机载多传感器数据融合技术研究
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摘要
本文针对当前机载多传感器系统工程应用中面临的实际问题,对机载多传感器数据融合技术进行了研究,研究内容主要集中于机载多传感器资源分配、目标类型识别、状态估计与跟踪四个方面。
系统建立了基于线性规划的多传感器资源分配算法模型,针对实际的机载火控系统的工程背景,提出将目标相对价值与传感器对目标的平均跟踪(锁定)时间之比作为效能函数,从而便于基于线性规划的分配算法的实现。给出了雷达与红外的协同探测模型和融合仿真模型。
多假设跟踪算法最早应用于红外等图像跟踪系统的数据处理,一直以来都应用于单传感器的目标跟踪中,对于多传感器组网的MHT应用情况,国内目前无任何相关报导,国外已有大型的先进组网探测系统应用了该算法框架,但未见技术细节。本文对机载雷达网系统的多假设跟踪(MHT)框架设计和工程实现问题进行了系统深入的研究。针对系统实际工程特点和实装情况,重点研究了集中式点迹融合的MHT的框架构建问题,并对工程实现中的若干关键技术细节进行了说明。
对目标电磁散射特征的证据组合识别作了研究,针对证据组合中的基本概率赋值求取问题,设计了单特征和多特征的目标组合识别方法,并对其实际效果进行了比较、分析和评价。
针对机载红外搜索跟踪系统的特点,分别对远距离IRST的第三代图像跟踪技术、多站交叉跟踪技术、红外与无源时差系统联合定位技术进行了研究。将Bayes检测引入到红外图像传感器在目标检测时的灰度过滤中,得到新的PDAF参数(检测概率、虚假量测分布等)。在基于MHT的远距离IRST的图像跟踪方法中,针对远距离IRST的目标属性信息的利用以及MHT的相关处理提出了相应解决办法。对双站交会跟踪的精度进行了CRLB不等式下界计算,并借用单站跟踪的模式建立一种基于单站的修正增益扩展kalman滤波(MGEKF)跟踪方法。对红外与无源时差定位系统的联合定位问题进行了精度分析。
There are many research aspects in airborne multi-sensor information fusion system. This dissertation focuses on the practical problems of engineering applications, such as airborne multi-sensor detecting modeling, target recognition, target state estimation and target tracking as well. The main contents and results in this dissertation are as follows.
The detecting and tracking models for multi-sensor system based on liner programming are well studied and established, aiming at the practical airborne fire control and guide systems. The value of effectiveness for liner programming is the ratio of the target's relative value to the mean time of tracking, that is, the overall value of the sensors locked in each time unit is served as the objective function. The liner programming is practically feasible since the establishment of target priority and mean time for tracking. Meanwhile, the coordinated detecting and fusing model for Radar and IRST is also given.
Multi-hypothesis tracking algorithm was primitively applied for the data processing of infrared image-tracking system, and it was applied for the target-tracking of single-sensor at all times. Nowadays, there are some large-scaled and advanced detecting systems overseas which adopt MHT algorithm; however, there is no technical detail. In this paper, a specific and efficient MHT algorithm for long-scan-period-radar and centralized fusion structure is established with engineering realization, based on the practical military equipments and the application circumstance.
For target recognition, evidence combination equation is applied with appropriate revise. The application of evidence theory in multi-sensor-multi-measurement airborne radar recognition is quite effective for bettering the single sensor or single characteristic recognition.
