多传感器目标跟踪数据融合关键技术研究
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摘要
随着目标跟踪和信息融合技术的高速发展,人们开始探索利用多个传感器的测量数据对跟踪目标的运动状态进行估计,以实现最大限度提取有用信息进行目标跟踪的目的。如何将多个传感器的数据进行有机融合,获得单一传感器无法达到的跟踪性能,已经成为目标跟踪领域中多传感器数据融合技术研究和关注的重点。
本文针对多传感器目标跟踪数据融合技术中的一些关键问题进行认真研究和深入探讨,为数据融合技术应用到目标跟踪问题提供了重要参考。在分析总结前人的研究和应用基础上,主要进行的工作如下：
1、将集合卡尔曼滤波技术引入到多传感器目标跟踪领域,验证EnKF算法的可行性和有效性；针对多个EnKF滤波器拥有共同的过程噪声以及观测集合导致滤波后航迹相关的问题,提出了基于EnKF的协方差加权航迹融合算法；来源于不同传感器的测量数据会产生多个可能初始状态,针对初始状态选择会对跟踪性能影响问题,提出了基于分块EnKF的非线性目标跟踪算法,采用分块思想生成初始集合,在分块间再进行协方差加权融合；将集合卡尔曼滤波算法和粒子滤波算法有效的结合在一起,提出了基于改进EnPF的非线性目标跟踪算法,采用两个独立集合,先利用一个独立集合进行集合卡尔曼滤波,然后利用集合卡尔曼滤波的分析集合和另一个独立集合组成粒子滤波的参考分布,最后进行粒子滤波的方式结合了集合卡尔曼滤波算法和粒子滤波算法的优点,解决了EnKF滤波算法不适合非高斯噪声系统和PF滤波算法计算量大不适用于实时跟踪的问题。
2、在目标跟踪系统中,传感器观测噪声往往具有非高斯特性。针对传感器对目标进行跟踪时观测噪声非高斯问题,提出了一种基于关系矩阵的主、被动传感器量测统计融合算法。算法采用方差加权距离解决传感器量测噪声非高斯问题,运用传感器综合融合度构建关系矩阵,并且在门限附近采用椭圆模糊处理技术。
3、传感器的测量误差是由固定误差和随机误差组成。固定测量误差一般由传感器本身特性决定,而随机误差会受到传感器与目标之间的距离、自然抑或是人为干扰等随机因素影响。在不考虑干扰的情况下,传感器与目标间的距离成为影响随机测量误差的主要原因。针对目标和传感器间的距离参数对传感器随机测量误差带来影响的问题,提出了一种基于模糊距离阈值的主被动传感器变权重量测融合算法,采用指数函数和模糊处理技术,利用先验信息实时改变主、被动传感器在量测融合过程中所占的权重,提高系统的跟踪性能。
4、多传感器目标跟踪系统中信息增量最大化只是传感器资源对运动目标进行分配的必要条件而非充分条件。本文综合考虑影响传感器资源对目标分配的因素,结合跟踪系统中信息增量,提出了一种信息增量和目标权重相结合的方法实现多目标跟踪系统中传感器资源对目标分配,并且给出了影响目标权重的距离和速度两个特征向量的具体量化方法。
With the rapid development of target tracking and information fusion technology, people begin to integrated utilize the measurements from multi-sensor to estimate the position and kinetics parameters, and useful information is extract to the greatest extent to track the moving object. How to effective fuse the measurements from multi-sensor and obtain better tracking performance compared to the single sensor are an important research in multi-sensor target tracking domain.
Some key technologies researches on multi-sensor data fusion for target tracking are investigated in this paper, and it can provide a reference for the data fusion application to the target tracking domain. Based on the work of predecessors, specific tasks in this paper are as follows:
1. Ensemble Kalman filter is introduced to multi-sensor target tracking system where feasibility and validity of ensemble Kalman filter are verified. For the common process noise and the same measurements to all the EnKF, the cross-covariance can not be omitted simply, a new track-to-track fusion algorithm based on ensemble Kalman filter using covariance matrix weighting is proposed. Considering the affection of target tracking performance from correlated tracks and different initial states, a new target tracking algorithm based on block ensemble Kalman filter is proposed, where initial ensemble is produced by block method and covariance matrix weighting is proposed for all the blocks in the target tracking process. An improved ensemble particle filter (EnPF) algorithm combining the advantages of EnKF and PF is proposed. Two separate ensembles are adopted, one ensemble is handled by EnKF first, then the analysis ensemble produced by EnKF and another ensemble are integrated to generate proposal distribution of PF; finally PF is executed based on this proposal distribution. And the improved EnPF combines the advantages of EnKF and PF and solves the deficiency of Gaussian condition of EnKF and high computational cost of PF.
2. A new statistical data fusion algorithm based on relation matrix is discussed when the measurements from sensors do not follow Gaussian distribution, the algorithm uses integrated fusion degree of each sensor to build the relation matrix where eclipse curve fuzzy technology is adopted nearby threshold.
3. Measurement errors of sensors are composed of constant errors and random errors. The constant errors are decided by sensors themselves, and the random errors are affected by other factors, such as the target distance, disturbance of weather or man-made. Without consideration of disturbance, random errors caused by the distance are the major causes. A variable-weight data fusion algorithm only considering random errors caused by the distance is discussed, the algorithm uses an exponential function to compute variable weight coefficient of active radar.
