基于滤波扩维融合的传感器定位测距方法改进

Improvement of Sensor Location and Distance Measurement Method Based on Filtering and Extended Dimension Fusion

研究传感器定位测距优化,可以对传感网络中的目标位置和速度进行准确跟踪。由于传感网络中存在多径、绕射、障碍物等因素,导致传感网络路径损耗使得定位过程中产生距离误差,而传统的PSO算法只能对节点的讯息熵特点提取,没有相关的补偿系数对产生的距离误差进行分析和补偿,导致降低定位精度。提出滤波扩维融合的传感器定位测距算法,构建传感器数据融合滤波结构模型,采用自适应信息分配方式对数据结果进行最优的状态估计,采用带宽约束和量化阈值的方法进行传感器定位测距,根据信息分配原则,在融合中心以各传感器的测量值为依据,更新融合中心的状态估计值和一步预测值,求得信号的时延和尺度,引入滤波扩维融合算法,提高网络跟踪系统的位置和速度估计精度,增加定位测距算法的性能。仿真结果表明,改进算法对传感器网络中目标的位置和速度的跟踪更加准确。

A sensor localization and distance measurement algorithm based on filtering and dimension extension fusion is proposed, and the structure model of sensor data fusion filtering is constructed. By using adaptive information distribution method, the optimal state estimation of the data result is performed. The method of bandwidth constraint and quantization threshold is used to make the localization and distance measurement of the sensor. According to the principle of information distribution, the measured value of each sensor in the fusion center is as the basis to update the state estimation value and single - step predictive value and the time delay and the scale of the signal are obtained. The filtering and dimension extension fusion algorithm is introduced to improve the position and speed estimation accuracy of the network tracking system, and increase the performance of the localization and distance measurement algorithm. Simulation results show that the improved algorithm can accurately track the target position and speed in sensor network.