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摘要: 研究了一类基于RSSI（Received signal strength indication）测距的分布式移动目标跟踪问题，提出了一种适用于事件触发无线传感器网络（Wireless sensor networks，WSNs）的分布式随机目标跟踪方法.首先考虑移动机器人模型的不确定性，引入了带有随机参数的过程噪声协方差，应用改进平方根容积卡尔曼滤波（Square root cubature Kalman filter，SRCKF）得到局部估计；然后采用无模型CI（Covariance intersection）融合估计方法以降低随机过程噪声协方差带来的不利影响.该方法充分利用有模型和无模型方法的优势，实现系统模型和量测不理想情况下的分布式目标跟踪.基于E-puck机器人的目标跟踪实验表明，事件触发的工作模式可有效地减少能量消耗，带随机参数的滤波方法更适合于随机目标的跟踪.
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关键词:
- 事件触发 /
- RSSI /
- 随机目标跟踪 /
- 平方根容积卡尔曼滤波
Abstract: This paper is concerned with distributed target tracking problem using RSSI method and presents a distributed tracking method for maneuvering targets with event-triggered wireless sensor networks (WSNs). Firstly process noise covariance with random parameter is introduced under consideration of modeling uncertainties, and then a modified square root cubature Kalman filter (SRCKF) is employed to generate local estimates. Secondly, the non-model-based CI fusion estimation method is employed to reduce the adverse effects of random process noise covariance. The method combines advantages of both model-based and non-model-based estimation methods in the case of inaccurate model and unreliable measurements. Simulation and experiment of the E-puck robot tracking show that the event-triggered mechanism can greatly reduce energy consumption and that the filtering method with random parameters is more suitable for maneuvering target tracking.1) 本文责任编委 潘泉 -
图 1 无线传感器网络环境下的分布式移动机器人跟踪系统
Fig. 1 The distributed mobile robot tracking system in WSNs
图 2 不同过程噪声协方差下的估计误差
Fig. 2 Results of the estimation errors with different process covariances
图 3 E-puck机器人目标跟踪实验平台
Fig. 3 The E-puck robot-based target tracking experiment platform
图 4 不同过程噪声协方差下的移动机器人跟踪结果
Fig. 4 Results of the mobile robot tracking with different process covariances
图 5 基于SRCKF的局部跟踪结果和CI融合估计结果的对比
Fig. 5 Comparison of the SRCKF-based local estimates and the CI fusion estimates of the mobile robot tracking
图 6 基于SRCKF的局部估计和CI融合估计 $X$ 轴误差对比
Fig. 6 Comparison of the SRCKF-based local estimation and the CI fusion estimation errors in the $X$ -coordinate
图 7 基于SRCKF的局部估计和CI融合估计 $Y$ 轴误差对比
Fig. 7 Comparison of the SRCKF-based local estimation and the CI fusion estimation errors in the $Y$ -coordinate
表 1 不同过程协方差情况下移动机器人跟踪的仿真结果
Table 1 The simulation results of the mobile robot tracking with the different process noise covariances
序号 q1 q2 局部1 局部2 局部3 平均 LCI CI 1 2.0 6.0 10.7446 10.7439 10.7589 10.7052 10.6792 10.7682 2 4.0 6.0 9.6714 9.6725 9.6676 9.6554 9.6555 9.6613 3 8.0 10.0 9.4976 9.4978 9.4981 9.4927 9.4940 9.4899 4 8.0 12.0 9.7339 9.7360 9.7365 9.7220 9.7289 9.7113 5 2.0 12.0 9.5359 9.5414 9.5236 9.3224 9.3629 9.3252 6 4.0 10.0 9.3189 9.3174 9.3135 9.2346 9.2505 9.2281 7 6.0 8.0 9.2046 9.2055 9.2082 9.1959 9.1985 9.1948 
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