The image tracking and angle only tracking techniques for long distance IRST are also investigated. For the long distance tracking of single small target by infrared image sensor, as long as the tracker could offer feedback information, i.e. the predicted location and distribution of target, the sensor would use Bayes detection. As a result, a variable greyscale threshold is implemented in image filtering, reducing the number of candidate measurements, which is in favor of the PDAF-based tracking method. As for MHT application, image overlapping is prevalent when targets are far from sensor and the targets density is relatively high, even if the false measurement density is not so serious. To cope with the image overlapping, a multi-assignment method is established based on one-to-one two-dimensional assignment. The fused tracking algorithm for multi-angle-only-sensor system is studied, which is a weakness in current information-fusion research. First, the CRLB is used to analyze the tracking precision by two sensors from different platforms, considering bandwidth influence. Second, the constant velocity model is applied to the EKF tracking in a single site manner. Calculation and simulation validates the significance of two passive sensors tracking. The applicability of MGEKF in multi-angle-only-sensor case is investigated and proved. Precision analysis is given to discuss the combination orientating problem between infrared and radar system.
系统建立了基于线性规划的多传感器资源分配算法模型,针对实际的机载火控系统的工程背景,提出将目标相对价值与传感器对目标的平均跟踪(锁定)时间之比作为效能函数,从而便于基于线性规划的分配算法的实现。给出了雷达与红外的协同探测模型和融合仿真模型。
多假设跟踪算法最早应用于红外等图像跟踪系统的数据处理,一直以来都应用于单传感器的目标跟踪中,对于多传感器组网的MHT应用情况,国内目前无任何相关报导,国外已有大型的先进组网探测系统应用了该算法框架,但未见技术细节。本文对机载雷达网系统的多假设跟踪(MHT)框架设计和工程实现问题进行了系统深入的研究。针对系统实际工程特点和实装情况,重点研究了集中式点迹融合的MHT的框架构建问题,并对工程实现中的若干关键技术细节进行了说明。
对目标电磁散射特征的证据组合识别作了研究,针对证据组合中的基本概率赋值求取问题,设计了单特征和多特征的目标组合识别方法,并对其实际效果进行了比较、分析和评价。
针对机载红外搜索跟踪系统的特点,分别对远距离IRST的第三代图像跟踪技术、多站交叉跟踪技术、红外与无源时差系统联合定位技术进行了研究。将Bayes检测引入到红外图像传感器在目标检测时的灰度过滤中,得到新的PDAF参数(检测概率、虚假量测分布等)。在基于MHT的远距离IRST的图像跟踪方法中,针对远距离IRST的目标属性信息的利用以及MHT的相关处理提出了相应解决办法。对双站交会跟踪的精度进行了CRLB不等式下界计算,并借用单站跟踪的模式建立一种基于单站的修正增益扩展kalman滤波(MGEKF)跟踪方法。对红外与无源时差定位系统的联合定位问题进行了精度分析。
There are many research aspects in airborne multi-sensor information fusion system. This dissertation focuses on the practical problems of engineering applications, such as airborne multi-sensor detecting modeling, target recognition, target state estimation and target tracking as well. The main contents and results in this dissertation are as follows.
The detecting and tracking models for multi-sensor system based on liner programming are well studied and established, aiming at the practical airborne fire control and guide systems. The value of effectiveness for liner programming is the ratio of the target's relative value to the mean time of tracking, that is, the overall value of the sensors locked in each time unit is served as the objective function. The liner programming is practically feasible since the establishment of target priority and mean time for tracking. Meanwhile, the coordinated detecting and fusing model for Radar and IRST is also given.
Multi-hypothesis tracking algorithm was primitively applied for the data processing of infrared image-tracking system, and it was applied for the target-tracking of single-sensor at all times. Nowadays, there are some large-scaled and advanced detecting systems overseas which adopt MHT algorithm; however, there is no technical detail. In this paper, a specific and efficient MHT algorithm for long-scan-period-radar and centralized fusion structure is established with engineering realization, based on the practical military equipments and the application circumstance.
For target recognition, evidence combination equation is applied with appropriate revise. The application of evidence theory in multi-sensor-multi-measurement airborne radar recognition is quite effective for bettering the single sensor or single characteristic recognition.
