复杂环境下弱目标检测与跟踪算法研究
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摘要
在现代战争中,尽可能地在远距离上检测并跟踪目标,可以确保指控系统获得足够的预警时间,实现有效打击,进而决定战争主动权的归属。目标的距离越远,由于多径干扰、成像面积小等因素,目标信号强度越弱；同时,随着当前隐身技术、低空突防技术、反辐射技术、电磁干扰技术的发展,对于雷达系统而言,目标的雷达散射截面积变小,回波信号变弱,目标探测面临着高虚警、高杂波等复杂背景的影响。因此,复杂环境下弱目标的检测与跟踪成为传感器探测系统必需考虑和亟待解决的技术难题。相对于经典的检测后跟踪(Detect Before Track, DBT)技术,检测前跟踪(Track Before Detect,TBD)技术是一种较好的解决该技术难题的方法,它采用软决策处理传感器的原始观测,充分挖掘观测的有用信息,通过时间积累实现对弱目标的检测与跟踪。本文针对复杂环境下的目标检测与跟踪问题,进行了相关的研究和应用,主要成果如下：
1)针对存在信号起伏的机动弱目标的检测与跟踪,提出一种基于滑窗平均法的多模型粒子滤波检测前跟踪(Multiple Model Particle Filter TBD, MMPF-TBD)算法。针对传统MMPF-TBD在目标机动性和信号起伏较强时,需要更多粒子保证性能,通过引入滑窗平均法,根据之前时刻目标的检测与估计结果,判断如何产生新粒子以影响下一时刻的粒子集分布,保证粒子的多样性和有效性,减少粒子数量,并提高算法的检测概率与跟踪精度。
2)针对多目标实时检测与跟踪问题,提出一种基于PF-TBD (Particle Filter TBD,PF-TBD)的非机动多弱目标检测算法,算法无需预知目标最大数目,在每一时刻,根据估计结果自适应调整状态合集和事件模型集,减少无效模型和状态的使用；经过滤波后,通过滑窗法判断是否有目标出现或消失,估计出当前时刻目标的数目和状态。仿真结果表明,算法适用于目标数目未知且时变的情况,并具有更好的检测性能。
3)针对当前TBD实现方法主要针对单传感器问题,提出一种基于粒子滤波的多异步雷达融合TBD算法。该方法根据各雷达采样率和探测区域的异同,通过设计一个准则,实现粒子集分类,在确保算法检测与跟踪性能的同时,增加粒子计算的有效性,并保证当前未采样雷达探测区域内粒了的多样性。仿真结果验证了算法的有效性。
4)针对传统跟踪技术在多机动目标状态估计上具有一定优势,将其引入到TBD框架中,在基于动态规划的检测前跟踪(Dynamic Programming TBD, DP-TBD)方法的基础上,提出一种带惩罚项的DP-TBD (Penalty DP-TBD, PDP-TBD)方法,突破了传统DP-TBD算法只能处理单个慢机动目标的限制。通过在值函数中构造一个惩罚项,设计新的值函数,将跟踪性能反馈到检测环节,实现传统跟踪技术与DP-TBD相互结合；针对跟踪分离问题的影响,在算法设计中引入一个约束条件,即传感器的量测有且只有一个来源,并基于该约束条件,设计PDP-TBD算法的流程。
5)针对实际工程应用中,真实环境存在的各种不确定因素,如目标运动不确定性、量测来源不确定性、相邻扫描间隔不确定性、目标数目不确定性,提出一种基于多模型的多机动目标跟踪算法。仿真和实验表明,算法可以同时有效处理100批机动或非机动目标,正确给出目标的航迹号和当前状态,跟踪性能良好,适用于工程应用。
In modern warfare, in order to achieve effective strike, remote targets should be detected and tracked to ensure enough time for command-control system. Because of multipath error, small target imaging area and so on, remote target signal power becomes weaker and more difficult to be detected and tracked. Meanwhile, with the developments of stealth technology, low-altitude penetration technology, anti-radiation technology and electromagnetic interference, the radar cross section becomes small and the target echo becomes weak. Furthermore, the target signal is also influenced by complex background, such as high false alarm and high clutter. Thus, detecting and tracking dim targets in complex environment becomes a technique challenge. Compared with classic detect before track (DBT) technique, track before detect (TBD) method is an effective solution. It makes soft decision on the measurement data. Unlike DBT, TBD detections are not declared at each scan. Instead, a number of scans of data are processed, and then the estimated target track is acquired when the detection is declared. In this thesis, research and application about detecting and tracking dim targets in complex environment are summarized in several key bullets below:
1. For detecting and tracking a maneuvering dim target with fluctuating amplitude, a multiple model particle filter track before detect (MMPF-TBD) algorithm based on sliding window is proposed. The traditional MMPF-TBD needs more particles when the target maneuvers and fluctuates strongly. The new algorithm applies sliding window to determine whether the particles are affected by the estimation of the target. When the value exceeds threshold, new particles are added in accordance with the state estimation of the previous moments. With the new algorithm, the diversity and effectiveness of the particles are protected, and the detection probability and tracking accuracy are improved.
