天基光学监视系统目标跟踪技术研究
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摘要
空间已成为维护国家安全和国家利益的战略“制高点”,对空间目标的快速捕获与高精度跟踪是利用空间、控制空间的前提和基础。天基光学监视系统结合了天基平台和光学传感器两者的优势,具有不受国界限制、覆盖范围广、测量精度高、隐蔽性强等优点,越来越受到各国的高度重视。本文针对天基光学监视系统中的目标跟踪问题进行了深入研究,重点就传感器像平面单目标跟踪、传感器像平面多目标跟踪、多平台多传感器像平面轨迹关联以及三维空间目标跟踪等关键技术进行了详细研究和讨论。论文主要工作如下:
第二章首先对目标的运动特性进行了分析,明确了目标运动模型中的主要影响因素;然后给出了基本的坐标系定义及转换关系,并根据天基光学传感器的成像过程,建立了传感器观测模型;接下来研究了天基光学传感器的视线测量误差,建立了视线测量误差分析模型;最后结合视线测量误差分析模型,进一步推导了目标的理论定位精度。第二章将为后续章节的研究提供支撑。
第三章研究了传感器像平面内的单目标轨迹起始与跟踪问题。首先,建立了像平面内的目标运动模型和测量模型;其次,在随机有限集理论框架下,通过将目标状态和量测数据建模为随机有限集,提出了一种不需要数据关联的基于随机有限集理论的像平面单目标跟踪算法;接着针对该算法得不到闭式解的问题,研究了一种基于序贯蒙特卡罗的算法实现技术和一种基于高斯混合的算法实现技术。仿真结果表明,该算法相对于经典单目标跟踪算法IPDA的跟踪性能更优。
第四章研究了传感器像平面内的多目标轨迹起始与跟踪问题。尝试将近年兴起的概率假设密度(Probability Hypothesis Density,PHD)滤波器引入到本问题中,并从PHD滤波器像平面跟踪的跟踪性能、计算效率以及算法实现等方面分别进行了研究。首先,利用信号幅度信息,提出了一种幅度参数辅助的像平面PHD滤波器,改善了在低信噪比环境下的多目标跟踪性能;其次,借鉴传统多目标跟踪方法中的加窗技术,提出了一种加窗的像平面PHD滤波器,提高了在强杂波环境中的计算效率;最后,结合sigma点滤波和高斯混合模型,提出了一种基于高斯混合sigma点滤波的PHD滤波器实现技术,从而将GM-PHD滤波器从线性、高斯条件扩展到非线性、非高斯条件。
第五章研究了多平台多感器之间的像平面轨迹关联问题。首先,将量测-量测关联问题中的GNN关联准则引入到倾角差假设检验算法中,提出了一种基于倾角差二维分配的像平面轨迹关联算法,仿真结果验证了该算法相对于倾角差假设检验算法的性能优势;其次,针对倾角差法存在受观测几何影响大、可能失效的缺陷,结合目标的三维运动特性,提出了一种基于极大似然的像平面轨迹关联算法,并进一步研究了与该算法密切相关的似然比表示、目标状态极大似然估计等问题。仿真结果表明,极大似然方法的像平面轨迹关联性能优于倾角差方法,但计算复杂度也更高。
第六章在第五章像平面轨迹关联的基础上,研究了多平台多传感器观测下的高精度三维空间目标跟踪问题。首先,基于第二章目标运动特性的分析结论,建立了一种同时考虑推力加速度变化和攻角变化的主动段动力学模型,扩展了动力学模型的应用范围;其次,将多模型概念和UKF滤波引入到本问题中,提出了一种基于动力学模型和J 2摄动模型的多模型UKF滤波的目标跟踪算法。仿真结果表明,该算法可以自主判断级间分离和关机,并且获得比单模型UKF滤波器、单模型EKF滤波器更优的主动段、中段跟踪性能。
The space has become the strategic frontier for maintaining the national security and interest. The capability of target acquisition and tracking is the foundation of the space dominance and controlling. Space-based optical surveillance system combines the advantages of space-based observer and optical sensor, and has the superiority in many aspects, such as unlimited by country boundaries, wide aera survelliance, high precision measurement, self-hiding, and so on. Consequently, space-based optical surveillance system has been studied comprehensively by many acadamicians. This dissertation focuses on the target tracking problem based on such surveillance system. The key technologies are discussed and researched particularly, including single target tracking on focal plane, multiple target tracking on focal plane, track-to-track association under multiple observers and multiple sensors, target tracking in the space. The main contributions of this dissertation are demonstrated as follows:
In chapter 2, the target kinematic characteristics are analyzed firstly, and then the main aspects of the target dynamic model are discussed. Secondly, definition and transformation of basic coordinates are introduced; a measurement model is established based on the mapping relationship from target stereo position to focal plane. Thirdly, an analytical method of the line-of-sight (LOS) measurement error is proposed for space-based optical sensor, a LOS error model is given. Lastly, theoretic target location accuracy is derived under the model. The aforementioned researches can serve as the foundation for the studies of succeeding chapters.
