基于时频差及测向的协同定位跟踪技术研究

发布时间：2018-12-15 04:53

【摘要】：现代战场的电磁环境越来越复杂,传统的有源定位技术往往无法准确测得目标位置信息,且容易暴露自身的位置。与有源定位技术相比,无源定位技术具有优良的定位性能及较强的战场生存能力,已成为现代战场环境下采用的一项关键技术。无源定位主要包括无源时差、频差及测向交叉定位三种基本的定位技术,该技术是通过接收来自目标辐射源的信号来对目标进行定位,观测站本身不发射信号,因此其隐蔽性高,观测的范围广,且定位精度高。但是,单手段的无源定位技术由于其接收的信号单一,存在诸多局限性,因此多种无源定位技术的协同定位是未来战场的必然趋势,在军事领域有广阔的应用前景。为此,本文在以下几个方面对基于无源时频差及测向的协同定位跟踪的相关技术展开了研究。首先,本文对几种基本无源定位技术开展了研究,主要对无源时差(TDOA)、频差(FDOA)、测向交叉(AOA)定位的原理和定位精度进行了理论及仿真分析,研究了影响不同定位技术定位精度的因素,并对GDOP进行了仿真分析。文中还分析研究了卡尔曼滤波的相关技术,以及基于卡尔曼滤波的融合算法,包括基于卡尔曼滤波的序贯融合算法和基于卡尔曼滤波的直接分布式融合算法。然后,研究了卡尔曼滤波技术在无源时差、频差及测向交叉定位中的应用。通过建模及算法分析,研究了扩展卡尔曼滤波在采用无源时差、频差及测向交叉技术进行定位跟踪时的使用方法,并对跟踪算法的性能进行了仿真分析,对比了真实轨迹与滤波轨迹,研究了不同测量误差对跟踪精度的影响。同时,对比了扩展卡尔曼滤波技术和无迹卡尔曼滤波技术在基于时频差及测向交叉的目标定位跟踪中的跟踪性能,验证了两种滤波技术的有效性。最后,在上述研究的基础上本文研究了现有的基于二次估计的时频差的协同定位跟踪技术,并针对该技术在时差定位中存在的模糊度问题进行了改进,提出了基于二次估计的时频差及测向交叉的协同定位跟踪技术。基于二次估计的TDOA/AOA-FDOA的协同定位技术与TDOA/FDOA的协同定位技术的主要区别在于首先要使用TDOA/AOA混合定位方法,在混合定位的过程中,使用集中式卡尔曼滤波序贯融合算法将测得的时差数据和角度数据进行第一次融合,得到目标位置的第一次估计。然后将得到的目标位置估计值代入到FDOA无源定位算法中,可以求解出目标的速度信息,再将其代入目标状态方程中,即可得到目标位置的第二次估计。最后,利用直接式分布卡尔曼滤波融合算法将两次估计的结果进行融合,即可得到最后的位置估计。通过将单手段无源定位跟踪技术、现有的联合定位技术与本文改进的协同定位跟踪技术进行仿真对比,结果表明,改进的基于二次估计的时频差及测向的协同定位跟踪技术获得的目标位置更加精确,可靠性更高。
[Abstract]:The electromagnetic environment of the modern battlefield is becoming more and more complex. The traditional active positioning technology often can not accurately measure the target location information, and it is easy to expose its position. Compared with active positioning technology, passive location technology has excellent positioning performance and strong battlefield survival ability, and has become a key technology in modern battlefield environment. Passive location mainly includes three basic positioning techniques: passive time difference, frequency difference and direction finding cross location. The technology is to locate the target by receiving signals from the target emitter, and the observation station itself does not transmit signals. Therefore, its concealment is high, the observation range is wide, and the positioning accuracy is high. However, the single means passive location technology has many limitations because of its single signal, so the cooperative location of multiple passive location technologies is an inevitable trend in the future battlefield, and has a broad application prospect in military field. Therefore, in the following aspects, the related technologies of cooperative location and tracking based on passive time-frequency difference and direction finding are studied in this paper. First of all, several basic passive positioning techniques are studied in this paper. The principle and accuracy of (FDOA), direction crossing (AOA) location with passive time difference (TDOA),) are analyzed in theory and simulation. The factors that affect the positioning accuracy of different positioning techniques are studied, and the simulation analysis of GDOP is carried out. The related technologies of Kalman filter and fusion algorithm based on Kalman filter are also analyzed and studied, including sequential fusion algorithm based on Kalman filter and direct distributed fusion algorithm based on Kalman filter. Then, the application of Kalman filter in passive time difference, frequency difference and direction finding cross location is studied. Through modeling and algorithm analysis, the application of extended Kalman filter in locating and tracking with passive time difference, frequency difference and direction finding is studied, and the performance of the tracking algorithm is simulated. The influence of different measurement errors on the tracking accuracy is studied by comparing the real trajectory with the filtering track. At the same time, the tracking performance of extended Kalman filter and unscented Kalman filter in target location and tracking based on time-frequency difference and direction-finding crossover is compared, and the effectiveness of the two filtering techniques is verified. Finally, on the basis of the above research, this paper studies the existing time-frequency difference (TFDTD) based cooperative localization and tracking technology, and improves the ambiguity problem in TDOA location. Based on quadratic estimation, a cooperative localization and tracking technique based on time-frequency difference and direction finding crossover is proposed. The main difference between the co-location technology of TDOA/AOA-FDOA based on quadratic estimation and that of TDOA/FDOA is that the hybrid localization method of TDOA/AOA should be used in the process of hybrid localization. A centralized Kalman filtering sequential fusion algorithm is used to fuse the measured time difference data and angle data for the first time to obtain the first estimation of the target position. Then, the estimated position of the target is substituted into the FDOA passive location algorithm, and the velocity information of the target can be solved, and then the second estimation of the target position can be obtained by inserting it into the state equation of the target. Finally, the results of the two estimates are fused by using the direct distributed Kalman filter fusion algorithm, and the final position estimation can be obtained. By comparing the single means passive location and tracking technology, the existing joint positioning technology with the improved cooperative positioning and tracking technology in this paper, the simulation results show that, The improved time-frequency difference and direction-finding cooperative location tracking technique based on quadratic estimation is more accurate and reliable.
【学位授予单位】：西南交通大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN95

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