2. For multiple radar target detection and tracking, a novel particle filter based track before detect (PF-TBD) algorithm is proposed to detect multiple targets with unknown maximum target number. At each scan, in order to reduce the influence of invalid models and states, the new algorithm adaptively updates the state set and event model set according to target estimation results. After filtering, sliding window method is applied to determine whether the targets appear or disappear, and the target number and state are estimated. Simulation results show that the new algorithm can effectively detect and track multiple targets in real time, and improve detection performance.
3. For traditional TBD methods assume a single sensor system, a new particle based TBD algorithm is proposed for multiple asynchronous radar system. According to the difference of radar sampling rates and detection coverage, this algorithm designs a classification criterion to divide particle set into two parts. One part of the particles is used to estimate the target state, and the other part is used to preserve adequate particles in each radar detection coverage. Simulation results confirm the efficiency of the new algorithm.
4. For traditional tracking techniques have advantages when estimating multiple maneuvering target states, an improved dynamic programming track-before-detect algorithm (DP-TBD) called penalty DP-TBD (PDP-TBD) is proposed. By PDP-TBD, the advantages of traditional tracking techniques are introduced to TBD frames. The performances of tracking techniques are used as a feedback to the detection part, and the feedback is constructed by a penalty term in the merit function. With this feedback, the algorithm combines traditional tracking techniques with DP-TBD and it can be applied to simultaneously detect and track maneuvering dim targets. Meanwhile, a reasonable constraint that a sensor measurement can originate from one target or clutter is proposed to minimize track separation.
5. In real environment, many uncertain problems exist, for example, the uncertainty of target movement, the uncertainty of the origination of measurements, the uncertainty of the interval of two successive sampling scan, the uncertainty of target number and so on. As a solution, a multi-target tracking algorithm based on multi-model is proposed. The simulation and experiment confirm that the algorithm can effectively deal with100targets, and estimate theses targets track number and states.
1)针对存在信号起伏的机动弱目标的检测与跟踪,提出一种基于滑窗平均法的多模型粒子滤波检测前跟踪(Multiple Model Particle Filter TBD, MMPF-TBD)算法。针对传统MMPF-TBD在目标机动性和信号起伏较强时,需要更多粒子保证性能,通过引入滑窗平均法,根据之前时刻目标的检测与估计结果,判断如何产生新粒子以影响下一时刻的粒子集分布,保证粒子的多样性和有效性,减少粒子数量,并提高算法的检测概率与跟踪精度。
2)针对多目标实时检测与跟踪问题,提出一种基于PF-TBD (Particle Filter TBD,PF-TBD)的非机动多弱目标检测算法,算法无需预知目标最大数目,在每一时刻,根据估计结果自适应调整状态合集和事件模型集,减少无效模型和状态的使用；经过滤波后,通过滑窗法判断是否有目标出现或消失,估计出当前时刻目标的数目和状态。仿真结果表明,算法适用于目标数目未知且时变的情况,并具有更好的检测性能。
3)针对当前TBD实现方法主要针对单传感器问题,提出一种基于粒子滤波的多异步雷达融合TBD算法。该方法根据各雷达采样率和探测区域的异同,通过设计一个准则,实现粒子集分类,在确保算法检测与跟踪性能的同时,增加粒子计算的有效性,并保证当前未采样雷达探测区域内粒了的多样性。仿真结果验证了算法的有效性。
4)针对传统跟踪技术在多机动目标状态估计上具有一定优势,将其引入到TBD框架中,在基于动态规划的检测前跟踪(Dynamic Programming TBD, DP-TBD)方法的基础上,提出一种带惩罚项的DP-TBD (Penalty DP-TBD, PDP-TBD)方法,突破了传统DP-TBD算法只能处理单个慢机动目标的限制。通过在值函数中构造一个惩罚项,设计新的值函数,将跟踪性能反馈到检测环节,实现传统跟踪技术与DP-TBD相互结合；针对跟踪分离问题的影响,在算法设计中引入一个约束条件,即传感器的量测有且只有一个来源,并基于该约束条件,设计PDP-TBD算法的流程。
5)针对实际工程应用中,真实环境存在的各种不确定因素,如目标运动不确定性、量测来源不确定性、相邻扫描间隔不确定性、目标数目不确定性,提出一种基于多模型的多机动目标跟踪算法。仿真和实验表明,算法可以同时有效处理100批机动或非机动目标,正确给出目标的航迹号和当前状态,跟踪性能良好,适用于工程应用。