In chapter 3, the issue of single target tracking on focal plane is studied. Firstly, the measurement model and target dynamic model on focal plane are both established. Secondly, a single target tracking algorithm on focal plane is proposed under the random finite set (RFS) theory framework, which avoids data association based on modeling target states and measurements as RFS variables. Thirdly, the algorithm implementation problem is taken into account; a Sequential Monte Carlo (SMC) implementation and a Gaussian Mixture (GM) implementation is presented, respectively. Simulation results demonstrate that the tracking performance of this algorithm is superior to the traditional single target tracking algorithm IPDA.
In chapter 4, the issue of multiple target tracking on focal plane is studied. The arisen probability hypothesis density (PHD) filter in recent years is applied to meet this problem, and some approaches are studied to improve the PHD filter performance on focal plane from the aspects of tracking accuracy, computation efficiency, and PHD implementation. Firstly, by using the signal amplitude information (AI), an AI auxiliary PHD filter on focal plane is proposed, which enhances the target tracking accuracy in low signal-to-noise ratio (SNR) circumstance. Secondly, by introducing the gating technology within traditional multiple target tracking methods, a gated PHD filter on focal plane is proposed, which improves the computation efficiency in strong clutter circumstance. Finally, by integrating the sigma-point filter and Gaussian mixture models (GMM) into PHD, a GM sigma-point PHD filter is proposed, which extends the GM-PHD from linear Gaussian conditions to nonlinear nonGaussian conditions.
In chapter 5, the issue of track-to-track association under multiple observers and multiple sensors is studied. Firstly, the global nearest neighbor (GNN) principle used in measurement-to-measurement association problem is introduced into the hinge angle difference hypothesis testing method, a track-to-track association algorithm based on 2 dimensional assignment principle is proposed, treating the hinge angle difference as the cost. Simulation results demonstrate that the performance is better than the hypothesis testing method. However, the hinge angle difference based track-to-track association algorithms mentioned above have some drawbacks, such as the performance is influenced by the geometry easily, and may become invalid at certain conditions. Because the target kinetic characteristics is a valuable information, hence a maximum likelihood (ML) based track-to-track association algorithm is proposed, which makes the best of the target kinetic information. At the same time, the likelihood ratio presentation and target state ML estimation problems are also studied. Simulation results show that the performance of the ML based track-to-track association algorithm is better than the hinge angle difference based track-to-track association algorithm, at the cost of additional computation.
