<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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.

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

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

running-head

Target Motion Analysis by incorporating Doppler frequency:

Target motion analysis is a key requirement of autonomous and self-driving machines like drones and robots. However, with strict weight limits, the aerospace...

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

Online veröffentlicht: 28. Apr. 2021

Seitenbereich: 1 - 12

Eingereicht: 25. Jan. 2021

DOI: https://doi.org/10.21307/ijssis-2021-008

Schlüsselwörter
Target motion analysis, TMA, Bearings, Doppler frequency, Kalman filter, Observability conditions

© 2021 Ehsan Ul Haq et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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

Online veröffentlicht: 28. Apr. 2021

Seitenbereich: 1 - 12

Eingereicht: 25. Jan. 2021

DOI: https://doi.org/10.21307/ijssis-2021-008

SchlüsselwörterTarget motion analysis, TMA, Bearings, Doppler frequency, Kalman filter, Observability conditions

© 2021 Ehsan Ul Haq et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Target motion analysis, TMA, Bearings, Doppler frequency, Kalman filter, Observability conditions