智能传感器侦察网络中的目标跟踪算法研究
摘要
粒子滤波算法是一种基于非参数的蒙特卡洛方法的近似贝叶斯滤波算法,具有算法简洁,使用灵活,容易实现,同时具有较好的并行结构等特点,适用于传统方法很难解决的非线性目标跟踪问题。其跟踪精度可以逼近最优估计。
本文结合实际应用需求,针对智能传感侦察网络中的机动目标跟踪问题开展研究工作。主要研究内容以粒子滤波算法为核心,分别从分布式算法设计与实现,纯方位角机动目标跟踪滤波发散问题的解决以及传感器网络节点能力受限条件下的多目标跟踪等三个方面的内容进行深入的研究,主要的研究内容和成果如下:
对目前机动目标跟踪的机动目标模型和跟踪算法做全面、系统的讨论。首先从机动模型入手,对适用于地面目标的模型做详尽的介绍。其次,对目前常用的目标跟踪算法进行归纳和总结,深入讨论其原理与适用范围。并在此基础上从贝叶斯重要性采样原理入手,详尽探讨粒子滤波的原理,以及基本的序列粒子滤波算法。接着对粒子滤波中的粒子退化问题现象和问题原因进行详细阐述,并介绍部分粒子滤波的改进算法。最后,通过仿真,比较扩展卡尔曼滤波算法和粒子滤波算法,证实粒子滤波能够很好的解决机动目标跟踪中的非线性问题,对目标的机动性有很好的适应能力。
首次提出一种分布式Unscented粒子滤波目标跟踪算法。针对智能无线传感侦察网络,存在着传感节点能量受限,通信受限的特点。首次将UT变换引入分布式粒子滤波算法,以解决现有的分布式粒子滤波算法网络通信负荷重,节点能量消耗高的缺点。UT变换为粒子滤波算法提供更为准确的建议分布,从而提高粒子的使用效率,降低粒子数量,并且UT变换能够降低粒子退化现象的发生。仿真表明,分布式Unscented粒子滤波能够有效的减少网络的通信流量,提高跟踪精度。
提出一种神经元网络改进的Unscented粒子滤波算法。以解决纯方位角目标跟踪中Unscented粒子滤波算法存在的精度下降,滤波发散的问题。首先,通过对Unscented粒子滤波原理的阐述,定性分析纯方位角目标跟踪中滤波发散的原因。在此基础上,将神经元网络方法引入到机动目标跟踪中。神经元网络的学习,记忆能力能够有效地改进重要性分布,提高Unscented粒子滤波对信噪比变化的适应能力。仿真表明,该算法只需增加少量的计算时间,就可以提高目标跟踪的精度,减少滤波发散几率。
首次对智能传感器侦察网络中多目标跟踪问题进行深入系统的研究,并在此基础上提出基于预测的多目标跟踪算法。通过研究无线传感器网络中的多目标跟踪的特有问题,第一次提出多目标跟踪问题的局部性特征和量测信息的信号混叠特性,即多目标跟踪问题仅发生在传感器网络监测的局部区域,并且发生时,传感节点的探测信号是多个目标信号混叠而成。在此基础上,提出一种新的基于预测的多目标粒子滤波算法。该算法充分利用滤波跟踪的历史信息,结合粒子滤波的预测结果,对传感节点探测信号进行分离,从而是实现多目标跟踪。仿真实验证明,基于预测的多目标跟踪算法能够用较小的计算代价,解决多目标跟踪问题,满足实用系统需求。
Particle filter method achieves recursive Bayesian filter via Monte Carlo simulation.It is simple,flexible and easy to be implemented,and has parallel structure.This method is suitable for nonlinear target tracking which is hard to solve by traditional algorithm.Its precision can approach optimal estimation.
Unattended sensor technology used in Smart Sensor Network for remote battlefield surveillance applications requires state-of-the-art algorithms to address the unprecedented challenges faced in target tracking.These sensors are not able to distinguish individual targets,decide how many distinct targets in the range.This dissertation studies on particle filter and its implementation in target tracking in Smart Sensor Network.The studies focus on the distributed tracking algorithm,bearing-only target tracking,and the multi-target tracking in Smart Sensor Network.The details and results of studies involved in this paper are follows:
(1) The maneuvering models and the measurement models for target tracking are discussed deeply and systemic.First,the maneuvering models suitable for ground target tracking are presented.Several tracking algorithms,including Kalman Filter and grid-based filter,are summarized,and their principle and applications are also discussed.Based on the analysis of the Bayesian Importance Sampling,sequential important sampling is presented as the basic of particle filter.The degeneracy problem and its reason are discussed further,and some improved algorithms to solve this problem are introduced as the evolution of particle filter.The simulation shows particle filter can solve the nonlinear problem in maneuvering target tracking, compared with extended Kalman filter which is widely used.
