本文選題：粒子濾波 + 檢測(cè)前跟蹤��； 參考：《江蘇科技大學(xué)》2017年碩士論文

【摘要】：弱小目標(biāo)的檢測(cè)和跟蹤是現(xiàn)代雷達(dá)必須要面對(duì)的關(guān)鍵問題之一。當(dāng)目標(biāo)的回波信號(hào)能量很低或者背景干擾很強(qiáng)時(shí),雷達(dá)傳感器接收到的目標(biāo)回波信噪比(SNR)就很低,此時(shí)基于單幀門限判決的傳統(tǒng)先檢測(cè)后跟蹤(TAD)方法已很難完成檢測(cè)和跟蹤的任務(wù)。檢測(cè)前跟蹤(TBD)方法為此類問題提供了一條有效的解決途徑。該方法集檢測(cè)和跟蹤于一體,不對(duì)單幀回波數(shù)據(jù)設(shè)置門限,沿著目標(biāo)可能的路徑進(jìn)行能量積累,能有效地對(duì)信噪比很低的弱小目標(biāo)進(jìn)行檢測(cè)和跟蹤。在諸多的TBD方法實(shí)現(xiàn)算法中,基于貝葉斯遞推估計(jì)理論的粒子濾波TBD算法(PF-TBD)性能優(yōu)越,是本文研究的重點(diǎn)。首先,本文研究了標(biāo)準(zhǔn)PF算法及其免重采樣改進(jìn)的高斯粒子濾波(GPF)算法的基本原理,針對(duì)將擬蒙特卡羅(QMC)方法應(yīng)用于GPF算法中雖提高了性能但同時(shí)增加了復(fù)雜度與運(yùn)算量的問題,用對(duì)基本粒子集的線性變換簡(jiǎn)化原算法中QMC采樣過程,提出了SQMC-GPF算法,該算法具有更低的復(fù)雜度和運(yùn)算量。仿真實(shí)驗(yàn)表明SQMC-GPF算法與QMC-GPF算法具有相近的濾波性能但擁有更高的運(yùn)算速度。其次,針對(duì)基于標(biāo)準(zhǔn)PF的TBD算法因?yàn)榇嬖谥夭蓸硬襟E而導(dǎo)致粒子失去多樣性和并行性減弱的問題,將GPF算法與RPF-TBD算法相結(jié)合,提出RGPF-TBD算法,并給出了該算法詳細(xì)的推導(dǎo)過程。該新算法繼承了GPF算法的優(yōu)點(diǎn),無需重采樣步驟,具有較高的并行性,且粒子的多樣性得到了保證,從而具有更好的檢測(cè)和跟蹤性能。再次,本文用QMC方法取代RGPF-TBD算法中的蒙特卡羅采樣(MC)方法,同時(shí)用超均勻序列取代RGPF-TBD算法中新生后驗(yàn)概率估計(jì)中的偽隨機(jī)序列,提出了QMC-RGPF-TBD算法。該算法可有效提高粒子的多樣性和利用率。仿真實(shí)驗(yàn)表明,相對(duì)于RPF-TBD和RGPF-TBD算法,該算法具有不錯(cuò)的檢測(cè)和跟蹤性能,但因?yàn)橐肓薗MC方法,所以該算法具有較高的復(fù)雜度和運(yùn)算量,針對(duì)這一問題,本文又提出了SQMC-RGPF-TBD算法,用線性變換簡(jiǎn)化連續(xù)后驗(yàn)概率分布估計(jì)中QMC采樣,同時(shí)用一次擬隨機(jī)采樣代替新生后驗(yàn)概率分布的估計(jì)中的多次擬隨機(jī)采樣,仿真實(shí)驗(yàn)表明,該算法在具有不錯(cuò)的檢測(cè)和跟蹤性能的同時(shí)具有更快的運(yùn)算速度。最后,本文對(duì)目標(biāo)運(yùn)動(dòng)和雷達(dá)量測(cè)系統(tǒng)進(jìn)行建模,將RPF-TBD、RGPF-TBD、QMC-RGPF-TBD和SQMC-RGPF-TBD算法應(yīng)用到弱小目標(biāo)的檢測(cè)中,比較并分析了四種算法在不同信噪比和粒子數(shù)量下的檢測(cè)和跟蹤性能,最終得到如下結(jié)論:當(dāng)不考慮運(yùn)算量時(shí),QMC-RGPF-TBD算法具有最好的檢測(cè)和跟蹤性能;當(dāng)要求實(shí)時(shí)性和性能兼顧時(shí),SQMC-RGPF-TBD算法是首選。
[Abstract]:The detection and tracking of small and weak targets is one of the key problems which must be faced by modern radar. When the echo signal energy of the target is very low or the background interference is very strong, the signal to noise ratio (SNR) of the target echo received by the radar sensor is very low. At this time, the traditional detection and tracking algorithm based on single frame threshold decision is difficult to complete the task of detection and tracking. This method provides an effective way to solve this problem. This method integrates detection and tracking, does not set a threshold for single frame echo data, accumulates energy along the possible path of the target, and can effectively detect and track small and weak targets with very low signal-to-noise ratio (SNR). Among the implementation algorithms of TBD, the particle filter TBD algorithm based on Bayesian recursive estimation theory has excellent performance, which is the focus of this paper. Firstly, the basic principles of the standard PF algorithm and the improved Gao Si particle filter (GPF) algorithm without resampling are studied. In order