Target Velocity Estimation for Quantization-Based Cooperative MIMO Radar and Communications System

Target velocity estimation is investigated for a cooperative multiple-input multiple-output (MIMO) integrated radar and communications (IRC) system employing quantized measurements. To reduce the communications burden, the local receivers quantize the local measurements, and then transmit the quantized measurements to the fusion center (FC). This paper discusses two distributed parameter estimation strategies, one directly quantizes the received signals at each local sensor and sends them to the FC for velocity estimation, while the other first estimates the Doppler frequency at each local sensor and then sends it to the FC after quantization. The FC estimates the target velocity utilizing the quantized measurements from all local receivers for both strategies. We derive the corresponding distributed maximum likelihood (ML) estimators and Cram\'{e}r-Rao bounds (CRBs). It is demonstrated that for small signal to clutter-plus-noise ratio (SCNR), the Doppler frequency quantization-based strategy has better estimation performance, while for large SCNR the received signal quantization-based strategy performs better.