中文摘要：

针对无线传感器网络中基于接收信号指示强度（RSSI）定位系统在精确性和实时性方面存在的问题,提出了一种基于自适应比例修正无迹卡尔曼滤波（ASUKF）的定位估计算法。通过分析RSSI定位模型的特点,将定位问题转化为非线性系统估计问题。该算法在滤波过程中采用比例修正对称采样策略,并利用次优Sage-Husa估计器实时处理系统噪声的统计特性,对目标位置和信道参数进行同时估计解算。实验及仿真结果表明,与标准UKF估计算法相比,新算法有效减小了状态估计误差,提高了滤波的稳定性,定位精度更为准确。

英文摘要：

To improve the accuracy and real-time performance of the the Received Signal Strength Indication（RSSI）-based location system,an estimation algorithm based on adaptive scaled Unscented Kalman Filter（UKF） is proposed.By analyzing the RSSI location model,this new algorithm convents the location problem into estimation of nonlinear system model.It can estimate the target position and the RSSI channel attenuation parameter simultaneously by using the scaled symmetric sampling and the modified Sage-Husa noise statistic estimaters.The experimental and simulation results show that,compared with standard UKF,the proposed algorithm can effectively reduce the state estimation error,improve the filter stability and provide more better accuracy for target location.