Target Detection Performance of Distributed MIMO Radar Systems Under Nonideal Conditions

Assessing the detection capability is crucial for the design and implementation of distributed multiple-input-multiple-output (MIMO) radar systems. However, existing MIMO radar models have mainly been established under ideal conditions, assuming spatially uncorrelated target echoes, strictly orthogonal transmitted waveforms, and negligible system errors. Such idealized assumptions may not hold in practical scenarios, making it challenging to comprehensively and realistically evaluate radar detection performance. Motivated by this, our article investigates the theoretical radar detection capability by removing these idealized assumptions. We develop a more general system model for distributed MIMO radar that considers the probable spatial correlations of target echoes, waveform nonorthogonality, and various system errors. These errors include synchronization errors in time, frequency, and phase, uncertainties in site position and beam pointing, as well as mismatch errors between the test-cell center and the target. Using this enhanced model, we derive an accurate and efficient equation to evaluate detection performance by employing a moment-matching approximation strategy. In addition, we analyze the impact of nonideal conditions through simulations. Our findings reveal several important insights. First, channel-correlated target returns can be advantageous for target detection, particularly in low signal-to-noise ratio (SNR) scenarios. Second, nonorthogonal waveforms increase the sensitivity of radar detection performance to nonideal factors. Finally, the threshold at which system errors significantly degrade radar detection performance can be adjusted by radar parameters, such as waveform characteristics and beamwidths. These results contribute to the design of radar systems in various aspects, including site placement, waveform optimization, beamforming, and test-cell creation.