Target Measurement Performance of Distributed MIMO Radar Systems Under Nonideal Conditions.

Evaluating target measurement performance is significant for the design and application of distributed multiple-input multiple-output (MIMO) radar. Existing research into the target measurement performance was mostly conducted under ideal conditions. In practice, however, distributed MIMO radar may inevitably suffer from the system errors in time sync, frequency sync, and beam pointing as well as the uncertainties in sensor positions. These non-ideal factors may lead to a degradation of the target estimation performance to some extent, and this joint impact remains unclear. Motivated by this, in this paper, we systematically explore the target measurement performance of distributed MIMO radar under non-ideal conditions. First, we formulate a general system model for distributed MIMO radar incorporating the errors in time sync, frequency sync, sensor position, and beam pointing. By treating these system errors as Gaussian random variables, we form the hybrid Cramér-Rao Bound (HCRB) for the joint estimation of the target parameters and these errors. The derived HCRB is shown to be effective in lower bounding the mean-square error (MSE) of the hybrid maximum likelihood/maximum a posteriori (ML/MAP) estimate. Therefore, this bound can be used as an asymptotic benchmark or metric of the radar performance in target measurement. Based on the HCRB, we also examine the effects of these non-ideal factors on the target measurement performance by simulations. The results offer some perspective on the relationships between the system tolerances for these system errors and the radar parameters; these findings will be helpful in the system design of distributed MIMO radar.