高频雷达目标数据处理技术研究
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摘要
高频雷达以其特有的超视距警戒能力和对低可观测性目标的优良探测能力,成为一种执行大范围远程战略监测的有效手段,日益受到世界各国的普遍重视。目标数据处理作为雷达系统的关键部分,实现对多目标的实时跟踪处理,估计目标的运动状态参数,并完成数据关联及航迹处理等功能,有效地抑制类目标干扰引起的虚警,提高雷达对目标的连续跟踪能力,以期获得较好的态势表达。高频雷达的目标数据处理是在空间分辨力和位置量测精度较低、量测误差统计特性不确定、数据率低、目标运动形式未知、目标可能暂时丢检的恶劣条件下进行的。随着观测环境变得日趋复杂,目标的多样性、机动性、密集性和低可观测性也在不断加强,因此研究适应性更强、可靠性更好的目标数据处理方法具有十分重要的意义。本文深入研究了提高高频雷达目标航迹处理的稳健性、建立航迹的快速有效性以及保持目标航迹连续性的处理方法与技术途径。
针对高频雷达的特殊数据处理环境,提出以目标量测数据为驱动的跟踪系统建模方法,克服了理想化先验模型存在的局限性。在雷达坐标系中通过改进型自学习方法建立目标状态转移模型,用双重中值平滑算法自适应估计量测噪声的统计参数,再进行限定记忆自适应滤波处理来获得较为准确的目标运动状态估计,并对跟踪滤波效果进行评估。量测数据预处理中,利用估值精度较高的多普勒速度来修正径向距离量测数据的异常情况,并建立一种基于模糊集合理论的实时野值判别准则,对数据进行合理性检验,提高了量测信息的准确性,避免引起滤波发散。仿真和实测数据的处理结果都表明,改进的跟踪滤波方法对先验信息依赖较小,能够自适应地反映跟踪系统状态,性能稳健可靠。
引入空间轨迹匹配原理,对目标运动轨迹的光滑性进行约束,并利用径向速度与径向距离之间的匹配原理来提高径向距离的量测精度。该方法不受雷达体制的限制,不受工作参数变化、数据率不一致、数据丢失的影响,可以独立使用,快速有效地建立起目标的航迹。进一步结合前面的跟踪滤波改进算法构成两步跟踪处理:通过空间轨迹匹配处理保证目标运动轨迹的空间合理性,并估计跟踪系统的噪声特性;再对上步的处理结果建立自学习目标运动模型,进行限定记忆自学习适应滤波。空间轨迹匹配原理能够有效地控制跟踪模型的准确性,削弱航迹的起伏抖动,保证数据处理系统的稳定性。两步跟踪滤波方法可以推广应用于多雷达组网工作的点迹融合处理中。在同步融合和顺序量测异步融合中,只需在第二步时域滤波时进行序贯处理。对于非顺序量测异步融合,则可以单独使用空间轨迹匹配处理进行目标状态估计。
分析了造成目标暂时丢检的原因,研究了量测数据缺失的情况下对目标航迹的续断处理方法,以保证对同一目标跟踪的连续性。多目标杂波环境下,目标间歇出现也可以进行关联处理,并采用修正逻辑法完成航迹起始。航迹稳定建立后,目标暂时丢失时并不立即进行航迹撤销,而是采用自适应盲跟踪方法锁定其运动规律,向前延续适当批次的航迹,以期对目标的再次俘获。深入研究盲跟踪涉及的多步状态预测技术、盲跟次数设置、关联范围调整、目标再次检出后的航迹平滑处理及航迹终止逻辑。针对目标航迹交叉引起的航迹中断,提出一种多假设简易联合概率数据关联算法来解决数据分配问题。
跟踪与检测的结合使用,能够保护被发现的目标不轻易丢失,有效抑制非背景噪声类干扰引起的虚警,提高对目标数据的连续处理性能。信号检测首先是在单批次的频谱数据上进行的,强目标回波通过基本信号检测就能够检出。低信噪比目标回波则依据跟踪处理得到的目标状态预测信息来动态调整信号检测的区域,进行保护性的低门限跟踪信号检测。针对探海时低径向速度区域常见的目标密集情况,提出一种整峰野值剔除准则来降低虚高的检测门限,有效地避免了弱目标被遮蔽的现象。应用跟踪中的目标检测,引入暂时航迹质量参数来进行目标确认,虚假航迹的识别与杂波图的建立。
设计实现了高频雷达目标数据处理系统,并已应用于实际工程项目中。该系统主要包括系统参数设定、目标运动设计、频谱数据生成、检测跟踪处理、态势显示等组成部分。对实验数据和仿真产生各种情况的目标点迹数据的处理结果,验证了本文提出的目标数据处理方法具有连续、稳定、快速有效的航迹处理能力。
High frequency (HF) radar is an effective means to execute large-scale and long-range strategic surveillance due to its over-the-horizon alert ability and excellent detection ability to the low-observability targets, so the world pays more attention to it. As the key section in radar system, target data processing is used to accomplish multiple targets tracking realtimely, estimating the motion state parameters, data association and trace processing, etc, which can suppress effectively the false alarms caused by target-like disturbances, improve the continuous tracking ability of target sequence, and obtain the better situation display. Target data processing in HF radar is carried on under the bad conditions of low space resolution, low precision of position measurement, uncertain statistical characteristics of measurement error, low data rate, unknown target motion mode and target may be lost. As the observation environment gets more complex, target’s maneuverability, diversity, density and low-observability are enhanced correspondingly. Therefore, it is necessary to do some research on adaptive and reliable target data processing algorithm. In this paper, the algorithm and technique are studied detailed to improve the abilities of making target trace processed stably, formed rapidly and effectively and maintained continuously in target data processing system of HF radar.
