本文選題：海雜波 + 恒虛警檢測�。� 參考：《浙江大學(xué)》2017年碩士論文

【摘要】：隨著海上交通運輸業(yè)的迅速發(fā)展,船舶流量越來越多,海上航行安全問題越來越引起人們的重視。為了保證海上航行的安全性,國內(nèi)外各大港口都引入船舶交通管理系統(tǒng)(Vessel Traffic Services,VTS系統(tǒng)),VTS系統(tǒng)通過雷達來監(jiān)視海域的船舶動態(tài),核心處理算法是雷達目標(biāo)檢測與跟蹤算法。針對目前的雷達目標(biāo)檢測與跟蹤算法存在檢測能力弱、跟蹤能力較差的問題,本文通過搭建采集平臺去現(xiàn)場采集海事雷達數(shù)據(jù),研究海事雷達目標(biāo)檢測與跟蹤算法。本文主要包含以下三個方面的工作:1.在目標(biāo)檢測算法方面,本文首先介紹了獲取海事雷達實測數(shù)據(jù)的整套流程,得到全文研究的數(shù)據(jù)集;然后利用參數(shù)估計與假設(shè)檢驗理論來分析實測海雜波數(shù)據(jù)集,得到海雜波的最優(yōu)分布模型;最后針對海雜波的最優(yōu)分布模型,詳細分析對應(yīng)的恒虛警檢測算法的原理,并用該檢測算法處理實測數(shù)據(jù)集,成功的濾除大部分海雜波信息。2.在目標(biāo)跟蹤算法方面,本文主要基于概率假設(shè)密度粒子濾波(PHD-PF)算法展開研究:針對傳統(tǒng)PHD-PF算法難以處理實際數(shù)據(jù)的問題,本文提出一種基于均勻粒子分布的UPDPHD-PF算法,通過數(shù)值仿真結(jié)果表明,提出的算法能夠較好的跟蹤多目標(biāo)狀態(tài),在同等條件設(shè)置下,具有與傳統(tǒng)算法相近的跟蹤性能,并用該算法處理實測數(shù)據(jù)集,取得了較好的跟蹤效果。3.在算法的實時性性能方面,針對傳統(tǒng)PHD-PF算法與UPDPHD-PF算法實時性較差的問題,本文提出一種基于自適應(yīng)粒子分布的APDPHD-PF算法,該算法根據(jù)當(dāng)前時刻的觀測值產(chǎn)生新生粒子,有效的減少粒子數(shù)目,進而降低算法計算量與運行時間。接著通過數(shù)值仿真結(jié)果,驗證了該算法確實可以在保持跟蹤性能的同時,提高算法的實時性。最后用該算法處理實測數(shù)據(jù)集,取得較好的跟蹤效果,同時與UPDPHD-PF算法相比,該算法在實時性性能上表現(xiàn)出了極大的優(yōu)勢。
[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.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TN957.51

【參考文獻】

相關(guān)期刊論文 前1條

1 田淑榮;蓋明久;何友;;隨機集的概率假設(shè)密度粒子濾波[J];海軍航空工程學(xué)院學(xué)報;2006年04期

相關(guān)博士學(xué)位論文 前1條

1 趙玲玲;目標(biāo)跟蹤中的粒子濾波與概率假設(shè)密度濾波研究[D];哈爾濱工業(yè)大學(xué);2011年

相關(guān)碩士學(xué)位論文 前2條

1 姚柯柯;基于粒子濾波的PHD多目標(biāo)跟蹤方法研究[D];西安電子科技大學(xué);2013年

2 孟凡志;基于DSP的雷達目標(biāo)檢測與信息處理系統(tǒng)[D];大連海事大學(xué);2007年

，

本文編號：1949234