A nonlinear optimized location algorithm for bistatic radar system

This paper discusses a nonlinear optimized location algorithm for bistatic radar system. The bistatic radar system proposed in the paper contains a T/R station and an R station. Generally, the T/R station provides data such as azimuth angle and distance, and in some circumstance, elevation of a target while the R station has measurements of azimuth angle and distance sum of the target. The coordinate transform is indispensable because of the long base line of a bistatic system. Thus five measurements transformed are presented for location resolution. Theoretically, 3-D location of a target requires only three independent measurements which correspond to three independent surfaces of position, therefore, it is possible to use this data redundancy to improve location accuracy. Some papers have discussed the location methods by means of measurement subsets division, GDOP analysis of measurement subsets, simplified LMS estimation (SLMS) or selecting the best subset (SBS) for solution. The SLMS method is based on the assumption that the relevance between every two different subsets is weak correlated to be neglected. In fact, it is not so. Data redundancy has not been sufficiently used in the SBS method. A nonlinear optimized method is presented in this paper which is based on the assumption that the change of GDOP in observation space of a T/R-R system is smooth that the weighted matrix of LMS estimation in the controlled observation area can be approached by means of nonlinear LMS learning method. Monte Carlo simulation test results of different methods are also given to show the improvement in location precision