基于无线热释电传感器网络的人体目标跟踪系统的研究
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摘要
人体跟踪指的是在特定的跟踪系统中,根据探测输出信号,从而确定人体是否存在以及当前所处位置的过程。人体跟踪技术广泛应用于各种环境监测系统、安全系统、智能辅助系统中,其往往涉及人身安全、紧急救援或贵重物品的保护,等等,具有重大的科学意义和市场前景。论文依托国家自然科学基金项目,完整地构建了基于无线热释电传感器网络的人体目标跟踪系统。系统硬件采用了专门设计的辅以菲涅尔阵列的热释电传感器网络,实现人体目标运动检测；系统软件包含了多目标跟踪算法和系统校准算法两个部分。
在本文系统的设计中,面临着如下的问题和挑战：(1)热释电传感器硬件结构设计,包括如何提高感测解析度、优化视觉域调节,和设计菲涅尔透镜阵列；(2)传感器节点的CPU容量限制以及严重受限的的网络通信带宽；(3)针对多人跟踪应用设计优化的、低时空复杂度的、易于实际系统实施的分布式推理与学习算法；(4)建立准确的、可拓展的分布式热释电传感器网络仿真系统平台,并构建实际场景中的系统测试平台。针对这些问题,本文所做的的主要工作为：
首先,通过对热释电传感器工作原理的研究,设计适用于人体目标跟踪的热释电传感器。在此基础上,构建热释电传感器节点并根据节点特性提出目标定位方法以及传感器网络的整体架构。
其次,提出了目标跟踪问题的数学模型,并根据此数学模型提出目标跟踪的数学表达公式,为跟踪算法指明研究方向；利用信号处理技术以及热释电传感器网络的结构和特点,提取目标检测结果,获得事件序列信号,利用网络节点的几何参数,为运动目标提供初步的定位结果。
第三,基于系统硬件特性,设计分布式多目标跟踪算法。其算法的设计主要包括两个部分,即数据-目标关联和运动滤波。在本文系统中,不同的目标极有可能在传感器的视角域内相互遮挡,从而造成对传感数据的解释模糊,使系统精确度下降。为了解决这一问题,数据-目标关联必不可少。而运动滤波对不同的目标的检测数据进行处理,消除检测数据中的噪声,并预测目标的下一步位置坐标,实现目标跟踪。本系统借鉴将目标假设为刚性的数据关联算法JPDA和对系统资源消耗较小的卡尔曼滤波算法对目标原始数据进行处理。此外,针对分布式的网络环境,将JPDA算法与分布式卡尔曼滤波结合,构建完整的分布式目标跟踪算法方案,延长整个网络的生存周期。
最后,根据传感器节点的检测特性,设计系统校准方案。人体目标跟踪系统在实际环境中运行时,往往受到环境噪声和硬件参数误差的影响,导致实际跟踪结果与仿真结果误差较大,因此需要校准算法进行调节。为了保证校准算法的简便,规定在校准时,校准目标的轨迹只需要保证为直线即可,不需要附加任何硬件设备。算法利用KL距离构建目标方程,并通过信息投影技术进行方程优化,从而获得新的网络节点参数。此外,在构建优化算法时,利用置信传播架构改造算法,获得分布式校准方案,为整个网络系统服务,以实现系统通过参数校准提高跟踪结果精度的目的。
基于上述研究工作,本文的创新工作主要体现在以下三个方面：
(1)根据系统硬件开发的基于分布式卡尔曼滤波器的多目标人体跟踪算法。本系统采用JPDA算法与分布式卡尔曼滤波器相结合的跟踪算法进行人体跟踪,算法的融合方案仅仅涉及节点测量协方差和测量数据的计算,保证计算复杂度较低,节点间通信量维持在较低的水平。
(2)基于信息投影技术优化校准目标方程。根据数学推导,确定传感器节点方向角度与节点位置参数在优化时分别对于m投影和e投影收敛,因此系统设计两级迭代优化算法来确定新的节点参数。基于信息投影的优化算法充分利用了多项式分布的特性,以及节点的硬件架构,同时保证优化过程的收敛性。
(3)利用高斯置信传播架构,设计分布式校准算法。利用置信传播架构是分布式学习中的有效方法,但是其本身计算较为复杂,不适用于廉价的无线传感器网络。因此,利用特殊的高斯置信传播架构,配合节点本地校准算法,设计分布式校准算法成为有力的选择。高斯置信传播能同时降低计算复杂度和通信消耗,同时保证在网络中实现分布式校准算法,完成整体系统。
Human tracking is the procedure which makes sure whether there exists human body and obtains positions of concerned human targets according to the detection signals in tracking system. The human tracking technology can be widely used in kinds of environment surveillance, security and intelligent assistant systems which relate to person security, emergency rescue, protection of valuables and so on. Therefore, the technology has wonderful science significance and market prospective. This thesis is supported by the National Natural Science Foundation and constructs the whole human tracking system based on wireless pyroelectric sensor network. The system hardware uses the pyroelectric sensor network with the Fresenel lens array to realize human movement detection and the system software includes multi-target tracking algorithm and system calibration algorithm.