(2) Considering the limitation of power and communication of the node in Smart Sensor Network,a novel distributed target tracking algorithm,distributed unscented particle filter,is proposed.Unscented transformation is used to generate the proposed distribution in parallel with more accuracy in the algorithm.It uses a deterministic sampling approach to get the estimation of the nonlinear function(accurate to the second order of the Taylor series expansion) Due to the unscented transformation, distributed unscented particle filter can enhance the efficiency of the particles,thus the amount of the particle can be small.Also unscented transformation can avoid the degeneracy problem in particle filter.Simulation shows distributed unscented particle filter can get more accurate tracking result with fewer particles and less communication,which in turn reduces the power consumption.
(3) Considering the highly nonlinear tracking problem,bearing-only target tracking,a neural network aided unscented particle filter is proposed.First,the reason of missing target in unscented particle filter is analyzed in bearing-only target tracking, which is the SNR is changing quickly when the direction of angle is detected by the node.Based on the result above,neural network is introduced into the unscented particle filter.Neural network is able to learn and remember the tracking result.This helps unscented particle filter to get a better proposed distribution to match the SNR value.As demonstrated in our simulation results,the neural network aided unscented particle filter can improve the tracking result and give lower RMSE than usual unscented particle filter with reasonable time cost.
(4) Multiple targets tracking in Smart Sensor Network are deeply researched.A multi-target particle filter tracking algorithm based on prediction is proposed.The analysis on the multi-target tracking in Smart Sensor Network shows it is different with the traditional tracking.First,Multi-targets tracking only happens in local area while not in the whole area of the network.Secondly,the measurement received by the sensor node is mixed with the signals from multiple individual targets,and it's hard to be separated.Thus,the common multi-target tracking algorithms can not used in this situation.The novel multi-target tracking method presented in this paper uses the predicting position,provided by particle filter,to separate the signal from the mixed measurement.Simulation experiments show it can solve the multi-target tracking problem,with only a few calculations added compared with single target tracking.
本文结合实际应用需求,针对智能传感侦察网络中的机动目标跟踪问题开展研究工作。主要研究内容以粒子滤波算法为核心,分别从分布式算法设计与实现,纯方位角机动目标跟踪滤波发散问题的解决以及传感器网络节点能力受限条件下的多目标跟踪等三个方面的内容进行深入的研究,主要的研究内容和成果如下:
对目前机动目标跟踪的机动目标模型和跟踪算法做全面、系统的讨论。首先从机动模型入手,对适用于地面目标的模型做详尽的介绍。其次,对目前常用的目标跟踪算法进行归纳和总结,深入讨论其原理与适用范围。并在此基础上从贝叶斯重要性采样原理入手,详尽探讨粒子滤波的原理,以及基本的序列粒子滤波算法。接着对粒子滤波中的粒子退化问题现象和问题原因进行详细阐述,并介绍部分粒子滤波的改进算法。最后,通过仿真,比较扩展卡尔曼滤波算法和粒子滤波算法,证实粒子滤波能够很好的解决机动目标跟踪中的非线性问题,对目标的机动性有很好的适应能力。
首次提出一种分布式Unscented粒子滤波目标跟踪算法。针对智能无线传感侦察网络,存在着传感节点能量受限,通信受限的特点。首次将UT变换引入分布式粒子滤波算法,以解决现有的分布式粒子滤波算法网络通信负荷重,节点能量消耗高的缺点。UT变换为粒子滤波算法提供更为准确的建议分布,从而提高粒子的使用效率,降低粒子数量,并且UT变换能够降低粒子退化现象的发生。仿真表明,分布式Unscented粒子滤波能够有效的减少网络的通信流量,提高跟踪精度。
提出一种神经元网络改进的Unscented粒子滤波算法。以解决纯方位角目标跟踪中Unscented粒子滤波算法存在的精度下降,滤波发散的问题。首先,通过对Unscented粒子滤波原理的阐述,定性分析纯方位角目标跟踪中滤波发散的原因。在此基础上,将神经元网络方法引入到机动目标跟踪中。神经元网络的学习,记忆能力能够有效地改进重要性分布,提高Unscented粒子滤波对信噪比变化的适应能力。仿真表明,该算法只需增加少量的计算时间,就可以提高目标跟踪的精度,减少滤波发散几率。
首次对智能传感器侦察网络中多目标跟踪问题进行深入系统的研究,并在此基础上提出基于预测的多目标跟踪算法。通过研究无线传感器网络中的多目标跟踪的特有问题,第一次提出多目标跟踪问题的局部性特征和量测信息的信号混叠特性,即多目标跟踪问题仅发生在传感器网络监测的局部区域,并且发生时,传感节点的探测信号是多个目标信号混叠而成。在此基础上,提出一种新的基于预测的多目标粒子滤波算法。该算法充分利用滤波跟踪的历史信息,结合粒子滤波的预测结果,对传感节点探测信号进行分离,从而是实现多目标跟踪。仿真实验证明,基于预测的多目标跟踪算法能够用较小的计算代价,解决多目标跟踪问题,满足实用系统需求。
Particle filter method achieves recursive Bayesian filter via Monte Carlo simulation.It is simple,flexible and easy to be implemented,and has parallel structure.This method is suitable for nonlinear target tracking which is hard to solve by traditional algorithm.Its precision can approach optimal estimation.
