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摘要: 为提高运动车辆定位可靠性与精度,研究了基于交通无线传感器网络的运动车辆定位系统。根据车辆位置区域随速度变化的规律,提出了一种变区间搜索量子粒子群算法对测量的车辆定位参量进行坐标粗估计,由于噪声干扰和信号传输延时,坐标粗估计值存在一定的误差。根据车辆的运动特性引入机动目标的当前统计模型,采用扩展Kalman滤波对坐标粗估计值存在的误差进行修正,以定位速度与精度为评价指标对定位方法进行了验证。验证结果表明:无线传感网络节点可大量布设的特点提高了定位可靠性;量子粒子群中引入变区间使定位速度提高了39.13%;Kalman误差修正使得定位精度提高了56.48%。可见,本文方法可以有效提高运动车辆定位速度与准确性。Abstract: To improve the location reliability and accuracy, moving vehicle location system based on traffic wireless sensor network was studied. Based on the law that vehicle location changed along with its speed, a variable interval quantum particle swarm optimization algorithm was proposed, by which the measured vehicle location parameters could be used for the rough estimation of vehicle coordinates. For the noise interferences and signal delay, the rough estimated values of vehicle coordinates were always prone to error. The current statistical model was introduced into the algorithm under the motion constraints of vehicle, and the extended Kalman filter was used to eliminate the location errors. The proposed method was tested by the evaluation indexes of speed and accuracy. Tested result indicates that the location reliability is improved for that the enormous sum nodes of wireless sensor network can be disposed. The variable interval introduced into the quantum particle swarm optimization increases the convergence speed by 39.13%. The Kalman filter corrects the errors, and improves location precision by 56.48%. The proposed algorithm demonstrates the superiority in terms of location reliability and accuracy.
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