基于改进加权质心和UKF的移动目标定位算法  

Moving target localization based on improved weighted centroid and UKF algorithm

作　　者：许杰[1,2] 戚大伟[3] 

机构地区：[1]黑龙江八一农垦大学信息技术学院,黑龙江大庆163319 [2]东北林业大学工程技术学院,哈尔滨150040 [3]东北林业大学理学院,哈尔滨150040

出　　处：《吉林大学学报（工学版）》2016年第4期1354-1359,共6页Journal of Jilin University:Engineering and Technology Edition

基　　金：国家自然科学基金项目(31170518);黑龙江省科技攻关项目(GC01KC156);黑龙江省教育厅科研项目(12531445)

摘　　要：为了解决传统算法对于移动目标定位误差较大的问题,提出了一种基于接受信号强度指示(RSSI)的改进加权质心定位算法,并结合一种改进的UKF算法对RSSI进行有效滤波。针对传统质心定位算法只能静态设置权重的缺陷,提出利用定位误差对距离进行修正,有效提高了算法的定位精度。对于标准UKF算法,在采样过程中采用改进最小偏度策略,引入调节因子,保证了预测方差矩阵的半正定性,并且在滤波更新过程中采用衰减记忆滤波方法,有效抑制了滤波发散,提高了滤波精度。仿真实验结果证明了本文算法的正确性和有效性。Using traditional localization algorithms the location error is high for moving target.To solve this problem,an improved weighted centroid localization algorithm based on Received Signal Strength Intensity（RSSI）is proposed,and an improved Unscented Kalman Filter（UFK）algorithm is combined for RSSI filtering.For the defect that traditional centroid localization algorithm can only statically set the weights,in this improved algorithm,the localization error is used to correct the distance,which can effectively improves the localization accuracy.For the standard UKF algorithm,the minimum skewness strategy is used in the sampling process,and the adjustment factors are introduced to ensure the positive semidefinite of the prediction covariance matrix;simultaneously,the fading memory filtering method is used in filter updating process,which effectively inhibits the filter divergence and improves the filtering accuracy.Simulation results prove the correctness and effectiveness of the proposed algorithm.

关 键 词：信息处理技术 移动目标定位 加权质心算法 无迹卡尔曼滤波 

分 类 号：TN911[电子电信—通信与信息系统]