In wireless location technology, the environment restriction cause the coverage area of single signal is limited and in-continuous. So it is hard to get better position precision and larger coverage area. At the same time, the traditional non-linear filter has too high complexity, So the speed of location is influenced very bad. Due to the mutual limits among precision, coverage and real-time, how to find a way to get a good position precision with less complexity is a key problem. The paper considers utilizing detection probability model to fuse multiple measurement signals in target area effectively. Then a line and nonlinear hybrid filter under Bayesian rule is established to estimate target location information. It is extremely interesting to improve the robust of location and tracking. Simulation results show the various signal fusion algorithm based on hybrid filter can not only guarantee the positioning accuracy very well, but also save more computation time. At all the performance of positioning and track is improved very well.%无线定位技术中,由于环境的限制,导致单一信号覆盖面受限,而且不连续,因此很难获得较高的定位精度和较广的覆盖面.同时,传统的非线性滤波计算复杂度太高,严重影响定位的实时性.考虑到精度、覆盖面和实时性相互制约的关系,如何寻找到一种不过多增加计算负担,并能保证一定定位精度的无缝定位方法是本文的重点.本文考虑利用检测概率模型,充分而有效地融合目标区域内的多种信号的量测信息,在贝叶斯框架下建立一种线性和非线性的组合滤波模型,对目标进行有效地定位估计,改善了定位跟踪技术的稳健性问题,具有较高的实际价值.仿真结果表明,论文所设计的多信号组合滤波模型,既能保证定位的精度,又能较多地节省计算时间,显著提高了定位跟踪的综合性能.
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