Subspace Optimization in Centralized Noncoherent MIMO Radar

Abstract

We consider the problem of subspace optimization for centralized noncoherent multiple input-multiple output (MIMO) radar based on various measures such as capacity, diversity, and probability of detection. In subspace centralized noncoherent MIMO radar (SC-MIMO), a subset of stations is selected based on channel knowledge or channel statistics to reduce system complexity while simultaneously attempting to optimize the performance of the reduced-dimension centralized MIMO radar system. The radar transmitters are assumed to be sufficiently separated (e.g., at different locations) to yield spatially white channel transfer gains and are assumed to operate on a noninterference basis through time-division or frequency-division multiplexing. Detection optimization for the SC-MIMO system in a Neyman Pearson (NP) sense is found to be equivalent to selecting the subspace that maximizes the Frobenius norm of the corresponding channel matrix. Information-theoretic measures for capacity and diversity are also applied to the problem of subspace selection. Channels with temporal coherence times that are long relative to the radar system's latencies and channels with coherence times that are short relative to the radar system's latencies are considered. In the former case, metrics are based upon instantaneous channel estimates, whereas in the latter case, average channel estimates are used. Numerical analyses are conducted to illustrate the use of the metrics for optimizing system performance.