海事雷达目标检测与跟踪算法研究

发布时间：2018-05-29 03:00

本文选题：海杂波 + 恒虚警检测　；参考：《浙江大学》2017年硕士论文

【摘要】：随着海上交通运输业的迅速发展,船舶流量越来越多,海上航行安全问题越来越引起人们的重视。为了保证海上航行的安全性,国内外各大港口都引入船舶交通管理系统(Vessel Traffic Services,VTS系统),VTS系统通过雷达来监视海域的船舶动态,核心处理算法是雷达目标检测与跟踪算法。针对目前的雷达目标检测与跟踪算法存在检测能力弱、跟踪能力较差的问题,本文通过搭建采集平台去现场采集海事雷达数据,研究海事雷达目标检测与跟踪算法。本文主要包含以下三个方面的工作:1.在目标检测算法方面,本文首先介绍了获取海事雷达实测数据的整套流程,得到全文研究的数据集;然后利用参数估计与假设检验理论来分析实测海杂波数据集,得到海杂波的最优分布模型;最后针对海杂波的最优分布模型,详细分析对应的恒虚警检测算法的原理,并用该检测算法处理实测数据集,成功的滤除大部分海杂波信息。2.在目标跟踪算法方面,本文主要基于概率假设密度粒子滤波(PHD-PF)算法展开研究:针对传统PHD-PF算法难以处理实际数据的问题,本文提出一种基于均匀粒子分布的UPDPHD-PF算法,通过数值仿真结果表明,提出的算法能够较好的跟踪多目标状态,在同等条件设置下,具有与传统算法相近的跟踪性能,并用该算法处理实测数据集,取得了较好的跟踪效果。3.在算法的实时性性能方面,针对传统PHD-PF算法与UPDPHD-PF算法实时性较差的问题,本文提出一种基于自适应粒子分布的APDPHD-PF算法,该算法根据当前时刻的观测值产生新生粒子,有效的减少粒子数目,进而降低算法计算量与运行时间。接着通过数值仿真结果,验证了该算法确实可以在保持跟踪性能的同时,提高算法的实时性。最后用该算法处理实测数据集,取得较好的跟踪效果,同时与UPDPHD-PF算法相比,该算法在实时性性能上表现出了极大的优势。
[Abstract]:With the rapid development of maritime transportation, more and more ships are flowing, and people pay more and more attention to the safety of maritime navigation. In order to ensure the safety of sea navigation, the vessel traffic management system (Vessel Traffic Services VTS system) is introduced into the port at home and abroad to monitor the ship dynamics in the sea area by radar. The core processing algorithm is the radar target detection and tracking algorithm. Aiming at the problem of weak detection ability and poor tracking ability in the current radar target detection and tracking algorithms, this paper studies the detection and tracking algorithm of maritime radar targets by building a collection platform to collect maritime radar data on the spot. This article mainly includes the following three aspects of work: 1. In the aspect of target detection algorithm, this paper first introduces the whole process of obtaining the measured data of maritime radar, and then analyzes the measured sea clutter data set by using the theory of parameter estimation and hypothesis test. Finally, the principle of the corresponding CFAR detection algorithm is analyzed in detail for the optimal distribution model of sea clutter, and the method is used to deal with the measured data set, which can filter out most of the sea clutter information .2. In the aspect of target tracking algorithm, this paper mainly studies the PHD-PFF algorithm based on probability assumption density particle filter. Aiming at the problem that the traditional PHD-PF algorithm is difficult to deal with the actual data, a UPDPHD-PF algorithm based on uniform particle distribution is proposed in this paper. The numerical simulation results show that the proposed algorithm can track the multi-target state well, and it has the same tracking performance as the traditional algorithm under the same conditions. The algorithm is used to deal with the measured data set, and a better tracking effect is obtained. In terms of the real-time performance of the algorithm, aiming at the poor real-time performance of the traditional PHD-PF algorithm and the UPDPHD-PF algorithm, this paper proposes a APDPHD-PF algorithm based on adaptive particle distribution, which produces new particles according to the observed values at the present time. Effectively reduce the number of particles, and then reduce the computational complexity and running time of the algorithm. Then the numerical simulation results show that the algorithm can keep the tracking performance and improve the real-time performance of the algorithm. Finally, the algorithm is used to deal with the measured data set, and the tracking effect is better. Compared with the UPDPHD-PF algorithm, the algorithm has a great advantage in real-time performance.
【学位授予单位】：浙江大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN957.51

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