Considering the special data processing environment in HF radar, a method of tracking system modeling driven by target measuring data is presented, which overcomes the limitations in the ideal prior knowledge models. The target state transformation model is built in radar coordinate through improved self-learning method. The statistical parameters of measuring noises are estimated adaptively by dual median smooth algorithm, and more accurate estimations of target state are acquired by limited memory adaptive filter algorithm. Then the effects of tracking filter are evaluated. In the preprocessing of measuring data, Doppler velocity which estimation is more precise is used to correct the false radial range measurement. A realtime outlier discrimination criterion based on fuzzy set theory is presented, which can verify the rationality and improve the veracity of those data to avoid the filter diverging. Through the results of simulation and processing of actual measuring data, it is shown that the improved tracking filter has less dependence on the prior knowledge information, can reflect the tracking system state adaptively and has the stable and credible performance.
The spatial trace matching principle is introduced to restrict the smoothness of target movement trace. And the measuring precision of radical range is improved according the matching principle between the radical velocity and range. This method is not restricted by the radar system style, not influenced by the change of working parameters, the difference of data rate and the lost of measuring data. The target trace can be formed rapidly and effectively only using this method. Combining with the former improved tracking filter algorithm, the two-step tracking procedure is formed. Firstly, the rationality of target movement trace is ensured by spatial trace matching processing and the noise characteristics are estimated. Then the self-learning target motion model is built by the results of former step, and the limited memory self-learning adaptive filer is executed. The spatial trace matching principle can effectively control the accuracy of tracking model, weaken the jitters of the trace and preserve the stability of the data processing system. This two-step tracking algorithm can be used in the measurement point fusion processing of multiple radar networks. In the synchronous fusion and the in-sequence measurement asynchronous fusion, sequential processing is merely required during the second step of the time domain filter. While in the out-of-sequence measurement asynchronous fusion, target state estimation can be carried out through the spatial trace matching processing singly.
For the reason of target temporary lost, the break-continue method in trace processing under the condition of the lack of measuring data is studied to protect the continuous tracking of the same target. In the environment of multiple targets and clutters, the data could be also associated even the target measurement appears discontinuously. The improved logic law is used in the track initiation. After the trace is formed stably, it is not canceled immediately when the target is lost temporarily, but adaptive blind tracking method is used to lock its motion pattern to keep tracking with opportune batches until the target redetection. Some problems involved in blind tracking, such as multi-step state prediction, times setting, association bound adjustment, trace smoothing after the target is recaptured and the trace termination logics, are studied in detail. To resolve the problem of trace interruption caused by traces intersection, an algorithm called multiple hypothesis cheap joint probability data association is proposed to allocate the data.
The combination of tracking and detection can prevent the detected target from missing easily, suppress effectively the false alarms caused by the non-background noise, and improve the performance of target data continuous processing. Signal detection is firstly processed during the spectrum data of a single batch. Those targets with strong echoes can be detected in the basic signal detection. While for those weak target echoes, the zone of signal detection are adjusted according to the state prediction information and sheltered tracking signal detection is processed with low threshold. Considering the multiple target situations which appear frequently in the lower Doppler velocity region during the sea surveillance, a whole-peak-outlier- elimination criterion is proposed to depress the overvalued detection threshold for the sake of avoiding the weaken target is shielded. Target detection in tracking is applied for confirming the target by the temporary trace quality parameter, identifying the false trace and forming the clutter map.
