Fusion detection in distributed MIMO radar under hybrid-order Gaussian model.

In this paper, we deal with the point-like target fusion detection in a partially homogeneous environment with distributed MIMO radar. Specifically, we consider the imperfect waveform separation problem, which means that the matched filter output contains not only the auto-correlation term of the current matched waveform but also the cross-correlation terms, called waveform residuals, with the remaining transmitted waveforms. To this end, a hybrid-order Gaussian (HOG) model is utilized, where the target amplitude is deterministic but unknown, and the waveform residuals obey the Gaussian distribution. Then two adaptive detectors are developed according to the GLRT and Wald test, named HOG-GLRT and HOG-Wald respectively. At the fusion detection stage, we focus on the problem of signal-to-noise ratio (SNR) diversity, i.e., the detection performance degradation due to the average weighting of spatial diversity channels when the echo SNR differs. Combined with the Model Order Selection criterion and multiple hypothesis test, two modified fusion detectors based on channel selection are proposed, named MHOG-GLRT and MHOG-Wald. Finally, the numerical simulation results show that the HOG-GLRT is sensitive against waveform residuals, while the HOG-Wald exhibits strong robustness. And it also demonstrates the effectiveness of the MHOG-GLRT and MHOG-Wald facing the extreme scene of SNR diversity. • We propose two detectors for distributed MIMO radar under imperfect waveform separation. • We develop two modified detectors to solve the problem of SNR diversity. • These detectors can improve the detection performance compared with the existing ones. [ABSTRACT FROM AUTHOR]