Parameter Estimation Using Quantized Cloud MIMO Radar Measurements

Joint location and velocity estimation is studied for cloud multiple-input multiple-output (MIMO) radar. To reduce communication burden, consider that local measurements received at each receiver contributed by each transmitter are quantized before sent to a fusion center. Unlike the existing literature on distributed and quantized sensor data estimation, the signal model for our problem turns out to be nonlinear and complex. We first analyze the quantization outputs, whose probability mass functions are utilized to calculate the maximum-likelihood (ML) estimates and the Cramer-Rao bounds (CRBs). Then, to facilitate computation, the quantization outputs are approximated as the inputs plus Gaussian quantization error. The corresponding ML estimates and CRBs are provided. Estimation performance under the direct and approximate analyses are compared and the effect of the quantization bits is presented.