as a reference clock (refclock) to chrony and demonstrated it is clearly within 10 millisecond of official GPS time.

You could write a script to do all this for you! I should probably try this myself…

In the next post, we can add PPS signals from the GPS module to increase our time accuracy by 1000x (into the microsecond range).

A note on why having faster message transmission is better for timing

My current PPS NTP server uses chrony with NMEA messages transmitted over serial and the PPS signal fed into a GPIO pin. GPSd as a rule does minimum configuration of GPS devices. It typically defaults to 9600 baud for serial devices. A typical GPS message looks like this:

$GPGGA, 161229.487, 3723.2475, N, 12158.3416, W, 1, 07, 1.0, 9.0, M, , , , 0000*18

That message is 83 bytes long. At 9600 baud (9600 bits per second), that message takes 69.1 milliseconds to transmit. Each character/byte takes 0.833 milliseconds to transmit. That means that as the message length varies, the jitter will increase. GPS messages do vary in length, sometimes significantly, depending on what is being sent (i.e. the satellite information, $GPGSV sentences, is only transmitted every 5-10 seconds).

I opened gpsmon to get a sample of sentences – I did not notice this until now but it shows how many bytes each sentence is at the front of the sentence:

(35) $GPZDA,144410.000,30,09,2021,,*59
------------------- PPS offset: -0.000001297 ------
(83) $GPGGA,144411.000,3953.xxxx,N,10504.xxxx,W,2,6,1.19,1637.8,M,-20.9,M,0000,0000*5A
(54) $GPGSA,A,3,26,25,29,18,05,02,,,,,,,1.46,1.19,0.84*02
(71) $GPRMC,144411.000,A,3953.xxxx,N,10504.xxxx,W,2.80,39.98,300921,,,D*44
(35) $GPZDA,144411.000,30,09,2021,,*58
------------------- PPS offset: -0.000000883 ------
(83) $GPGGA,144412.000,3953.xxxx,N,10504.xxxx,W,2,7,1.11,1637.7,M,-20.9,M,0000,0000*52
(56) $GPGSA,A,3,20,26,25,29,18,05,02,,,,,,1.39,1.11,0.84*00
(70) $GPGSV,3,1,12,29,81,325,27,05,68,056,21,20,35,050,17,18,34,283,24*76
(66) $GPGSV,3,2,12,25,27,210,14,15,27,153,,13,25,117,,02,23,080,19*78
(59) $GPGSV,3,3,12,26,17,311,22,23,16,222,,12,11,184,,47,,,*42
------------------- PPS offset: -0.000000833 ------
(71) $GPRMC,144412.000,A,3953.xxxx,N,10504.xxxx,W,2.57,38.19,300921,,,D*48
(35) $GPZDA,144412.000,30,09,2021,,*5B
(83) $GPGGA,144413.000,3953.xxxx,N,10504.xxxx,W,2,7,1.11,1637.6,M,-20.9,M,0000,0000*52
(56) $GPGSA,A,3,20,26,25,29,18,05,02,,,,,,1.39,1.11,0.84*00
(71) $GPRMC,144413.000,A,3953.xxxx,N,10504.xxxx,W,2.60,36.39,300921,,,D*41
(35) $GPZDA,144413.000,30,09,2021,,*5A

These sentences range from 83 bytes to 35 bytes, a variation of (83 bytes -35 bytes)*0.833 milliseconds per byte = 39.984 milliseconds.

Compare to the u-blox binary UBX messages which seem to always be 60 bytes and transmitted at 57600 baud, which is 8.33 milliseconds to transmit the entire message.

UBX protocol messages (blanked out lines). I have no idea what part of the message is location, hopefully I got the right part blanked out.

The variance (jitter) is thus much lower and can be much more accurate as a NTP source. GPSd has no problem leaving u-blox modules at 57600 baud. This is why the USB GPS modules perform much more accurate for timekeeping than NMEA-based devices when using GPSd.

For basically every GPS module/chipset, it is possible to send it commands to enable/disable sentences (as well as increase the serial baud rate). In an ideal world for timekeeping, GPSd would disable every sentence except for time ($GPZDA), and bump up the baud rate to the highest supported level (115200, 230400, etc.). Unfortunately for us, GPSd’s default behavior is to just work with every GPS, which essentially means no configuring the GPS device.

