The multifunctional radar (MFR) possesses robust anti-jamming capabilities, and effectively jamming with it is an urgent problem that needs to be addressed. This paper first analyzes the process of cognitive radar antagonism and subsequently constructs a jamming decision system with cognitive abilities based on real battlefield environments. An algorithm based on proximal policy optimization (PPO) is employed for the jamming decision algorithm to determine the optimal jamming strategy. Through simulation experiments, it has been demonstrated that the PPO-based jamming decision algorithm can learn the most effective jamming strategy in dynamic game conditions between radar and jammers. It exhibits faster convergence speed compared to traditional strategy gradient methods.
Considering the high computational burden of typical space–time adaptive processing (STAP) based on sparse representation (SR) (SR-STAP) method, a reduced-dimension (RD) SR-STAP method using simplified time–time (STT) transform spectrum is proposed to overcome this issue. First, the STT transform spectrum formula of clutter on cell under test (CUT) is deduced and the main energy of CUT in the STT transform domain is extracted. Second, to design the RD matrix, an adjustable RD threshold is defined, which is used to make a comparison with STT transform spectrum energy. Third, the RD SR dictionary is constructed to estimate the clutter spatial–temporal spectrum. Numerical simulation results demonstrate that the proposed sparse representation based on simplified time-time-STAP method reduces the computational burden significantly and has a highly similar clutter suppression performance compared with the typical SR-STAP.
The performance of space–time adaptive processing (STAP) may degrade significantly when some of the training samples are contaminated by the signal-like components (outliers) in nonhomogeneous clutter environments. To remove the training samples contaminated by outliers in nonhomogeneous clutter environments, a robust nonhomogeneous training samples detection method using the sparse-recovery (SR) with knowledge-aided (KA) is proposed. First, the reduced-dimension (RD) overcomplete spatial–temporal steering dictionary is designed with the prior knowledge of system parameters and the possible target region. Then, the clutter covariance matrix (CCM) of cell under test is efficiently estimated using a modified focal underdetermined system solver (FOCUSS) algorithm, where a RD overcomplete spatial–temporal steering dictionary is applied. Third, the proposed statistics are formed by combining the estimated CCM with the generalized inner products (GIP) method, and the contaminated training samples can be detected and removed. Finally, several simulation results validate the effectiveness of the proposed KA-SR-GIP method.
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