Unattended sensor technology used in Smart Sensor Network for remote battlefield surveillance applications requires state-of-the-art algorithms to address the unprecedented challenges faced in target tracking.These sensors are not able to distinguish individual targets,decide how many distinct targets in the range.This dissertation studies on particle filter and its implementation in target tracking in Smart Sensor Network.The studies focus on the distributed tracking algorithm,bearing-only target tracking,and the multi-target tracking in Smart Sensor Network.The details and results of studies involved in this paper are follows:
(1) The maneuvering models and the measurement models for target tracking are discussed deeply and systemic.First,the maneuvering models suitable for ground target tracking are presented.Several tracking algorithms,including Kalman Filter and grid-based filter,are summarized,and their principle and applications are also discussed.Based on the analysis of the Bayesian Importance Sampling,sequential important sampling is presented as the basic of particle filter.The degeneracy problem and its reason are discussed further,and some improved algorithms to solve this problem are introduced as the evolution of particle filter.The simulation shows particle filter can solve the nonlinear problem in maneuvering target tracking, compared with extended Kalman filter which is widely used.
(2) Considering the limitation of power and communication of the node in Smart Sensor Network,a novel distributed target tracking algorithm,distributed unscented particle filter,is proposed.Unscented transformation is used to generate the proposed distribution in parallel with more accuracy in the algorithm.It uses a deterministic sampling approach to get the estimation of the nonlinear function(accurate to the second order of the Taylor series expansion) Due to the unscented transformation, distributed unscented particle filter can enhance the efficiency of the particles,thus the amount of the particle can be small.Also unscented transformation can avoid the degeneracy problem in particle filter.Simulation shows distributed unscented particle filter can get more accurate tracking result with fewer particles and less communication,which in turn reduces the power consumption.
(3) Considering the highly nonlinear tracking problem,bearing-only target tracking,a neural network aided unscented particle filter is proposed.First,the reason of missing target in unscented particle filter is analyzed in bearing-only target tracking, which is the SNR is changing quickly when the direction of angle is detected by the node.Based on the result above,neural network is introduced into the unscented particle filter.Neural network is able to learn and remember the tracking result.This helps unscented particle filter to get a better proposed distribution to match the SNR value.As demonstrated in our simulation results,the neural network aided unscented particle filter can improve the tracking result and give lower RMSE than usual unscented particle filter with reasonable time cost.
(4) Multiple targets tracking in Smart Sensor Network are deeply researched.A multi-target particle filter tracking algorithm based on prediction is proposed.The analysis on the multi-target tracking in Smart Sensor Network shows it is different with the traditional tracking.First,Multi-targets tracking only happens in local area while not in the whole area of the network.Secondly,the measurement received by the sensor node is mixed with the signals from multiple individual targets,and it's hard to be separated.Thus,the common multi-target tracking algorithms can not used in this situation.The novel multi-target tracking method presented in this paper uses the predicting position,provided by particle filter,to separate the signal from the mixed measurement.Simulation experiments show it can solve the multi-target tracking problem,with only a few calculations added compared with single target tracking.
引文
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