Categories
homelab Kubernetes Linux proxmox Terraform

Deploying Kubernetes VMs in Proxmox with Terraform

Background

The last post covered how to deploy virtual machines in Proxmox with Terraform. This post shows the template for deploying 4 Kubernetes virtual machines in Proxmox using Terraform.

Youtube Video Link

Coming soon

Kubernetes Proxmox Terraform Template

Without further ado, below is the template I used to create my virtual machines. The main LAN network is 10.98.1.0/24, and the Kube internal network (on its own bridge) is 10.17.0.0/24.

This template creates a Kube server, two agents, and a storage server.

terraform {
  required_providers {
    proxmox = {
      source = "telmate/proxmox"
      version = "2.7.4"
    }
  }
}

provider "proxmox" {
  pm_api_url = "https://prox-1u.home.fluffnet.net:8006/api2/json"
  pm_api_token_id = [secret]
  pm_api_token_secret = [secret]
  pm_tls_insecure = true
}

resource "proxmox_vm_qemu" "kube-server" {
  count = 1
  name = "kube-server-0${count.index + 1}"
  target_node = "prox-1u"

  clone = "ubuntu-2004-cloudinit-template"

  agent = 1
  os_type = "cloud-init"
  cores = 2
  sockets = 1
  cpu = "host"
  memory = 4096
  scsihw = "virtio-scsi-pci"
  bootdisk = "scsi0"

  disk {
    slot = 0
    size = "10G"
    type = "scsi"
    storage = "local-zfs"
    #storage_type = "zfspool"
    iothread = 1
  }

  network {
    model = "virtio"
    bridge = "vmbr0"
  }
  
  network {
    model = "virtio"
    bridge = "vmbr17"
  }

  lifecycle {
    ignore_changes = [
      network,
    ]
  }

  ipconfig0 = "ip=10.98.1.4${count.index + 1}/24,gw=10.98.1.1"
  ipconfig1 = "ip=10.17.0.4${count.index + 1}/24"
  sshkeys = <<EOF
  ${var.ssh_key}
  EOF
}

resource "proxmox_vm_qemu" "kube-agent" {
  count = 2
  name = "kube-agent-0${count.index + 1}"
  target_node = "prox-1u"

  clone = "ubuntu-2004-cloudinit-template"

  agent = 1
  os_type = "cloud-init"
  cores = 2
  sockets = 1
  cpu = "host"
  memory = 4096
  scsihw = "virtio-scsi-pci"
  bootdisk = "scsi0"

  disk {
    slot = 0
    size = "10G"
    type = "scsi"
    storage = "local-zfs"
    #storage_type = "zfspool"
    iothread = 1
  }

  network {
    model = "virtio"
    bridge = "vmbr0"
  }
  
  network {
    model = "virtio"
    bridge = "vmbr17"
  }

  lifecycle {
    ignore_changes = [
      network,
    ]
  }

  ipconfig0 = "ip=10.98.1.5${count.index + 1}/24,gw=10.98.1.1"
  ipconfig1 = "ip=10.17.0.5${count.index + 1}/24"
  sshkeys = <<EOF
  ${var.ssh_key}
  EOF
}

resource "proxmox_vm_qemu" "kube-storage" {
  count = 1
  name = "kube-storage-0${count.index + 1}"
  target_node = "prox-1u"

  clone = "ubuntu-2004-cloudinit-template"

  agent = 1
  os_type = "cloud-init"
  cores = 2
  sockets = 1
  cpu = "host"
  memory = 4096
  scsihw = "virtio-scsi-pci"
  bootdisk = "scsi0"

  disk {
    slot = 0
    size = "20G"
    type = "scsi"
    storage = "local-zfs"
    #storage_type = "zfspool"
    iothread = 1
  }

  network {
    model = "virtio"
    bridge = "vmbr0"
  }
  
  network {
    model = "virtio"
    bridge = "vmbr17"
  }

  lifecycle {
    ignore_changes = [
      network,
    ]
  }

  ipconfig0 = "ip=10.98.1.6${count.index + 1}/24,gw=10.98.1.1"
  ipconfig1 = "ip=10.17.0.6${count.index + 1}/24"
  sshkeys = <<EOF
  ${var.ssh_key}
  EOF
}

After running Terraform plan and apply, you should have 4 new VMs in your Proxmox cluster:

Proxmox showing 4 virtual machines ready for Kubernetes

Conclusion

You now have 4 VMs ready for Kubernetes installation. The next post will show how to install Kubernetes with Ansible.