The image tracking and angle only tracking techniques for long distance IRST are also investigated. For the long distance tracking of single small target by infrared image sensor, as long as the tracker could offer feedback information, i.e. the predicted location and distribution of target, the sensor would use Bayes detection. As a result, a variable greyscale threshold is implemented in image filtering, reducing the number of candidate measurements, which is in favor of the PDAF-based tracking method. As for MHT application, image overlapping is prevalent when targets are far from sensor and the targets density is relatively high, even if the false measurement density is not so serious. To cope with the image overlapping, a multi-assignment method is established based on one-to-one two-dimensional assignment. The fused tracking algorithm for multi-angle-only-sensor system is studied, which is a weakness in current information-fusion research. First, the CRLB is used to analyze the tracking precision by two sensors from different platforms, considering bandwidth influence. Second, the constant velocity model is applied to the EKF tracking in a single site manner. Calculation and simulation validates the significance of two passive sensors tracking. The applicability of MGEKF in multi-angle-only-sensor case is investigated and proved. Precision analysis is given to discuss the combination orientating problem between infrared and radar system.
引文
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188.An Efficient Implementation of Reid's Multiple Hypothesis Tracking Algorithm lngemar J. Cox and Sunita L. Hingorani IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL.18, NO.2, FEBRUARY 1996:138-150
189.An MHT algorithm for UWB radar-based multiple human target tracking SangHyun Chang, Michael Wolf, and Joel W. Burdick ICUWB 2009 (September 9-11, 2009):459-463
190.Curved Shape Reconstruction Using Multiple Hypothesis Tracking Bradley A. Moran, John J. Leonard, IEEE JOURNAL OF OCEANIC ENGINEERING, VOL.22, NO.4, OCTOBER 1997:625-638
191.Detection and Localization of Multiple Unresolved Extended Targets via Monopulse Radar Signal Processing XIN ZHANG, PETER WILLETT, YAAKOV BAR-SHALOM, IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS VOL.45, NO.2 APRIL 2009:456-472
192.Dimensionless Score Function for Multiple Hypothesis Tracking YAAKOV BAR-SHALOM, SAM S. BLACKMAN, ROBERT J. FITZGERALD, IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS VOL.43, NO. 1 JANUARY 2007:392-400
193.Efficient Multiple Hypothesis Tracking by Track Segment Graph, Chee-Yee Chong, Greg Castanon, Nathan Cooprider Shozo Mori, Ravi Ravichandran,12th International Conference on Information Fusion Seattle, WA, USA, July 6-9,2009:2177-2184
194.IMM/MHT Solution to Radar Benchmark Tracking Problem, S. S. BLACKMAN, IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS VOL. 35, NO.2 APRIL 1999:730-738
195.Mode-based Multi-Hypothesis Head Tracking Using Parametric Contours, Yunqiang Chen, Yong Rui, Thomas Huang, Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition (FGR.02)
196.Multihypothesis Tracking Using Incoherent Signal-Strength Information, G. VAN KEUK, IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS VOL.32, NO.3 JULY 1996:1164-1170
197.Multiple Hypothesis Track Maintenance with Possibly Unresolved Measurements, W. KOCH, G. VAN KEUK, IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS VOL.33, NO.3 JULY 1997:883-892
198.Multiple Hypothesis Tracking For Multiple Target Tracking, SAMUEL S.BLACKMAN, IEEE A&E SYSTEMS MAGAZINE Vol.19,NO.1 January 2004: 5-18
199.Multiple Hypothesis Tracking Using Clustered Measurements Michael T. Wolf, 2009 IEEE International Conference on Robotics and Automation Kobe International Conference Center Kobe, Japan, May 12-17,2009:3955-3961
200.Multiple Maneuver Model Track-Oriented MHT (Multiple Hypothesis Tracking) Yoshio KOSUGE, Masamichi KOJIMA, and Shingo TSUJIMICHI, SICE'99 July 28-30, Morioka:1129-1134
201.Tracking Algorithm Speed Comparisons Between MHT and PMHT Darin T. Dunham ISIF 2002:846-851