In chapter 6, the issue of target tracking in the space is studied. Firstly, a booster kinetic model is established, which considers the thrust acceleration variation and the attack angle variation at the same time. This booster kinetic model extends the application scope of the constant attack angle kinetic model. Then, the multiple model concept and UKF filter are integrated into this tracking problem, a target tracking algorithm based on multiple model and UKF filter is proposed, which uses 2 kinetic models, one is the booster kinetic model, and the other is the J2 Keplerian model. Simulation results show that this algorithm can adapt to interstage separation and burnout time automatically, and gets better tracking performance than single model UKF filter and single model EKF filter.
第二章首先对目标的运动特性进行了分析,明确了目标运动模型中的主要影响因素;然后给出了基本的坐标系定义及转换关系,并根据天基光学传感器的成像过程,建立了传感器观测模型;接下来研究了天基光学传感器的视线测量误差,建立了视线测量误差分析模型;最后结合视线测量误差分析模型,进一步推导了目标的理论定位精度。第二章将为后续章节的研究提供支撑。
第三章研究了传感器像平面内的单目标轨迹起始与跟踪问题。首先,建立了像平面内的目标运动模型和测量模型;其次,在随机有限集理论框架下,通过将目标状态和量测数据建模为随机有限集,提出了一种不需要数据关联的基于随机有限集理论的像平面单目标跟踪算法;接着针对该算法得不到闭式解的问题,研究了一种基于序贯蒙特卡罗的算法实现技术和一种基于高斯混合的算法实现技术。仿真结果表明,该算法相对于经典单目标跟踪算法IPDA的跟踪性能更优。
第四章研究了传感器像平面内的多目标轨迹起始与跟踪问题。尝试将近年兴起的概率假设密度(Probability Hypothesis Density,PHD)滤波器引入到本问题中,并从PHD滤波器像平面跟踪的跟踪性能、计算效率以及算法实现等方面分别进行了研究。首先,利用信号幅度信息,提出了一种幅度参数辅助的像平面PHD滤波器,改善了在低信噪比环境下的多目标跟踪性能;其次,借鉴传统多目标跟踪方法中的加窗技术,提出了一种加窗的像平面PHD滤波器,提高了在强杂波环境中的计算效率;最后,结合sigma点滤波和高斯混合模型,提出了一种基于高斯混合sigma点滤波的PHD滤波器实现技术,从而将GM-PHD滤波器从线性、高斯条件扩展到非线性、非高斯条件。
第五章研究了多平台多感器之间的像平面轨迹关联问题。首先,将量测-量测关联问题中的GNN关联准则引入到倾角差假设检验算法中,提出了一种基于倾角差二维分配的像平面轨迹关联算法,仿真结果验证了该算法相对于倾角差假设检验算法的性能优势;其次,针对倾角差法存在受观测几何影响大、可能失效的缺陷,结合目标的三维运动特性,提出了一种基于极大似然的像平面轨迹关联算法,并进一步研究了与该算法密切相关的似然比表示、目标状态极大似然估计等问题。仿真结果表明,极大似然方法的像平面轨迹关联性能优于倾角差方法,但计算复杂度也更高。
第六章在第五章像平面轨迹关联的基础上,研究了多平台多传感器观测下的高精度三维空间目标跟踪问题。首先,基于第二章目标运动特性的分析结论,建立了一种同时考虑推力加速度变化和攻角变化的主动段动力学模型,扩展了动力学模型的应用范围;其次,将多模型概念和UKF滤波引入到本问题中,提出了一种基于动力学模型和J 2摄动模型的多模型UKF滤波的目标跟踪算法。仿真结果表明,该算法可以自主判断级间分离和关机,并且获得比单模型UKF滤波器、单模型EKF滤波器更优的主动段、中段跟踪性能。
The space has become the strategic frontier for maintaining the national security and interest. The capability of target acquisition and tracking is the foundation of the space dominance and controlling. Space-based optical surveillance system combines the advantages of space-based observer and optical sensor, and has the superiority in many aspects, such as unlimited by country boundaries, wide aera survelliance, high precision measurement, self-hiding, and so on. Consequently, space-based optical surveillance system has been studied comprehensively by many acadamicians. This dissertation focuses on the target tracking problem based on such surveillance system. The key technologies are discussed and researched particularly, including single target tracking on focal plane, multiple target tracking on focal plane, track-to-track association under multiple observers and multiple sensors, target tracking in the space. The main contributions of this dissertation are demonstrated as follows:
In chapter 2, the target kinematic characteristics are analyzed firstly, and then the main aspects of the target dynamic model are discussed. Secondly, definition and transformation of basic coordinates are introduced; a measurement model is established based on the mapping relationship from target stereo position to focal plane. Thirdly, an analytical method of the line-of-sight (LOS) measurement error is proposed for space-based optical sensor, a LOS error model is given. Lastly, theoretic target location accuracy is derived under the model. The aforementioned researches can serve as the foundation for the studies of succeeding chapters.
