Multi-target Joint Detection and Ambiguity Resolving Based on Adaptive Birth Density

Pulse Doppler （PD） radar will produce range ambiguity and Doppler ambiguity problems. The traditional method is to transmit multiple pulse repetition frequencies （PRFs） and correlate them to solve the ambiguity. However， when the signal-to-noise ratio is low， a large number of false alarms are generated. To ensure the detection of targets by using low threshold. The Traditional method fails to solve the ambiguity because of the high computational complexity caused by data association， the cardinality balanced multi-target multi-Bernoulli （CBMeMBer） based on random finite set can effectively solve this problem. In this paper， the CBMeMBer filter is used to estimate the number of targets and solve ambiguity in Bayesian framework， Aiming at the nonlinear problem of the model， a sequential Monte Carlo （SMC） implementation method based on adaptive newborn density is proposed. Simulation results showed that the proposed filter can achieve joint detection and estimation of multiple targets using ambiguous measurements containing densely distributed false alarms， and its performance was better than cardinalized probability hypothesis density （CPHD）.