The target data processing system in HF Radar is designed and realized, which has been applied in the real project. This system is divided into several parts such as system parameter setting, target motion design, spectrum data generation, target detection and tracking and state display, etc. The processing results of experiment data and simulation of various target motion show that the synthesis target data processing methods proposed in this dissertation have perfect trace processing performance with continuity, stability, celerity and validity.
针对高频雷达的特殊数据处理环境,提出以目标量测数据为驱动的跟踪系统建模方法,克服了理想化先验模型存在的局限性。在雷达坐标系中通过改进型自学习方法建立目标状态转移模型,用双重中值平滑算法自适应估计量测噪声的统计参数,再进行限定记忆自适应滤波处理来获得较为准确的目标运动状态估计,并对跟踪滤波效果进行评估。量测数据预处理中,利用估值精度较高的多普勒速度来修正径向距离量测数据的异常情况,并建立一种基于模糊集合理论的实时野值判别准则,对数据进行合理性检验,提高了量测信息的准确性,避免引起滤波发散。仿真和实测数据的处理结果都表明,改进的跟踪滤波方法对先验信息依赖较小,能够自适应地反映跟踪系统状态,性能稳健可靠。
引入空间轨迹匹配原理,对目标运动轨迹的光滑性进行约束,并利用径向速度与径向距离之间的匹配原理来提高径向距离的量测精度。该方法不受雷达体制的限制,不受工作参数变化、数据率不一致、数据丢失的影响,可以独立使用,快速有效地建立起目标的航迹。进一步结合前面的跟踪滤波改进算法构成两步跟踪处理:通过空间轨迹匹配处理保证目标运动轨迹的空间合理性,并估计跟踪系统的噪声特性;再对上步的处理结果建立自学习目标运动模型,进行限定记忆自学习适应滤波。空间轨迹匹配原理能够有效地控制跟踪模型的准确性,削弱航迹的起伏抖动,保证数据处理系统的稳定性。两步跟踪滤波方法可以推广应用于多雷达组网工作的点迹融合处理中。在同步融合和顺序量测异步融合中,只需在第二步时域滤波时进行序贯处理。对于非顺序量测异步融合,则可以单独使用空间轨迹匹配处理进行目标状态估计。
分析了造成目标暂时丢检的原因,研究了量测数据缺失的情况下对目标航迹的续断处理方法,以保证对同一目标跟踪的连续性。多目标杂波环境下,目标间歇出现也可以进行关联处理,并采用修正逻辑法完成航迹起始。航迹稳定建立后,目标暂时丢失时并不立即进行航迹撤销,而是采用自适应盲跟踪方法锁定其运动规律,向前延续适当批次的航迹,以期对目标的再次俘获。深入研究盲跟踪涉及的多步状态预测技术、盲跟次数设置、关联范围调整、目标再次检出后的航迹平滑处理及航迹终止逻辑。针对目标航迹交叉引起的航迹中断,提出一种多假设简易联合概率数据关联算法来解决数据分配问题。
跟踪与检测的结合使用,能够保护被发现的目标不轻易丢失,有效抑制非背景噪声类干扰引起的虚警,提高对目标数据的连续处理性能。信号检测首先是在单批次的频谱数据上进行的,强目标回波通过基本信号检测就能够检出。低信噪比目标回波则依据跟踪处理得到的目标状态预测信息来动态调整信号检测的区域,进行保护性的低门限跟踪信号检测。针对探海时低径向速度区域常见的目标密集情况,提出一种整峰野值剔除准则来降低虚高的检测门限,有效地避免了弱目标被遮蔽的现象。应用跟踪中的目标检测,引入暂时航迹质量参数来进行目标确认,虚假航迹的识别与杂波图的建立。
设计实现了高频雷达目标数据处理系统,并已应用于实际工程项目中。该系统主要包括系统参数设定、目标运动设计、频谱数据生成、检测跟踪处理、态势显示等组成部分。对实验数据和仿真产生各种情况的目标点迹数据的处理结果,验证了本文提出的目标数据处理方法具有连续、稳定、快速有效的航迹处理能力。
High frequency (HF) radar is an effective means to execute large-scale and long-range strategic surveillance due to its over-the-horizon alert ability and excellent detection ability to the low-observability targets, so the world pays more attention to it. As the key section in radar system, target data processing is used to accomplish multiple targets tracking realtimely, estimating the motion state parameters, data association and trace processing, etc, which can suppress effectively the false alarms caused by target-like disturbances, improve the continuous tracking ability of target sequence, and obtain the better situation display. Target data processing in HF radar is carried on under the bad conditions of low space resolution, low precision of position measurement, uncertain statistical characteristics of measurement error, low data rate, unknown target motion mode and target may be lost. As the observation environment gets more complex, target’s maneuverability, diversity, density and low-observability are enhanced correspondingly. Therefore, it is necessary to do some research on adaptive and reliable target data processing algorithm. In this paper, the algorithm and technique are studied detailed to improve the abilities of making target trace processed stably, formed rapidly and effectively and maintained continuously in target data processing system of HF radar.
