Multiple Passive Location System Nonuniformly-Sample Data Fusion Under Complex Eviroment

With the excellent hidden characteristics of passive receiving mechanism, passive location gains wide attention and recognition. However, this is confronted with the problems of low precision of location and instability of working status under complex environment. Multi-sensor data fusion technique provides an effective solution to improve the performance of passive location. Meanwhile, with the combination of intelligent information processing technique, Multi-sensor data fusion can also achieve improvement of robustness in multi-passive-sensor system. Based on Multi-sensor data fusion technique, this paper investigates data fusion algorithm based on the passive location observation characteristics, synchronizing algorithm for passive location nonuniformly-sampled data and data fusion algorithm under complex environment based on the Neuro Network-Fuzzy Reasoning theory.Firstly, with the in-depth analysis of observation characteristics, a multi-dimension track correlation algorithm is proposed. This method includes two kinds of observation data for the correlation rule, hence resulting in enhanced spatial resolving capability. Weighted track fusion With CEP as weight is proposed. This method has less calculation and notable result. To be adaptable to the complex environment, multi-model track fusion algorithm with the basis of weighted track fusion is given. The experiment is carried with two sections, simulation and actual measurement, to evaluate the validity of this algorithm.Sencondly, passive location system nonuniformly-sampled data synchronization algorithm is designed based on the particularity of the passive location nonuniformly-sampled data, which including adaptive system period algorithm with sliding window and fast interpolation algorithm. The system period is determined by weighted fusion result of all sensors periods with precision as weight and a index function is designed to evaluate the deviation of system period. When the index exceeds the given threshold, the algorithm adjusts the system period to track the data interval of all passive sensors. Threshold and sliding window decide the sensitivity and stability.Finally, to solve the passive location system data fusion problem under the...