Beamspace reduced-dimension space–time adaptive processing for multiple-input multiple-output radar based on maximum cross-correlation energy

Beamspace reduced-dimension space–time adaptive processing for multiple-input multiple-output radar based on maximum cross-correlation energy

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A new method for selecting auxiliary channels in reduced-dimension space–time adaptive processing (STAP) based on maximum cross-correlation energy has been proposed for airborne multiple-input multiple-output radar. It is demonstrated that the proposed algorithm can achieve the same output signal-to-interference–noise ratio (SINR) performance as the multistage multiple-beam STAP algorithm which can assure the optimal performance when the number of auxiliary channels is fixed, but the auxiliary channels selecting process of the proposed algorithm is extremely simplified. Hence, the computation complexity is reduced dramatically. Practical considerations dictate that only the minimum number of auxiliary channels [(adaptive degrees of freedom (DoFs)] is required to achieve the desired array performance. The proposed approach can achieve the desired output signal-to-interference–noise performance with the minimum number of auxiliary channels. Consequently, the proposed approach can reduce the requirement of the sample support dramatically. It is demonstrated that the SINR loss will be <3 dB when only one channel is selected as the auxiliary channel. Generally, two to three channels are enough even when the clutter covariance matrix is unknown. This will be more obvious advantage when the number of independent and identically distributed secondary samples is limited. The reduction in DoFs can make the proposed approach more suitable for the practical clutter environments.


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