适航地图构建与地面目标跟踪研究
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摘要
地面目标跟踪是当前无人机研究的一项热点。由于地面情况的复杂性、地面目标的多样性以及无人机机载传感器的探测能力和精度等诸多因素的影响,准确地进行地面目标跟踪存在很多困难。地面目标的运动很大程度上受到实际情况的制约,对于战术无人机,显然可以利用这些信息从多角度实现对地面目标的精确侦察与跟踪。本文提出了一种同时适航地图构建与目标跟踪方法,无人机通过实时融合自身侦察获得的地面环境信息,构建地面目标的适航地图,并利用该适航地图对地面目标的运动进行约束,通过贝叶斯滤波方法进行目标估计与跟踪。
首先分析了现有的目标跟踪技术在无人机目标跟踪问题中的优缺点,采用贝叶斯滤波器作为无人机目标跟踪的一般性方法,并结合适航地图的概念,提出了一种适用于间断观测和局部观测情形的跟踪算法。得益于贝叶斯滤波器内在的灵活结构,所提出的BF-HMap算法能够同时处理非线性、与环境相关的目标动力学模型,以及由无人机有限视野范围和自身运动限制所造成的非解析的传感器模型。综合性能比较表明,在间断观测和局部观测情形下,BF-HMap算法的估计和跟踪效果远好于现有的目标跟踪方法。
第二部分主要研究了无人机遥感图像的全自动道路提取问题。本文将环境中的道路作为适航地图最主要的构成因素,为自动生成适航地图,需从无人机遥感图像中自动提取出主要道路。提出的方法主要基于以下两条途径:其一,微分几何方法提取线性结构的中心线;其二,基于主动轮廓模型(又被称为Snake)的道路边界提取。采用基于模型的策略进行图像的道路识别,提取流程如下:首先采用自底而上的方法生成图像中的可能目标,然后回到原始图像,将假想进行验证和分类。采用了三级道路模型,依次提取显著道路,非显著道路,以及交叉口。
本文的第三部分从三个方面对全局适航地图的构建进行了研究:针对多传感器融合问题,提出了基于D-S证据理论的适航地图构建算法;为适应无人机目标跟踪任务需求,采用机载POS系统直接对地定位的方案进行遥感影像定位;为从航拍图像序列生成全局的适航地图,提出一种基于尺度不变特征变换(SIFT)的自动拼接方法。该方法主要包括图像配准和镶嵌两个步骤。由于航拍序列各帧图像之间存在较大差异,因而常用的基于特征的配准方法适用性较差,对此,提出利用SIFT特征来实现准确、稳健的航拍图像配准。通过对航拍图像序列进行拼接实验验证了所提方法的有效性。
为提高无人机在未知环境中目标跟踪的性能,本文对无人机的地面目标跟踪相关技术进行了研究,提出了一种基于适航地图的无人机地面目标跟踪方法。本文提出的方法充分考虑了如下一些问题:非线性贝叶斯滤波器设计、适航地图的概念及构建、遥感影像解读及定位,构成了一套比较完整的无人机地面目标跟踪算法的设计方案。
Ground target tracking is one of the most critical issues in UAVs study. There are many factors which influence the accuracy of tracking such as the complexity of the ground situation, diversity of the ground targets, precision of the UAVs airborne sensors, etc. Since the movements of ground targets constrained by the actual ground situation on a large scale, the tactical UAVs can obviously use such information to achieve better detection and tracking performance from multiple perspectives. This dissertation presented a simultaneous hospitability map building and target tracking method, the UAVs build ground targets' hospitability maps through the integration of real-time access to their own reconnaissance of environmental information, and targets estimation and tracking are carried through Bayesian filtering methods.
First, with an exhaustive analysis of the advantages and disadvantages of existing target tracking approaches, a generic method for target track maintenance, the BF-HMap approach, was proposed based on the Bayesian filtering method and the hospitability map. Due to the flexible scheme of Bayesian inference inherent in this algorithm, BF-HMap method is capable of exploiting non-analytic prior environmental knowledge as well as handling intermittent and regional measurements caused by the coverage and motion constraints on UAVs. Comprehensive performance comparison shows that BF-HMap algorithm estimates and tracks targets far better than existing methods under intermittent and regional observation.
The second major part considers the automatic road extraction from UAV remote sensing images. Roads in the environment are regarded as the most important components of hospitability maps. In order to build hospitability map automatically, main roads need to be extracted from the UAV remote sensing images automatically. Proposed method is based on two previously developed approaches: the first one is the differential geometric approach to extract linear features; second one is road contour extraction based on Active Contour Models (also known as Snake). A model-based strategy is used to recognize main roads in images, whose extraction processes as follows: First, one-directional bottom-up methods were used to generate the hypotheses of roads in images. Then, the hypotheses were verified and classified with its return to the original date. Three road models were used to extract salient roads, non-salient roads, and road crossings, respectively.
Further research work on the construction of hospitability map is given in the third part of this dissertation in three main aspects. First is to propose a D-S theory based hospitability map construction method for the fusion of information from multiple sensors. Second is to use Positioning Orientation System (POS) to realize direct geo-referencing. Third is to present a SIFT based automatic stitching of aerial image sequence method for the generation of global hospitability map. It consisted of two core steps: image registration and mosaic. As two consecutive frames in aerial image sequences often exhibit significant differences, the present commonly used feature-based methods are not very suitable for their registration. Concerned with this issue, an iterative registration method using the SIFT features was put forward to achieve accurate and robust aerial image registration. Experiments on simulated aerial sequences prove the effectiveness of the proposed method.
