机动目标无源协同定位技术

发布时间：2018-04-17 13:44

本文选题：机动目标跟踪 + 无源协同定位　；参考：《杭州电子科技大学》2017年硕士论文

【摘要】：随着隐身技术、电子干扰、反辐射导弹等技术的飞速发展,雷达探测环境日益复杂,主动雷达的探测性能面临巨大挑战。无源协同定位(Passive Coherent Location,PCL)技术就是利用外辐射源(如商业调频广播电台,电视台,手机基站等)照射目标,通过接收雷达的接收天线被动接收目标散射的外辐射源信号,对目标进行定位。该技术成本低,具有良好的隐身与反隐身能力,受到国内外学者的日益关注。本文对机动目标无源协同定位技术开展了深入研究,主要工作如下:首先,介绍了机动目标无源协同定位技术基本理论,深入研究了有向图切换变结构多模型算法和自适应变结构多模型算法这两个机动目标跟踪算法,通过仿真,将这两种算法与传统交互多模型算法进行了比较,有效地提高了强机动目标的跟踪性能。其次,提出一种基于最远点优先渐近的当前统计有向图切换变结构交互多模型算法。该方法利用最远点优先渐近算法来优化伪测量,并将当前统计模型与有向图切换变结构交互多模型算法结合,进行滤波估计,实现对机动目标进行跟踪。与传统方法相比,所提算法有效提高了机动目标无源协同定位的性能。最后,提出一种基于逆向平滑的当前统计有向图切换变结构交互多模型算法。该方法通过最速下降法来优化伪测量,并在当前统计有向图切换变结构交互多模型算法滤波完成后,采用逆向平滑来进行平滑回溯,优化跟踪轨迹,实现对机动目标进行跟踪,有效改善强机动目标无源协同定位性能。仿真结果验证了所提方法的有效性。
[Abstract]:With the rapid development of stealth technology, electronic jamming, anti-radiation missile and other technologies, the radar detection environment is increasingly complex, and the detection performance of active radar is facing a huge challenge.Passive Coherent location (PCL) technology uses external emitter (such as commercial FM radio station, TV station, mobile phone base station, etc.) to illuminate the target, and passively receives the external emitter signal from the target by receiving the receiving antenna of radar.Locate the target.The technology has low cost and good ability of stealth and anti-stealth, and has been paid more and more attention by scholars at home and abroad.In this paper, the passive cooperative positioning technology of maneuvering target is deeply studied. The main work is as follows: firstly, the basic theory of passive cooperative positioning of maneuvering target is introduced.In this paper, two maneuvering target tracking algorithms, I. e., directional graph switching variable structure multiple model algorithm and adaptive variable structure multiple model algorithm, are studied in depth. By simulation, the two algorithms are compared with the traditional interactive multiple model algorithm.The tracking performance of strong maneuvering targets is improved effectively.Secondly, an active multimodel algorithm based on the farthest point first asymptotic approach is proposed.In this method, the furthest point first asymptotic algorithm is used to optimize the pseudo-measurement, and the current statistical model is combined with the directional graph switching variable structure interactive multi-model algorithm to carry out filtering and estimation to realize the tracking of maneuvering targets.Compared with the traditional method, the proposed algorithm can effectively improve the performance of passive cooperative location of maneuvering targets.At last, a new algorithm based on reverse smoothing is proposed for switching variable structure muti-model of statistical directed graph.In this method, the pseudo measurement is optimized by the steepest descent method. After the filtering of the current statistical directed graph switching variable structure interactive multi-model algorithm is completed, the reverse smoothing is used to smooth the backtracking, optimize the track track, and realize the maneuvering target tracking.The performance of passive cooperative positioning of strong maneuvering target is improved effectively.Simulation results show the effectiveness of the proposed method.
【学位授予单位】：杭州电子科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN95

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