具有量化的传感器网络机动目标状态估计算法  

Maneuvering Target State Estimation Algorithm with Quantization for Sensor Network

作　　者：何文韬 陈欣[1] 王威振 He Wentao;Chen Xin;Wang Weizhen(College of Automation Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China)

机构地区：[1]南京航空航天大学自动化学院,南京210016

出　　处：《兵工自动化》2025年第2期55-59,共5页Ordnance Industry Automation

摘　　要：针对传感器网络(sensor network,SN)对机动目标的状态估计问题,提出一种交互式多模型量化无迹卡尔曼滤波(interacting multiple model quantization-based unscented Kalman filter,IMM-QUKF)算法。为节省通信带宽,传感器的测量数据经过概率量化后发送给远程局部估计器;考虑量化机制引入的误差,设计改进的无迹卡尔曼滤波算法,并与交互式多模型算法结合得到局部估计。数值仿真验证结果表明:该算法对于机动目标具有较好的跟踪效果。To estimate the state of a maneuvering target in sensor networks,an interacting multiple model quantized unscented Kalman filter(IMM-QUKF)algorithm is proposed.In order to save the communication bandwidth,the measurement data of sensors are sent to the remote local estimator after probability quantization.Considering the error introduced by the quantization mechanism,an improved unscented Kalman filter algorithm is designed and combined with the interacting multiple model algorithm to obtain the local estimation.Numerical simulation results show that the algorithm has good tracking effect for maneuvering target.

关 键 词：传感器网络 状态估计 交互式多模型 无迹卡尔曼滤波 概率量化 

分 类 号：TP212[自动化与计算机技术—检测技术与自动化装置]