In chapter 3, the issue of single target tracking on focal plane is studied. Firstly, the measurement model and target dynamic model on focal plane are both established. Secondly, a single target tracking algorithm on focal plane is proposed under the random finite set (RFS) theory framework, which avoids data association based on modeling target states and measurements as RFS variables. Thirdly, the algorithm implementation problem is taken into account; a Sequential Monte Carlo (SMC) implementation and a Gaussian Mixture (GM) implementation is presented, respectively. Simulation results demonstrate that the tracking performance of this algorithm is superior to the traditional single target tracking algorithm IPDA.
In chapter 4, the issue of multiple target tracking on focal plane is studied. The arisen probability hypothesis density (PHD) filter in recent years is applied to meet this problem, and some approaches are studied to improve the PHD filter performance on focal plane from the aspects of tracking accuracy, computation efficiency, and PHD implementation. Firstly, by using the signal amplitude information (AI), an AI auxiliary PHD filter on focal plane is proposed, which enhances the target tracking accuracy in low signal-to-noise ratio (SNR) circumstance. Secondly, by introducing the gating technology within traditional multiple target tracking methods, a gated PHD filter on focal plane is proposed, which improves the computation efficiency in strong clutter circumstance. Finally, by integrating the sigma-point filter and Gaussian mixture models (GMM) into PHD, a GM sigma-point PHD filter is proposed, which extends the GM-PHD from linear Gaussian conditions to nonlinear nonGaussian conditions.
In chapter 5, the issue of track-to-track association under multiple observers and multiple sensors is studied. Firstly, the global nearest neighbor (GNN) principle used in measurement-to-measurement association problem is introduced into the hinge angle difference hypothesis testing method, a track-to-track association algorithm based on 2 dimensional assignment principle is proposed, treating the hinge angle difference as the cost. Simulation results demonstrate that the performance is better than the hypothesis testing method. However, the hinge angle difference based track-to-track association algorithms mentioned above have some drawbacks, such as the performance is influenced by the geometry easily, and may become invalid at certain conditions. Because the target kinetic characteristics is a valuable information, hence a maximum likelihood (ML) based track-to-track association algorithm is proposed, which makes the best of the target kinetic information. At the same time, the likelihood ratio presentation and target state ML estimation problems are also studied. Simulation results show that the performance of the ML based track-to-track association algorithm is better than the hinge angle difference based track-to-track association algorithm, at the cost of additional computation.
In chapter 6, the issue of target tracking in the space is studied. Firstly, a booster kinetic model is established, which considers the thrust acceleration variation and the attack angle variation at the same time. This booster kinetic model extends the application scope of the constant attack angle kinetic model. Then, the multiple model concept and UKF filter are integrated into this tracking problem, a target tracking algorithm based on multiple model and UKF filter is proposed, which uses 2 kinetic models, one is the booster kinetic model, and the other is the J2 Keplerian model. Simulation results show that this algorithm can adapt to interstage separation and burnout time automatically, and gets better tracking performance than single model UKF filter and single model EKF filter.
引文
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