Multi-target Joint Detection and Estimation of Doppler Radar Based on Superpositional Sensors

‍ ‍Multi-target detection and estimation is a basic task of Doppler radar. When the signal-to-noise ratio is low， in order to ensure that the target is detected， the threshold needs to be lowered and a large number of false alarms are generated. The multi-hypothesis tracking （MHT） and joint probabilistic data association （JPDA） methods based on data processing fail due to high computational complexity. Random finite set （RFS） filters based on original signal processing can effectively solve this problem. The Doppler radar echo signal is affected by multiple targets in a superpositional manner， and the problem of multi-target detection and estimation is a typical application of superpositional sensors. Based on the superpositional multi-Bernoulli （MBR） filter， this paper uses independent and identically distributed cluster （IIDC） RFS with accurate potential estimation to model the new target， and uses auxiliary particle filter The auxiliary particle filter （APF） realizes multi-target joint detection and state estimation. The simulation results show that the hybrid MBR and cardinalized probability hypothesis density （CPHD） filter has better performance for multi-target detection and estimation of Doppler radar than the MBR filter， and reduces the computational complexity.