The design of the human system has to deal with following problems and challenges:(1) The design of the structure of pyroelectric sensor hardware, which contains how to improve the detection resolution and optimize the field of view (FOV) of sensor, and the Fresenel lens array.(2) The CPU capacity of sensor node is limited and the network communication bandwidth is severely restricted.(3) The realization of distributed learning algorithm which is designed for multi-human tracking application and is easy for implement of real system and has low complexity.(4) The establishment of exact and extensible simulation platform of distributed pyroelectric sensor network and system testing platform in real environment. The main work of this thesis in allusion to these problems is:
First, the thesis designs the pyroecectric sensor which is suitable for human targets tracking after the research of the pyroecectric theory. Then, the thesis constructs the sensor node and gives the target positioning method and network structure in term of the characters of pyroecectric sensor.
Second, the thesis gives the mathematic model of target tracking problem and the corresponding mathematic formula for tracking algorithm. At the same time, the tracking system uses the signal processing technology and the structure characters of pyroelectric sensor network to distill the target detecting results for obtaining event sequence signal which provides the original positioning results with the assistance of geometric parameters of network nodes.
Third, the distributed multi-target tracking algorithm is based on the system hardware. The procedure of algorithm design contains two parts, namely the data-to-target association and motion filter. For this system, different targets may blot out others in the FOV of sensors, and this situation causes the blur explanation of sensing data and decreases the precision of tracking system. In order to solve this weakness, the data-to-target association is necessary. The motion filter can deal with the detecting data of different targets, eliminate the noise and forecast the next positions of targets to realize target tracking. The system uses the data-to-target association algorithm JPDA which supposes the target as rigidity one and the Kalman filtering consuming less resource to cope with original target data. Moreover, the paper combines the JPDA with distributed Kalman filter for the distributed environment to construct the whole distributed tracking algorithm which prolongs the life cycle of the whole network.
Finally, the thesis designs the system calibration scheme in terms of detecting characters of sensor node. When the human target tracking system works in the real environment, it is affected by the environmental noise and hardware parameters'error. It leads to the increase of tracking error in comparison with the simulation situation. So, the calibration algorithm is needed. This scheme requires the calibrating target moves through the line trajectory and does not need any additional equipment for convenience. The calibration utilizes KL divergence to build objective function and information projection to optimize the function to get new node parameters. In addition, the belief propagation is used to obtain distributed calibration scheme which serves the network and improves the tracking results by optimizing node parameters.
Based on the above research work, the novelty of this thesis includes three points:
(1) The multi-target human tracking algorithm based on the distributed Kalman filter is developed for the system hardware. In this system, the algorithm is the combination of JPDA and distributed Kalman filter for the lower computation and communication cost and this is because it only use the measure covariance and measure data.
(2) The calibration objective function is optimized by the information projection technology. According to the mathematic analysis, the node orientation and node position are convergent for m-projection and e-projection respectively, therefore two-step iterative optimization algorithm is developed. This algorithm takes full advantage of multi-nominal distribution and node hardware structure, and guarantees the convergence during the whole procedure.
