Joint Coherent Integration and Detection of Radar Spread Targets With Range Migration

With the advancement of high-resolution radar technologies, the detection of range spread targets (RSTs) with range migration (RM) has become increasingly important. Due to the severe energy dispersion caused by unknown range spread and RM effects during the illumination period, the detection performance for RSTs may degrade significantly. We make two contributions toward addressing this problem. First, we propose a joint coherent integration and detection (JCID) framework motivated by a multiple-hypothesis architecture with respect to the RST motion state and scattering centers (SCs) distribution. Within this framework, the detection of RSTs, and the joint estimation of both target motion state and SCs’ distribution, are performed simultaneously using the maximum likelihood (ML) criterion. In essence, this method improves the detection performance of RSTs by maximizing target energy utilization through the combination of inter-pulse coherent integration and intra-pulse incoherent integration. Second, as the proposed JCID framework is a general strategy without any particular model assumptions, we further derive the detailed implementation equations of JCID for a frequently encountered case involving zero-mean Gaussian noise. Additionally, the exhaustive search of all hypotheses can be computationally intensive. Hence, we incorporate a sparse representation (SR)-based estimation strategy for the prior knowledge of target SCs into JCID, called JCID-SR. This algorithm achieves both tractable computational complexity and superior detection performance. Our theoretical analysis shows that the proposed JCID-SR method maintains the constant false alarm rate (CFAR) property. Finally, comprehensive performance assessments are conducted to confirm the effectiveness of the proposed method through numerical simulations and real-measured data processing.