to solve the problem that quasi Monte Carlo QMC (QMC) method can improve the performance but increase the complexity and computational complexity of the GPF algorithm, a SQMC-GPF algorithm is proposed to simplify the QMC sampling process in the original algorithm by linear transformation of the basic particle set. The algorithm has lower complexity and computational complexity. The simulation results show that the SQMC-GPF algorithm and the QMC-GPF algorithm have similar filtering performance but higher computing speed. Secondly, aiming at the problem that the TBD algorithm based on standard PF leads to the loss of diversity of particles and the weakening of parallelism due to the existence of resampling steps, the RGPF-TBD algorithm is proposed by combining the GPF algorithm with the RPF-TBD algorithm, and the detailed derivation process of the algorithm is given. The new algorithm inherits the advantages of GPF algorithm, and it does not need resampling steps. It has high parallelism, and the diversity of particles is guaranteed, so it has better detection and tracking performance. Thirdly, this paper uses QMC method to replace Monte Carlo sampling method in RGPF-TBD algorithm and superuniform sequence to replace pseudorandom sequence in RGPF-TBD algorithm. A new QMC-RGPF-TBD algorithm is proposed. This algorithm can effectively improve the diversity and utilization of particles. The simulation results show that the algorithm has good detection and tracking performance compared with RPF-TBD and RGPF-TBD algorithms, but because of the introduction of QMC method, the algorithm has a high complexity and computational complexity. In order to solve this problem, SQMC-RGPF-TBD algorithm is proposed in this paper. The linear transformation is used to simplify the QMC sampling in the estimation of the continuous posterior probability distribution. At the same time, the quasi random sampling in the estimation of the new posteriori probability distribution is replaced by one quasi random sampling. The simulation results show that, The algorithm not only has good detection and tracking performance, but also has faster operation speed. Finally, the target motion and radar measurement systems are modeled. The RPF-TBDN RGPF-TBD-QMC-RGPF-TBD and SQMC-RGPF-TBD algorithms are applied to the detection of small and weak targets. The detection and tracking performances of the four algorithms under different SNR and particle number are compared and analyzed. The conclusion is as follows: QMC-RGPF-TBD algorithm has the best detection and tracking performance when the computational complexity is not considered, and SQMC-RGPF-TBD algorithm is the first choice when both real-time and performance are required.
【學(xué)位授予單位】：江蘇科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN713;TN95

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