Categories
Announcements SDR

RTL-SDR Giveaway Active!

I am giving away a NooElec RTL-SDR v4 bundle to get someone started in the hobby/addiction! Enter now through Sept 19, 2021. Link here – SDR Forums – RTL-SDR Giveaway!

Categories
Blog Admin Linux

UFW – add IPv6 rule to top of chain

Brief Introduction

This WordPress blog is decently protected from bots/hackers (read more at Securing this WordPress blog from evil hackers!) but I still get a ton of attempts on the site. Wordfence can block requests at the application layer but as I grow in traffic, I want to make sure CPU cycles aren’t wasted. Thus, I want to block some of these bots/hackers from even connecting to the server. I use Ubuntu, and UFW (Uncomplicated FireWall) is included. It’s pretty simple so I’ve stuck with it.

Blocking IPv4 is easy:

sudo ufw insert 1 deny from 1.2.3.4 comment "repeated unwanted hits on sdrforums.com"

The command broken down:

  • sudo – run as root since firewall modification requires root access
  • ufw – run the uncomplicated firewall program
  • insert – add a rule
  • 1 – insert at the top of the rule list (firewalls evaluate rules from the top down – putting a deny after an allow would mean the traffic wouldn’t be blocked)
  • deny – deny the request
  • from – from the following IP
  • 1.2.3.4 – IP address
  • comment – so you can leave a comment to remind yourself why the rule is in place (“unwanted hits on site.com”, “change request #123456”, “incident remedy #44444”, etc.)

Blocking IPv6 with UFW

So I tried the same command format with an IPv6 address and got an error message – “ERROR: Invalid position ‘1’”. I’ve never got that message before. Also, I do realize I need to widen this IPv6 subnet and block a much larger range of IPs but that’s a topic for a different day.

sudo ufw insert 1 deny from 2400:adc5:11f:5600:e468:9464:e881:b1c0 comment "repeated unwanted hits on sdrforums.com"
ERROR: Invalid position '1'
ufw ERROR: Invalid position '1' screenshot
ufw ERROR: Invalid position ‘1’ screenshot

My rule list at the time looked like this:

[email protected]:~$ sudo ufw status numbered
Status: active

     To                         Action      From
     --                         ------      ----
[ 1] Anywhere                   DENY IN     51.195.216.255             # repeated unwanted hits on sdrforums.com
[ 2] Anywhere                   DENY IN     51.195.90.229              # repeated unwanted hits on sdrforums.com
[ 3] Anywhere                   DENY IN     206.217.139.28             # excessive hits to wp-login and xmlrpc
[ 4] 22/tcp                     ALLOW IN    Anywhere
[ 5] 80/tcp                     ALLOW IN    Anywhere
[ 6] 443/tcp                    ALLOW IN    Anywhere
[ 7] 22/tcp (v6)                ALLOW IN    Anywhere (v6)
[ 8] 80/tcp (v6)                ALLOW IN    Anywhere (v6)
[ 9] 443/tcp (v6)               ALLOW IN    Anywhere (v6)

Pretty easy to understand what’s going on here. I have a few IPv4 addresses blocked, but nothing specific to IPv6. A bit of searching later and I learned that the first IPv6 rule needs to come after the last IPv4 rule. So in this case I needed to add the rule to position #7, since that is where the first IPv6 rule current is located:

[email protected]:~$ sudo ufw insert 7 deny from 2400:adc5:11f:5600:e468:9464:e881:b1c0 comment "repeated unwanted hits on
 sdrforums.com"
Rule inserted (v6)

And it worked!

