Compressed Space-Time Adaptive Processing (CSTAP)

A wavelet transform technique is used to reduce (compress) the dimensionality of the covariance matrix as well as signal snapshots in space-time adaptive processing (STAP) to cut the computational demand. The algorithm is tested using both simulated airborne radar data generated by the high-fidelity airborne radar simulation software, RLSTAP, as well as real airborne radar data collected by the MCARM system. It shows that while the computational demand is reduced by as much as 85%, there is no sacrifice to signal detection. Unlike existing dimensionality-reduced algorithms which are based on certain assumptions and lead to partially adaptive STAP, the proposed algorithm, compressed STAP (CSTAP), does not require any assumptions, so is still fully adaptive.