This is the 2nd video of the 3 part series demonstrating moving object tracking.

This video demonstrates 3 different use of Extended Kalman Filter to overcome noise in non-linear system and non-linear measurement data. It also shows Extended Kalman Filter can handle both uniform noise error conditions and non-uniform noise errors.

Differential equations modeling the system and measurement data in this case are non-linear. They contain trigonometric functions in them so the regular Kalman Filter cannot be used.

This 3-part demo does take the curvature of the earth into account.

-- PART 1: SYSTEM STATE: Lat, long, Velocity in X, Velocity in Y directions SYSTEM MEASUREMENT: Distance and direction of the object from the point of measurement
Modeled differential equations are non-linear

-- PART 2:
Same equations as Part 1 above but remap the measurement from distance and direction to X-Y (lat, long) coordinates

-- PART 3:
Same equations as Part 1 but use non-uniform noise error injection (or more real life conditions)

This video discusses moving vehicle tracking using radio frequency techniques and Kalman Filter for accurate position estimation. Subsequent videos will show Extended Kalman Filter for non-linear systems, Time Difference of Arrival and Angle of Arrival methods.

Kalman Filter is the DE FACTO standard for use in positioning and navigation. It has a wide range of applications including guidance, navigation, and control of vehicles, especially aircraft, spacecraft and moving ships. It also has usage in cognitive radio, sensors and others.

Kalman Filter is a statistical algorithm. It uses series of measurements fed into it over time to accurately estimate the results and overcoming noise presence in the measured data. It is a machine learning method. KF is modeled by a set of matrix equations for prediction and updating of the estimated results. It can be compute intensive.

The demo has 3 parts:

Modeled in Octave Matlab; uses X-Y coordinates (latitude, longitude), apply uniform error and estimate position

Uses Distance and Angle measurements with uniform error in the measurements

Uses Distance and Angle measurements but with more realistic non-uniform error distribution

Main discussion is about the developing an FPGA-based wireless relay system from our emulated gateway system. We also discuss issues encountered and how we overcome them. An actual demo is also presented as the 2nd half of the video.

The demo's first part is booting, bringing up the system, making wireless connection (In the demo, this part from the client, it will indicate as no Internet accessible. This is because the client is connected to the Internet via the gateway/proxy system. Not directly.)

The demo 2nd part is to browse the Internet and play various simultaneous videos to show its performance. This FPGA has less CPU cores and slower processor frequency (relative to the x86 CPUs). But still need to maintain high quality user experience. Optimization was done and painlessly.

The 3rd part of the demo is to use "tcpdump" to capture packets and prove that the actual packets traverse from wireless interface to the Ethernet interface as we expected how things should work in a proxy application.

The architecture supports wireless technology of different types and Ethernet. Wireless interface can be 5G networking, OFDM wireless, beamforming wireless interfaces, etc. The structures and code are in place. We will continue to advance this platform as you will see.

This is an exciting phase. It opens up many exciting possibilities for us.