(3) The distributed calibration algorithm is designed by using Gaussian belief propagation. The belief propagation is an effective method of distributed learning, but it is complicated and thus not very suitable for low-cost wireless sensor networks. Therefore the Gaussian belief propagation is the better choice and it is used to build distributed calibration scheme with the help of local calibration. The Gaussian belief propagation can reduce the cost of computation and communication, and meanwhile realizes the distributed calibration in the network.
在本文系统的设计中,面临着如下的问题和挑战：(1)热释电传感器硬件结构设计,包括如何提高感测解析度、优化视觉域调节,和设计菲涅尔透镜阵列；(2)传感器节点的CPU容量限制以及严重受限的的网络通信带宽；(3)针对多人跟踪应用设计优化的、低时空复杂度的、易于实际系统实施的分布式推理与学习算法；(4)建立准确的、可拓展的分布式热释电传感器网络仿真系统平台,并构建实际场景中的系统测试平台。针对这些问题,本文所做的的主要工作为：
首先,通过对热释电传感器工作原理的研究,设计适用于人体目标跟踪的热释电传感器。在此基础上,构建热释电传感器节点并根据节点特性提出目标定位方法以及传感器网络的整体架构。
其次,提出了目标跟踪问题的数学模型,并根据此数学模型提出目标跟踪的数学表达公式,为跟踪算法指明研究方向；利用信号处理技术以及热释电传感器网络的结构和特点,提取目标检测结果,获得事件序列信号,利用网络节点的几何参数,为运动目标提供初步的定位结果。
第三,基于系统硬件特性,设计分布式多目标跟踪算法。其算法的设计主要包括两个部分,即数据-目标关联和运动滤波。在本文系统中,不同的目标极有可能在传感器的视角域内相互遮挡,从而造成对传感数据的解释模糊,使系统精确度下降。为了解决这一问题,数据-目标关联必不可少。而运动滤波对不同的目标的检测数据进行处理,消除检测数据中的噪声,并预测目标的下一步位置坐标,实现目标跟踪。本系统借鉴将目标假设为刚性的数据关联算法JPDA和对系统资源消耗较小的卡尔曼滤波算法对目标原始数据进行处理。此外,针对分布式的网络环境,将JPDA算法与分布式卡尔曼滤波结合,构建完整的分布式目标跟踪算法方案,延长整个网络的生存周期。
最后,根据传感器节点的检测特性,设计系统校准方案。人体目标跟踪系统在实际环境中运行时,往往受到环境噪声和硬件参数误差的影响,导致实际跟踪结果与仿真结果误差较大,因此需要校准算法进行调节。为了保证校准算法的简便,规定在校准时,校准目标的轨迹只需要保证为直线即可,不需要附加任何硬件设备。算法利用KL距离构建目标方程,并通过信息投影技术进行方程优化,从而获得新的网络节点参数。此外,在构建优化算法时,利用置信传播架构改造算法,获得分布式校准方案,为整个网络系统服务,以实现系统通过参数校准提高跟踪结果精度的目的。
基于上述研究工作,本文的创新工作主要体现在以下三个方面：
(1)根据系统硬件开发的基于分布式卡尔曼滤波器的多目标人体跟踪算法。本系统采用JPDA算法与分布式卡尔曼滤波器相结合的跟踪算法进行人体跟踪,算法的融合方案仅仅涉及节点测量协方差和测量数据的计算,保证计算复杂度较低,节点间通信量维持在较低的水平。
(2)基于信息投影技术优化校准目标方程。根据数学推导,确定传感器节点方向角度与节点位置参数在优化时分别对于m投影和e投影收敛,因此系统设计两级迭代优化算法来确定新的节点参数。基于信息投影的优化算法充分利用了多项式分布的特性,以及节点的硬件架构,同时保证优化过程的收敛性。
(3)利用高斯置信传播架构,设计分布式校准算法。利用置信传播架构是分布式学习中的有效方法,但是其本身计算较为复杂,不适用于廉价的无线传感器网络。因此,利用特殊的高斯置信传播架构,配合节点本地校准算法,设计分布式校准算法成为有力的选择。高斯置信传播能同时降低计算复杂度和通信消耗,同时保证在网络中实现分布式校准算法,完成整体系统。
Human tracking is the procedure which makes sure whether there exists human body and obtains positions of concerned human targets according to the detection signals in tracking system. The human tracking technology can be widely used in kinds of environment surveillance, security and intelligent assistant systems which relate to person security, emergency rescue, protection of valuables and so on. Therefore, the technology has wonderful science significance and market prospective. This thesis is supported by the National Natural Science Foundation and constructs the whole human tracking system based on wireless pyroelectric sensor network. The system hardware uses the pyroelectric sensor network with the Fresenel lens array to realize human movement detection and the system software includes multi-target tracking algorithm and system calibration algorithm.