In modern warfare, in order to achieve effective strike, remote targets should be detected and tracked to ensure enough time for command-control system. Because of multipath error, small target imaging area and so on, remote target signal power becomes weaker and more difficult to be detected and tracked. Meanwhile, with the developments of stealth technology, low-altitude penetration technology, anti-radiation technology and electromagnetic interference, the radar cross section becomes small and the target echo becomes weak. Furthermore, the target signal is also influenced by complex background, such as high false alarm and high clutter. Thus, detecting and tracking dim targets in complex environment becomes a technique challenge. Compared with classic detect before track (DBT) technique, track before detect (TBD) method is an effective solution. It makes soft decision on the measurement data. Unlike DBT, TBD detections are not declared at each scan. Instead, a number of scans of data are processed, and then the estimated target track is acquired when the detection is declared. In this thesis, research and application about detecting and tracking dim targets in complex environment are summarized in several key bullets below:
1. For detecting and tracking a maneuvering dim target with fluctuating amplitude, a multiple model particle filter track before detect (MMPF-TBD) algorithm based on sliding window is proposed. The traditional MMPF-TBD needs more particles when the target maneuvers and fluctuates strongly. The new algorithm applies sliding window to determine whether the particles are affected by the estimation of the target. When the value exceeds threshold, new particles are added in accordance with the state estimation of the previous moments. With the new algorithm, the diversity and effectiveness of the particles are protected, and the detection probability and tracking accuracy are improved.
2. For multiple radar target detection and tracking, a novel particle filter based track before detect (PF-TBD) algorithm is proposed to detect multiple targets with unknown maximum target number. At each scan, in order to reduce the influence of invalid models and states, the new algorithm adaptively updates the state set and event model set according to target estimation results. After filtering, sliding window method is applied to determine whether the targets appear or disappear, and the target number and state are estimated. Simulation results show that the new algorithm can effectively detect and track multiple targets in real time, and improve detection performance.
3. For traditional TBD methods assume a single sensor system, a new particle based TBD algorithm is proposed for multiple asynchronous radar system. According to the difference of radar sampling rates and detection coverage, this algorithm designs a classification criterion to divide particle set into two parts. One part of the particles is used to estimate the target state, and the other part is used to preserve adequate particles in each radar detection coverage. Simulation results confirm the efficiency of the new algorithm.
4. For traditional tracking techniques have advantages when estimating multiple maneuvering target states, an improved dynamic programming track-before-detect algorithm (DP-TBD) called penalty DP-TBD (PDP-TBD) is proposed. By PDP-TBD, the advantages of traditional tracking techniques are introduced to TBD frames. The performances of tracking techniques are used as a feedback to the detection part, and the feedback is constructed by a penalty term in the merit function. With this feedback, the algorithm combines traditional tracking techniques with DP-TBD and it can be applied to simultaneously detect and track maneuvering dim targets. Meanwhile, a reasonable constraint that a sensor measurement can originate from one target or clutter is proposed to minimize track separation.
5. In real environment, many uncertain problems exist, for example, the uncertainty of target movement, the uncertainty of the origination of measurements, the uncertainty of the interval of two successive sampling scan, the uncertainty of target number and so on. As a solution, a multi-target tracking algorithm based on multi-model is proposed. The simulation and experiment confirm that the algorithm can effectively deal with100targets, and estimate theses targets track number and states.
引文
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