My UFW status is now this:

numbered UFW status list
numbered UFW status list

References

The post that started me in the right direction is here – https://joshtronic.com/2015/09/06/error-invalid-position-1/. Thank you Josh for posting about this! Stackoverflow wasn’t actually helpful for once.

Categories
ADS-B SDR Tutorials

Getting Started With SDR (software defined radio): Tutorial

Introduction – what is SDR?

SDR stands for software defined radio. It is a term used to describe devices that can receive radio frequency signals over a wide range of frequencies. These devices most commonly interface with USB drives of computers / Raspberry Pi / similar. There are standalone-devices but they often cost quite a bit more than the USB devices. The best part about the base-level SDRs? They’re less than $50. I have a couple that I paid $18 for.

So you’re here on the “Getting Started with SDR” post. In this SDR tutorial post we’ll get a SDR plugged into your Windows computer, install drivers, and start sniffing for signals. We’ll try FM radio first since they have very powerful transmitters and there is almost certainly one you can receive at your hose, followed by checking for those wireless temperature sensors, and then some aircraft position signals.

What does a SDR look like? Below is a image with 3 I have around the house that I snagged for a picture (USB plug for size reference). Below the picture is the table of contents for the post.

picture showing 3 SDR devices - NooElec NESDR Nano 2, FlightAware ProStick, RTL-SDR Blog R820T2 RTL2832U V#
Example of a couple SDRs (all Realtek chipset-based, hence the RTL abbreviation)

SDR Tutorial Contents

  1. Purchasing a suitable SDR
  2. Plugging it into a Windows computer and installing drivers
  3. Installing SDR# (a program to monitor any and all frequencies)
  4. Listening to a FM radio station
  5. Installing rtl_433 and listening for wireless temperature sensors
  6. Installing dump1090 and listening for aircraft position signals

1 – Purchasing a suitable SDR

To get started, you’ll obviously need a RTL-SDR. I recommend the NooElec devices, specifically the NooElec NESDR Smart v4 bundle. NooElec devices have lasted the longest out of the 8 SDRs I’ve purchased and have been very useful for me. The link below shows the current price of the newest version (V4) and includes 3 different antenna (433 MHz for sensors, 1090 MHz for ADS-B aircraft signals, and an adjustable antenna) which are useful for the rest of the tutorial:

With a SDR in hand, let’s get started!

2 – Installing drivers and plugging in the RTL-SDR

I use Windows 10 for this SDR tutorial, but I use Linux for the long-term monitoring I have in place around the house. Linux doesn’t ever automatically reboot for updates, and is generally much more stable (and doesn’t need a license). Windows is easy to get started so we’ll use it.

The instructions for installation are a bit long so I’ll just leave a link to the official source where they will always have the most up to date drivers and such – https://www.rtl-sdr.com/rtl-sdr-quick-start-guide/. I will re-write these at some point (and also make a video) but I’ll just leave the link for now.

3 – SDRSharp installation

If you followed the instructions in the link above, you should have a file called SDRSharp.exe in the folder you used:

SDRSharp.exe present in working directory

This means it is already “installed” since it is just a simple executable file. No need to click next, next, next finish to install.

4 – Listening to a FM radio station with your RTL-SDR

Go ahead and double-click on SDRSharp.exe and launch it! First thing you need to do is select the RTL-SDR USB option from the sources drop down menu:

Selecting the RTL-SDR USB source in SDRSharp

Next up, we need to crank the gain to get useful signal out of the SDR. Click the gear icon, make sure the RTL2832U device is selected, then set the RF gain slider to around 40dB, then click close:

Setting the RTL-SDR gain to 40.2 dB

Now we are ready to listen to FM radio!