The design of the human system has to deal with following problems and challenges:(1) The design of the structure of pyroelectric sensor hardware, which contains how to improve the detection resolution and optimize the field of view (FOV) of sensor, and the Fresenel lens array.(2) The CPU capacity of sensor node is limited and the network communication bandwidth is severely restricted.(3) The realization of distributed learning algorithm which is designed for multi-human tracking application and is easy for implement of real system and has low complexity.(4) The establishment of exact and extensible simulation platform of distributed pyroelectric sensor network and system testing platform in real environment. The main work of this thesis in allusion to these problems is:
First, the thesis designs the pyroecectric sensor which is suitable for human targets tracking after the research of the pyroecectric theory. Then, the thesis constructs the sensor node and gives the target positioning method and network structure in term of the characters of pyroecectric sensor.
Second, the thesis gives the mathematic model of target tracking problem and the corresponding mathematic formula for tracking algorithm. At the same time, the tracking system uses the signal processing technology and the structure characters of pyroelectric sensor network to distill the target detecting results for obtaining event sequence signal which provides the original positioning results with the assistance of geometric parameters of network nodes.
Third, the distributed multi-target tracking algorithm is based on the system hardware. The procedure of algorithm design contains two parts, namely the data-to-target association and motion filter. For this system, different targets may blot out others in the FOV of sensors, and this situation causes the blur explanation of sensing data and decreases the precision of tracking system. In order to solve this weakness, the data-to-target association is necessary. The motion filter can deal with the detecting data of different targets, eliminate the noise and forecast the next positions of targets to realize target tracking. The system uses the data-to-target association algorithm JPDA which supposes the target as rigidity one and the Kalman filtering consuming less resource to cope with original target data. Moreover, the paper combines the JPDA with distributed Kalman filter for the distributed environment to construct the whole distributed tracking algorithm which prolongs the life cycle of the whole network.
Finally, the thesis designs the system calibration scheme in terms of detecting characters of sensor node. When the human target tracking system works in the real environment, it is affected by the environmental noise and hardware parameters'error. It leads to the increase of tracking error in comparison with the simulation situation. So, the calibration algorithm is needed. This scheme requires the calibrating target moves through the line trajectory and does not need any additional equipment for convenience. The calibration utilizes KL divergence to build objective function and information projection to optimize the function to get new node parameters. In addition, the belief propagation is used to obtain distributed calibration scheme which serves the network and improves the tracking results by optimizing node parameters.
Based on the above research work, the novelty of this thesis includes three points:
(1) The multi-target human tracking algorithm based on the distributed Kalman filter is developed for the system hardware. In this system, the algorithm is the combination of JPDA and distributed Kalman filter for the lower computation and communication cost and this is because it only use the measure covariance and measure data.
(2) The calibration objective function is optimized by the information projection technology. According to the mathematic analysis, the node orientation and node position are convergent for m-projection and e-projection respectively, therefore two-step iterative optimization algorithm is developed. This algorithm takes full advantage of multi-nominal distribution and node hardware structure, and guarantees the convergence during the whole procedure.
(3) The distributed calibration algorithm is designed by using Gaussian belief propagation. The belief propagation is an effective method of distributed learning, but it is complicated and thus not very suitable for low-cost wireless sensor networks. Therefore the Gaussian belief propagation is the better choice and it is used to build distributed calibration scheme with the help of local calibration. The Gaussian belief propagation can reduce the cost of computation and communication, and meanwhile realizes the distributed calibration in the network.
引文
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