Research On Target Localization And Power Allocation In Widely Separated MIMO Radar

Multiple-Input Multiple Output(MIMO) radar has become one of the hot topics at the frontier of radar research fields recently. According to the distance between radar antennas, MIMO radar can be classified into two categories, MIMO radar with colocated antennas and that with widely separated antennas. The former one can obtain waveform diversity gain to improve the angular resolution of radar system, while the latter one can utilize the spatial diversity gain to combat the target radar cross section(RCS) scintillation. The widely separated MIMO radar provides an effective way to tackle stealth target detection problem and poor anti-jamming capability of conventional radar from diverse target view directions. The preliminary achievements on widely separated MIMO radar research has shown its great advantage of target detection and parameter estimation. However, the processing technique, and performance evaluation and system design of this MIMO radar type are rarely investigated, additionally, some concepts and key technologies need further investigation. This dissertation focuses on widely separated MIMO radar target localization and power allocation research. The main efforts are devoted to dealing with the key problems encountered in the existing target localization approaches and power allocation algorithms. Specifically, the research in the dissertation consists of several aspects which are listed as follows.Chapter 1 elaborates the background and significance of this dissertation, introduces the research status of MIMO radar especially widely separated MIMO radar, and finally points out the necessity of target localization approaches and optimal power management investigation.Chapter 2 investigates the MIMO radar target localization performance and its sensitivity to radar antenna position errors. The target localization problem is firstly modeled as maximum likelihood estimation(MLE) problem, and the Fisher information matrix(FIM) as well as Cramer-rao lower bound(CRLB) are derived. In practice, radar antenna position error would decrease the localization accuracy. The mean square errors(MSE)of target location estimate ignoring antenna position errors is derived by employing firstorder Taylor approximation. Then it derives the joint CRLB of both antenna position errors and target location, and the degradation of localization accuracy due to the antenna position errors is analyzed quantifiablely, which provide some theoretical foundations for the subsequent investigation.Chapter 3 investigates the MIMO radar indirect localization method of a single target based on semidefinite programming theory. The indirect localization based on bistatic ranges is modeled as a nonlinear least square(NLS) problem. The linearized localization method employing first-order Taylor approximation is derived. To theoretically guarantee the global optimal solution of this problem, we proposes a semidefinite programming target localization method. It introduces nuisance variables and turns the NLS problem into convex optimization problem with constraints, and then relaxes the non-convex constraints using semidefinite relaxation technique to turn it into a solvable semidefinite programming problem. Under this framework, it proposes a semidefinite programming localization method assuming there are antenna position errors. This method can significantly reduce the impact of antenna position errors and enhance the robustness of target localization.Chapter 4 investigates the MIMO radar direct localization method of multiple targets based on sparse recovery theory. The block sparse representation model is firstly established for the direct localization based on match filtered signal, and target localization is thus turned into a block sparse recovery problem. To solve this problem, it develops a multi-target localization algorithm under the framework of block sparse Bayesian learning(BSBL), which explores the intra-block correlation and improves recovery accuracy. The simulation results have shown that the proposed algorithm is capable of dealing with localization problem such as dense targets and compressed sampling condition without data association. Moreover, an iterative algorithm is proposed under framework of BSBL and MLE to tackle the phase synchronization problem in coherent processing. Specifically,it estimates the phase mismatch error using MLE and rebuilds scattering coefficients using BSBL iteratively, and it obtains high localization accuracy with phase mismatch error correction capability.Chapter 5 investigates the optimal power allocation problem in MIMO radar for target localization. Using indirect localization model, the Bayesian FIM of target location is derived, and the stochastic observability computation method employing particle filter is described. The power allocation problem is modeled as a non-convex quadratic programming problem, where stochastic observability is the object function and total transmit power is the constraint. To solve this problem, a power allocation method based on Shapley value under the cooperative game theory framework is derived, and the detailed implementation is given. The Shapley value computation process is simplified through Weighted-Graph Game and the concept has clear physical meaning. The simulation results has demonstrated that, the proposed algorithm has the ability to improve localization accuracy and resource utilization by optimal power allocation.Chapter 6 makes a summery of the research studies and main contributions in this dissertation, and also points out several open problems in future research.