基于時(shí)頻差及測向的協(xié)同定位跟蹤技術(shù)研究
發(fā)布時(shí)間：2018-12-15 04:53
【摘要】：現(xiàn)代戰(zhàn)場的電磁環(huán)境越來越復(fù)雜,傳統(tǒng)的有源定位技術(shù)往往無法準(zhǔn)確測得目標(biāo)位置信息,且容易暴露自身的位置。與有源定位技術(shù)相比,無源定位技術(shù)具有優(yōu)良的定位性能及較強(qiáng)的戰(zhàn)場生存能力,已成為現(xiàn)代戰(zhàn)場環(huán)境下采用的一項(xiàng)關(guān)鍵技術(shù)。無源定位主要包括無源時(shí)差、頻差及測向交叉定位三種基本的定位技術(shù),該技術(shù)是通過接收來自目標(biāo)輻射源的信號來對目標(biāo)進(jìn)行定位,觀測站本身不發(fā)射信號,因此其隱蔽性高,觀測的范圍廣,且定位精度高。但是,單手段的無源定位技術(shù)由于其接收的信號單一,存在諸多局限性,因此多種無源定位技術(shù)的協(xié)同定位是未來戰(zhàn)場的必然趨勢,在軍事領(lǐng)域有廣闊的應(yīng)用前景。為此,本文在以下幾個(gè)方面對基于無源時(shí)頻差及測向的協(xié)同定位跟蹤的相關(guān)技術(shù)展開了研究。首先,本文對幾種基本無源定位技術(shù)開展了研究,主要對無源時(shí)差(TDOA)、頻差(FDOA)、測向交叉(AOA)定位的原理和定位精度進(jìn)行了理論及仿真分析,研究了影響不同定位技術(shù)定位精度的因素,并對GDOP進(jìn)行了仿真分析。文中還分析研究了卡爾曼濾波的相關(guān)技術(shù),以及基于卡爾曼濾波的融合算法,包括基于卡爾曼濾波的序貫融合算法和基于卡爾曼濾波的直接分布式融合算法。然后,研究了卡爾曼濾波技術(shù)在無源時(shí)差、頻差及測向交叉定位中的應(yīng)用。通過建模及算法分析,研究了擴(kuò)展卡爾曼濾波在采用無源時(shí)差、頻差及測向交叉技術(shù)進(jìn)行定位跟蹤時(shí)的使用方法,并對跟蹤算法的性能進(jìn)行了仿真分析,對比了真實(shí)軌跡與濾波軌跡,研究了不同測量誤差對跟蹤精度的影響。同時(shí),對比了擴(kuò)展卡爾曼濾波技術(shù)和無跡卡爾曼濾波技術(shù)在基于時(shí)頻差及測向交叉的目標(biāo)定位跟蹤中的跟蹤性能,驗(yàn)證了兩種濾波技術(shù)的有效性。最后,在上述研究的基礎(chǔ)上本文研究了現(xiàn)有的基于二次估計(jì)的時(shí)頻差的協(xié)同定位跟蹤技術(shù),并針對該技術(shù)在時(shí)差定位中存在的模糊度問題進(jìn)行了改進(jìn),提出了基于二次估計(jì)的時(shí)頻差及測向交叉的協(xié)同定位跟蹤技術(shù)。基于二次估計(jì)的TDOA/AOA-FDOA的協(xié)同定位技術(shù)與TDOA/FDOA的協(xié)同定位技術(shù)的主要區(qū)別在于首先要使用TDOA/AOA混合定位方法,在混合定位的過程中,使用集中式卡爾曼濾波序貫融合算法將測得的時(shí)差數(shù)據(jù)和角度數(shù)據(jù)進(jìn)行第一次融合,得到目標(biāo)位置的第一次估計(jì)。然后將得到的目標(biāo)位置估計(jì)值代入到FDOA無源定位算法中,可以求解出目標(biāo)的速度信息,再將其代入目標(biāo)狀態(tài)方程中,即可得到目標(biāo)位置的第二次估計(jì)。最后,利用直接式分布卡爾曼濾波融合算法將兩次估計(jì)的結(jié)果進(jìn)行融合,即可得到最后的位置估計(jì)。通過將單手段無源定位跟蹤技術(shù)、現(xiàn)有的聯(lián)合定位技術(shù)與本文改進(jìn)的協(xié)同定位跟蹤技術(shù)進(jìn)行仿真對比,結(jié)果表明,改進(jìn)的基于二次估計(jì)的時(shí)頻差及測向的協(xié)同定位跟蹤技術(shù)獲得的目標(biāo)位置更加精確,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.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TN95
本文編號：2380015
[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.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TN95
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