Considering the special data processing environment in HF radar, a method of tracking system modeling driven by target measuring data is presented, which overcomes the limitations in the ideal prior knowledge models. The target state transformation model is built in radar coordinate through improved self-learning method. The statistical parameters of measuring noises are estimated adaptively by dual median smooth algorithm, and more accurate estimations of target state are acquired by limited memory adaptive filter algorithm. Then the effects of tracking filter are evaluated. In the preprocessing of measuring data, Doppler velocity which estimation is more precise is used to correct the false radial range measurement. A realtime outlier discrimination criterion based on fuzzy set theory is presented, which can verify the rationality and improve the veracity of those data to avoid the filter diverging. Through the results of simulation and processing of actual measuring data, it is shown that the improved tracking filter has less dependence on the prior knowledge information, can reflect the tracking system state adaptively and has the stable and credible performance.
The spatial trace matching principle is introduced to restrict the smoothness of target movement trace. And the measuring precision of radical range is improved according the matching principle between the radical velocity and range. This method is not restricted by the radar system style, not influenced by the change of working parameters, the difference of data rate and the lost of measuring data. The target trace can be formed rapidly and effectively only using this method. Combining with the former improved tracking filter algorithm, the two-step tracking procedure is formed. Firstly, the rationality of target movement trace is ensured by spatial trace matching processing and the noise characteristics are estimated. Then the self-learning target motion model is built by the results of former step, and the limited memory self-learning adaptive filer is executed. The spatial trace matching principle can effectively control the accuracy of tracking model, weaken the jitters of the trace and preserve the stability of the data processing system. This two-step tracking algorithm can be used in the measurement point fusion processing of multiple radar networks. In the synchronous fusion and the in-sequence measurement asynchronous fusion, sequential processing is merely required during the second step of the time domain filter. While in the out-of-sequence measurement asynchronous fusion, target state estimation can be carried out through the spatial trace matching processing singly.
For the reason of target temporary lost, the break-continue method in trace processing under the condition of the lack of measuring data is studied to protect the continuous tracking of the same target. In the environment of multiple targets and clutters, the data could be also associated even the target measurement appears discontinuously. The improved logic law is used in the track initiation. After the trace is formed stably, it is not canceled immediately when the target is lost temporarily, but adaptive blind tracking method is used to lock its motion pattern to keep tracking with opportune batches until the target redetection. Some problems involved in blind tracking, such as multi-step state prediction, times setting, association bound adjustment, trace smoothing after the target is recaptured and the trace termination logics, are studied in detail. To resolve the problem of trace interruption caused by traces intersection, an algorithm called multiple hypothesis cheap joint probability data association is proposed to allocate the data.
The combination of tracking and detection can prevent the detected target from missing easily, suppress effectively the false alarms caused by the non-background noise, and improve the performance of target data continuous processing. Signal detection is firstly processed during the spectrum data of a single batch. Those targets with strong echoes can be detected in the basic signal detection. While for those weak target echoes, the zone of signal detection are adjusted according to the state prediction information and sheltered tracking signal detection is processed with low threshold. Considering the multiple target situations which appear frequently in the lower Doppler velocity region during the sea surveillance, a whole-peak-outlier- elimination criterion is proposed to depress the overvalued detection threshold for the sake of avoiding the weaken target is shielded. Target detection in tracking is applied for confirming the target by the temporary trace quality parameter, identifying the false trace and forming the clutter map.
The target data processing system in HF Radar is designed and realized, which has been applied in the real project. This system is divided into several parts such as system parameter setting, target motion design, spectrum data generation, target detection and tracking and state display, etc. The processing results of experiment data and simulation of various target motion show that the synthesis target data processing methods proposed in this dissertation have perfect trace processing performance with continuity, stability, celerity and validity.
引文
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