Ensure the WFM radio button is selected in the signal type, set the zoom slider all the way down, set the step to 100 kHz, then click the frequency numbers up/down to pick a known FM station. This should get everything ready to go:

Setting SDRSharp for FM radio station reception

With all that setup out of the way, click the play button and watch the signals start streaming in! Assuming your speakers are set to a decent volume, you’ll hear the radio from your computer! From my 2nd story bedroom near Broomfield, CO, I can easily get 98.5 MHz and 99.5 MHz (and many others). You can also check the “FM Stereo” checkbox on the left side if you know you’ll be receiving FM stereo. Make sure you uncheck it if you start looking at other things:

SDRSharp FM radio reception for 98.5 Mhz (KYGO) and 99.5 MHz (KQMT)

Here is a picture showing my setup for writing this blog – a simple NooElec Nano SDR I bought in 2016, a little bit of cable, and a 1090 MHz antenna (which is clearly not ideal for FM radio frequencies, but radio is so powerful it doesn’t really matter). Also say hi to Fluffy the cat:

RTL-SDR set up for writing this blog post, with an appearance from Fluffy the black cat

5 – Installing rtl_433 and listening for wireless temperature/humidity sensors

Ok so now that we know radio works, let’s see what other radio frequency signals are traveling through the air. We will start by downloading the rtl_433 Windows release from GitHub. The latest version as of this post is here (https://github.com/winterrace/rtl_433_win/releases/download/v19.8.19/rtl_433_win_2019-08-19.zip). Unzip the .zip file. There should be two files inside, rtl_433.exe and rtlsdr.dll. I put these files on my desktop in a folder called rtl_433:

rtl_433.exe in a folder called rtl_433 on my desktop

Now open a command window, and change directory (cd) to Desktop/rtl_433.

C:\Users\Austin>cd Desktop\rtl_433

C:\Users\Austin\Desktop\rtl_433>

Ok now we’re in the rtl_433 directory so we can run commands now. The most basic command for this program is to run it and only specify the gain. We used 40 dB for the FM radio so let’s use 40 again by specifying it with the -g option (you can view all commands by running rtl_433 -h):

rtl_433 -g 40

The output will show the following (ending in “Tuned to 433.92 MHz”) if all went well:

Let it run for a couple minutes. You might see some thermometers and other such devices! Here is what my output looks like after about 60 seconds:

If you look closely, you can see there are three separate devices broadcasting on 433 MHz that I can pick up with my (still not desirable) 1090 MHz antenna! One is sitting in my garage, another is next to me in the bedroom, and I don’t actually know where the 3rd is or who owns it.

With the same, tiny USB RTL-SDR, we have picked up FM radio as well as temperature/humidity readings from three separate sensors!

6 – Installing dump1090 and listening for aircraft position (ADS-B) signals

Last up for this RTL-SDR tutorial is installing dump1090 and checking to see if we can pick up any aircraft signals.

Dump1090 is a utility written many years ago that decodes aircraft ADS-B position signals. Some brave souls ported it for use in Windows a while ago. Using the directions and links from https://sonicgoose.com/using-dump1090-in-windows/ I have distilled it down for you. Download the dump1090 windows package that I’ve rehosted here –

And then extract it to your desktop in a folder called dump1090.

There is a .bat file (batch file) in the folder, double-click it to run it:

dump1090 folder in Windows showing dump1090.bat highlighted

You will be presented with a screen that updates in realtime as signals come in. You are now receiving ADS-B signals with the same device that could listen to FM radio, and also temperature/humidity sensors around the house!

Windows dump1090 showing ADS-B data streaming in from the RTL-SDR

All this from a 1090 MHz antenna (which is actually finally the right frequency for the application at hand). Fluffy is still supervising:

1090 MHz antenna attached to RTL-SDR to pick up aircraft ADS-B signals. Cat is helping (and has not moved in 3 hours).

Conclusion

In this SDR tutorial, we have purchased a RTL-SDR, installed the drivers, plugged it in, listened to FM radio, checked for wireless temperature/humidity sensors and found 3, and listened for aircraft ADS-B signals (and found 15 aircraft broadcasting in the last screenshot). This is an addictive hobby. With the right antenna, you can hear people speaking and morse code coding from across the world. In my next post I’ll show how to implement some of these into automated programs to take the data and input it into Home Assistant and other databases.

One last thing – receiving RF signals is 98% about the antenna and 2% about what you’re receiving the signals with! The base antennas that come with RTL-SDRs are good for the basics but if you really want to get into receiving interesting/distant signals, be prepared to spend 2-3x the cost of a SDR on a single antenna!

If you’re looking for more SDR discussion, head on over to SDRForums.com.

Hope you learned something and enjoyed this tutorial!