Ground target tracking by UAVs was studied in this dissertation. A simultaneous hospitability map building and target tracking method was proposed to improve the UAVs target tracking performance in unknown environment. The proposed method fully considered the following issues: non-linear Bayesian filter design, the concept and construction of hospitability map, and remote sensing image interpretation and position, all of which constituted a set of relatively complete UAVs ground target tracking algorithm.
首先分析了现有的目标跟踪技术在无人机目标跟踪问题中的优缺点,采用贝叶斯滤波器作为无人机目标跟踪的一般性方法,并结合适航地图的概念,提出了一种适用于间断观测和局部观测情形的跟踪算法。得益于贝叶斯滤波器内在的灵活结构,所提出的BF-HMap算法能够同时处理非线性、与环境相关的目标动力学模型,以及由无人机有限视野范围和自身运动限制所造成的非解析的传感器模型。综合性能比较表明,在间断观测和局部观测情形下,BF-HMap算法的估计和跟踪效果远好于现有的目标跟踪方法。
第二部分主要研究了无人机遥感图像的全自动道路提取问题。本文将环境中的道路作为适航地图最主要的构成因素,为自动生成适航地图,需从无人机遥感图像中自动提取出主要道路。提出的方法主要基于以下两条途径:其一,微分几何方法提取线性结构的中心线;其二,基于主动轮廓模型(又被称为Snake)的道路边界提取。采用基于模型的策略进行图像的道路识别,提取流程如下:首先采用自底而上的方法生成图像中的可能目标,然后回到原始图像,将假想进行验证和分类。采用了三级道路模型,依次提取显著道路,非显著道路,以及交叉口。
本文的第三部分从三个方面对全局适航地图的构建进行了研究:针对多传感器融合问题,提出了基于D-S证据理论的适航地图构建算法;为适应无人机目标跟踪任务需求,采用机载POS系统直接对地定位的方案进行遥感影像定位;为从航拍图像序列生成全局的适航地图,提出一种基于尺度不变特征变换(SIFT)的自动拼接方法。该方法主要包括图像配准和镶嵌两个步骤。由于航拍序列各帧图像之间存在较大差异,因而常用的基于特征的配准方法适用性较差,对此,提出利用SIFT特征来实现准确、稳健的航拍图像配准。通过对航拍图像序列进行拼接实验验证了所提方法的有效性。
为提高无人机在未知环境中目标跟踪的性能,本文对无人机的地面目标跟踪相关技术进行了研究,提出了一种基于适航地图的无人机地面目标跟踪方法。本文提出的方法充分考虑了如下一些问题:非线性贝叶斯滤波器设计、适航地图的概念及构建、遥感影像解读及定位,构成了一套比较完整的无人机地面目标跟踪算法的设计方案。
Ground target tracking is one of the most critical issues in UAVs study. There are many factors which influence the accuracy of tracking such as the complexity of the ground situation, diversity of the ground targets, precision of the UAVs airborne sensors, etc. Since the movements of ground targets constrained by the actual ground situation on a large scale, the tactical UAVs can obviously use such information to achieve better detection and tracking performance from multiple perspectives. This dissertation presented a simultaneous hospitability map building and target tracking method, the UAVs build ground targets' hospitability maps through the integration of real-time access to their own reconnaissance of environmental information, and targets estimation and tracking are carried through Bayesian filtering methods.
First, with an exhaustive analysis of the advantages and disadvantages of existing target tracking approaches, a generic method for target track maintenance, the BF-HMap approach, was proposed based on the Bayesian filtering method and the hospitability map. Due to the flexible scheme of Bayesian inference inherent in this algorithm, BF-HMap method is capable of exploiting non-analytic prior environmental knowledge as well as handling intermittent and regional measurements caused by the coverage and motion constraints on UAVs. Comprehensive performance comparison shows that BF-HMap algorithm estimates and tracks targets far better than existing methods under intermittent and regional observation.
The second major part considers the automatic road extraction from UAV remote sensing images. Roads in the environment are regarded as the most important components of hospitability maps. In order to build hospitability map automatically, main roads need to be extracted from the UAV remote sensing images automatically. Proposed method is based on two previously developed approaches: the first one is the differential geometric approach to extract linear features; second one is road contour extraction based on Active Contour Models (also known as Snake). A model-based strategy is used to recognize main roads in images, whose extraction processes as follows: First, one-directional bottom-up methods were used to generate the hypotheses of roads in images. Then, the hypotheses were verified and classified with its return to the original date. Three road models were used to extract salient roads, non-salient roads, and road crossings, respectively.
Further research work on the construction of hospitability map is given in the third part of this dissertation in three main aspects. First is to propose a D-S theory based hospitability map construction method for the fusion of information from multiple sensors. Second is to use Positioning Orientation System (POS) to realize direct geo-referencing. Third is to present a SIFT based automatic stitching of aerial image sequence method for the generation of global hospitability map. It consisted of two core steps: image registration and mosaic. As two consecutive frames in aerial image sequences often exhibit significant differences, the present commonly used feature-based methods are not very suitable for their registration. Concerned with this issue, an iterative registration method using the SIFT features was put forward to achieve accurate and robust aerial image registration. Experiments on simulated aerial sequences prove the effectiveness of the proposed method.
Ground target tracking by UAVs was studied in this dissertation. A simultaneous hospitability map building and target tracking method was proposed to improve the UAVs target tracking performance in unknown environment. The proposed method fully considered the following issues: non-linear Bayesian filter design, the concept and construction of hospitability map, and remote sensing image interpretation and position, all of which constituted a set of relatively complete UAVs ground target tracking algorithm.
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