Sparsity-based space-time adaptive processing in random pulse repetition frequency and random arrays radar

In this paper, we focus on sparsity-based space-time adaptive processing (STAP) in airborne radar with compressive sampling both in Doppler and spatial domains. Compared with the uniform pluses repetition Frequency (UPRF) and uniform arrays (UA) radar, the designed radar transmits random pulse repetition interval pulses and receives the returns with random arrays, which reduces the number of pulses in one coherent processing interval (CPI) and brings down the number of sensors. Firstly, we build the sparse model with spatial-temporal compressive sampling, and analyze the restricted isometry property (RIP) for the steering dictionary. Then, we recover the clutter angle-Doppler profile via existing sparse recovery algorithms, and design the space-time filter to mitigate the clutter. Simulations are conducted to illustrate the effectiveness of the sparsity-based STAP in random PRF and random arrays radar.