4. To the problem of tracking multi-target, information gain maximization is not sufficient condition but only necessary condition when it is used to distribute the sensor resources, a new method of sensor assignment based on combing target priority with information gain is proposed, and the quantitative method based on distance and velocity affecting the target priority is discussed.
本文针对多传感器目标跟踪数据融合技术中的一些关键问题进行认真研究和深入探讨,为数据融合技术应用到目标跟踪问题提供了重要参考。在分析总结前人的研究和应用基础上,主要进行的工作如下：
1、将集合卡尔曼滤波技术引入到多传感器目标跟踪领域,验证EnKF算法的可行性和有效性；针对多个EnKF滤波器拥有共同的过程噪声以及观测集合导致滤波后航迹相关的问题,提出了基于EnKF的协方差加权航迹融合算法；来源于不同传感器的测量数据会产生多个可能初始状态,针对初始状态选择会对跟踪性能影响问题,提出了基于分块EnKF的非线性目标跟踪算法,采用分块思想生成初始集合,在分块间再进行协方差加权融合；将集合卡尔曼滤波算法和粒子滤波算法有效的结合在一起,提出了基于改进EnPF的非线性目标跟踪算法,采用两个独立集合,先利用一个独立集合进行集合卡尔曼滤波,然后利用集合卡尔曼滤波的分析集合和另一个独立集合组成粒子滤波的参考分布,最后进行粒子滤波的方式结合了集合卡尔曼滤波算法和粒子滤波算法的优点,解决了EnKF滤波算法不适合非高斯噪声系统和PF滤波算法计算量大不适用于实时跟踪的问题。
2、在目标跟踪系统中,传感器观测噪声往往具有非高斯特性。针对传感器对目标进行跟踪时观测噪声非高斯问题,提出了一种基于关系矩阵的主、被动传感器量测统计融合算法。算法采用方差加权距离解决传感器量测噪声非高斯问题,运用传感器综合融合度构建关系矩阵,并且在门限附近采用椭圆模糊处理技术。
3、传感器的测量误差是由固定误差和随机误差组成。固定测量误差一般由传感器本身特性决定,而随机误差会受到传感器与目标之间的距离、自然抑或是人为干扰等随机因素影响。在不考虑干扰的情况下,传感器与目标间的距离成为影响随机测量误差的主要原因。针对目标和传感器间的距离参数对传感器随机测量误差带来影响的问题,提出了一种基于模糊距离阈值的主被动传感器变权重量测融合算法,采用指数函数和模糊处理技术,利用先验信息实时改变主、被动传感器在量测融合过程中所占的权重,提高系统的跟踪性能。
4、多传感器目标跟踪系统中信息增量最大化只是传感器资源对运动目标进行分配的必要条件而非充分条件。本文综合考虑影响传感器资源对目标分配的因素,结合跟踪系统中信息增量,提出了一种信息增量和目标权重相结合的方法实现多目标跟踪系统中传感器资源对目标分配,并且给出了影响目标权重的距离和速度两个特征向量的具体量化方法。
With the rapid development of target tracking and information fusion technology, people begin to integrated utilize the measurements from multi-sensor to estimate the position and kinetics parameters, and useful information is extract to the greatest extent to track the moving object. How to effective fuse the measurements from multi-sensor and obtain better tracking performance compared to the single sensor are an important research in multi-sensor target tracking domain.
Some key technologies researches on multi-sensor data fusion for target tracking are investigated in this paper, and it can provide a reference for the data fusion application to the target tracking domain. Based on the work of predecessors, specific tasks in this paper are as follows:
1. Ensemble Kalman filter is introduced to multi-sensor target tracking system where feasibility and validity of ensemble Kalman filter are verified. For the common process noise and the same measurements to all the EnKF, the cross-covariance can not be omitted simply, a new track-to-track fusion algorithm based on ensemble Kalman filter using covariance matrix weighting is proposed. Considering the affection of target tracking performance from correlated tracks and different initial states, a new target tracking algorithm based on block ensemble Kalman filter is proposed, where initial ensemble is produced by block method and covariance matrix weighting is proposed for all the blocks in the target tracking process. An improved ensemble particle filter (EnPF) algorithm combining the advantages of EnKF and PF is proposed. Two separate ensembles are adopted, one ensemble is handled by EnKF first, then the analysis ensemble produced by EnKF and another ensemble are integrated to generate proposal distribution of PF; finally PF is executed based on this proposal distribution. And the improved EnPF combines the advantages of EnKF and PF and solves the deficiency of Gaussian condition of EnKF and high computational cost of PF.
2. A new statistical data fusion algorithm based on relation matrix is discussed when the measurements from sensors do not follow Gaussian distribution, the algorithm uses integrated fusion degree of each sensor to build the relation matrix where eclipse curve fuzzy technology is adopted nearby threshold.
3. Measurement errors of sensors are composed of constant errors and random errors. The constant errors are decided by sensors themselves, and the random errors are affected by other factors, such as the target distance, disturbance of weather or man-made. Without consideration of disturbance, random errors caused by the distance are the major causes. A variable-weight data fusion algorithm only considering random errors caused by the distance is discussed, the algorithm uses an exponential function to compute variable weight coefficient of active radar.
4. To the problem of tracking multi-target, information gain maximization is not sufficient condition but only necessary condition when it is used to distribute the sensor resources, a new method of sensor assignment based on combing target priority with information gain is proposed, and the quantitative method based on distance and velocity affecting the target priority is discussed.
引文
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