一种基于Taylor和Kalman的室内协同定位方法

结合Chan算法、Taylor算法及Kalman算法三种TDOA算法的优点,提出一种能应用于室内实时定位的协同方法。首先基于Chan与Taylor的协同定位方法估算位置信息,并通过对估计结果的残差设置阈值来鉴别NLOS,从而抛弃受到NLOS污染严重的测量数据。其次,再对符合条件的测量数据,利用Kalman方法计算定位结果,与Taylor方法的定位结果通过设置判别条件进行比较,以此进一步抑制NLOS干扰。对符合判别条件的定位结果,进行残差加权及移动平均加权处理,从而完成最终定位结果的更新。最后,利用室内实时定位实验,证明该方法能有效过滤受到NLOS污染严重的测距数据,提高定位精度,并且具有良好的稳定性。

An Indoor Cooperative Localization Method Based on Taylor and Kalman Algorithms

A cooperative method for indoor real-time localization based on three TDOA algorithms is presented in this paper. The algorithms are Chan algorithms, Taylor serials expansion algorithm, and Extended Kalman filter algorithm. Firstly, estimation result is calculated by a cooperative method based on Chan and Taylor and threshold value of its residuals is set to identity NLOS and discard the ranging data that is disturbed severely by NLOS. Then, Kalman method is used for the retain data to get estimation position. The location result of Kalman is compared with the result of Taylor through setting some condition to further restrain NLOS error. After, the final estimation result is obtained, by using residual weighting algorithm and moving weighted average method to the meet results. Finally, the experimental results show that this method can restrain NLOS error efficiently and improve the precision of location.