Comparative study of Kalman filter-based target motion analysis by incorporating Doppler frequency measurements

Target motion analysis is a key requirement of autonomous and self-driving machines like drones and robots. However, with strict weight limits, the aerospace industry is always on the hunt for simpler and lighter sensing solutions. Continuous-wave Doppler radars are the simplest radars that can easily obtain a target’s relative velocity using the Doppler shift in the received wave. However, these radars cannot provide the target’s range. In this work, we address the problem of obtaining target’s range and velocity by incorporating Doppler frequency measurements from a simple continuous wave Doppler radar. To this end, we find out the movement patterns and maneuvers that an observer can make to converge to the target’s location. After presenting the observability requirements, we design and compare various non-linear Kalman filter-based target trackers. We experimented with different simulation scenarios to compare the tracking results with bearings-only, frequency-only, and bearings-frequency measurement sets. In our analysis, Unscented Kalman Filter with bearings-frequency measurements performed best. Experiments show that an observer can locate the target accurately within 10 cm by incorporating Doppler frequency measurements. Moreover, it also reduced the convergence time to a fraction of a second.