Incorporation of aircraft orientation into automatic target recognition using passive radar

• Published in: IET radar, sonar & navigation Vol. 14; no. 7; pp. 1079 - 1087
• Main Authors: Ehrman, Lisa M, Lanterman, Aaron D
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 01.07.2020
• Subjects: aircraft orientation; aircraft position; antenna gain patterns; ATR algorithms; automatic target recognition capabilities; dynamic programming; extended Kalman filter; feasible orientation profiles; flight model; FM radio; Kalman filters; nonlinear filters; passive radar; passive radar exploiting illuminators; potential targets; propagation losses; radar antennas; radar cross‐section; radar cross‐sections; radar detection; radar return; radar signal processing; radar target recognition; radar tracking; Research Article; signal model accounts; target library; target orientations; target tracking; target types; tracking algorithm; velocity measurement; radar detection; tracking algorithm; automatic target recognition capabilities; passive radar exploiting illuminators; FM radio; radar antennas; radar signal processing; passive radar; ATR algorithms; radar target recognition; signal model accounts; radar cross-section; Kalman filters; aircraft orientation; feasible orientation profiles; radar tracking; target library; dynamic programming; aircraft position; target types; nonlinear filters; extended Kalman filter; target tracking; radar cross-sections; radar return; velocity measurement; antenna gain patterns; propagation losses; potential targets; flight model; target orientations
• ISSN: 1751-8784; 1751-8792; 1751-8792
• DOI: 10.1049/iet-rsn.2020.0010

Most research regarding passive radar exploiting ‘illuminators of opportunity’, such as FM radio, has focused on detecting and tracking targets. This study explores adding automatic target recognition (ATR) capabilities to such systems. The ATR algorithms described here use the radar cross-section (RCS) of potential targets, collected over a short period of time. The received signal model accounts for aircraft position and orientation, propagation losses, and antenna gain patterns. One proposed algorithm uses a coordinated flight model to estimate aircraft orientations, while a more sophisticated algorithm uses an extended Kalman filter to estimate the target orientations along with measures of uncertainty in those estimates. In both cases, the orientations are estimated using velocity measurements obtained from a tracking algorithm. The radar return of each aircraft in the target library is simulated as though each is executing the same manoeuvre as the target detected by the system. To improve the robustness of the result, the more sophisticated algorithm jointly optimises over feasible orientation profiles and target types via dynamic programming.