Detection of Manoeuvring Low SNR Objects in Receiver Arrays

In this work, we are interested in detecting manoeuvring objects in high noise background using an active sensor with a uniform linear array (ULA) receiver and propose a joint pulse integration and trajectory estimation algorithm. This algorithm allows us to detect low SNR objects by integrating multiple pulse returns while taking into account the possibility of object manoeuvres. In the proposed algorithm, the detection is performed by a Neyman-Pearson test, i.e., a likelihood ratio test. The likelihood function used in this test accommodates the radar ambiguity function evaluated in accordance with object related parameters such as location, velocity and reflection coefficient. The trajectory estimation is performed by Bayesian recursive filtering based on the state model of the location and velocity parameters. The reflection coefficient is estimated by a maximum likelihood (ML) estimator. These estimates are used in pulse integration, leading to coherent integration during a coherent processing interval (CPI) and non-coherent integration across consecutive CPIs. We also compare the proposed algorithm with conventional techniques.