基于粒子濾波的檢測前跟蹤算法研究
發(fā)布時間：2019-06-07 14:51
【摘要】： 弱小目標(biāo)的檢測與跟蹤是紅外預(yù)警系統(tǒng)、精確制導(dǎo)系統(tǒng)、衛(wèi)星遙感系統(tǒng)中的一項(xiàng)關(guān)鍵技術(shù)。在長距離衰減和強(qiáng)噪聲影響下,傳感器接收的目標(biāo)信噪比極低,此時傳統(tǒng)的目標(biāo)檢測與跟蹤方法已很難滿足要求。近年來出現(xiàn)的檢測前跟蹤(TBD)方法為解決此問題提供了一條有效途徑,這種方法集檢測和跟蹤于一體,在充分利用未經(jīng)閾值化處理的傳感器數(shù)據(jù)的基礎(chǔ)上,通過時間積累目標(biāo)能量,從而提高信噪比,實(shí)現(xiàn)對弱小目標(biāo)的檢測與跟蹤。 基于粒子濾波(PF)的TBD算法性能優(yōu)越,但粒子濾波一般需要大量的隨機(jī)樣本才能保證其性能,而大量隨機(jī)樣本的預(yù)測、更新和重采樣計算使得粒子濾波很難滿足工程上的實(shí)時性要求。本文重點(diǎn)研究了基于粒子濾波的TBD算法,采用多種技術(shù)減輕算法的計算負(fù)擔(dān),提高算法的實(shí)時性。 首先,通過對紅外弱小目標(biāo)模型研究分析,提出一個基于邊緣化粒子濾波的TBD算法。該算法的特點(diǎn)在于采用邊緣化方法,把目標(biāo)狀態(tài)中具有線性高斯特征的目標(biāo)速度狀態(tài)分離出來,對其使用線性最優(yōu)的卡爾曼濾波,而目標(biāo)位置、強(qiáng)度等非線性狀態(tài)則仍用粒子濾波處理。這樣不僅降低了粒子濾波估計狀態(tài)的維數(shù),大大減少了計算量,而且還提高了算法在低信噪比下的檢測性能和跟蹤精度。 其次,用一種誤差收斂更快的擬蒙特卡羅(QMC)積分替代粒子濾波中傳統(tǒng)的蒙特卡羅(MC)積分方法,提出一個改進(jìn)算法:基于擬蒙特卡羅的高斯粒子濾波(QMC-GPF)。由于QMC積分能用較少的、分布規(guī)整的樣本點(diǎn)達(dá)到MC積分的精度,該算法能在保證精度的前提下節(jié)省了大量計算負(fù)擔(dān)。 最后,在QMC-GPF算法的基礎(chǔ)上,利用濾波狀態(tài)協(xié)方差矩陣在跟蹤過程中的收斂特性構(gòu)建判斷邏輯,實(shí)現(xiàn)目標(biāo)檢測。算法結(jié)構(gòu)簡單,計算量小,仿真實(shí)驗(yàn)和實(shí)測數(shù)據(jù)實(shí)驗(yàn)顯示,該算法對3dB以上的目標(biāo)具有良好的跟蹤檢測能力。
[Abstract]:The detection and tracking of weak and small targets is a key technology in infrared early warning system, precision guidance system and satellite remote sensing system. Under the influence of long distance attenuation and strong noise, the signal-to-noise ratio (SNR) of the target received by the sensor is very low, so the traditional target detection and tracking method is difficult to meet the requirements. In recent years, the pre-detection tracking (TBD) method provides an effective way to solve this problem. This method integrates detection and tracking, and makes full use of the sensor data without threshold processing. The energy of the target is accumulated by time, so as to improve the signal-to-noise ratio (SNR) and realize the detection and tracking of weak and small targets. The TBD algorithm based on particle filter (PF) has excellent performance, but particle filter generally needs a large number of random samples to ensure its performance, and a large number of random samples predict. Update and resampling calculation make it difficult for particle filter to meet the real-time requirements of engineering. In this paper, the TBD algorithm based on particle filter is studied. A variety of techniques are used to reduce the computational burden of the algorithm and improve the real-time performance of the algorithm. Firstly, through the research and analysis of infrared small and weak target model, a TBD algorithm based on marginalized particle filter is proposed. The characteristic of this algorithm is that the target speed state with linear Gao Si characteristics in the target state is separated by using the marginalization method, and the linear optimal Kalman filter is used for the target state, and the target position is used. The nonlinear states such as intensity are still treated by particle filter. This not only reduces the dimension of particle filter estimation state and greatly reduces the amount of computation, but also improves the detection performance and tracking accuracy of the algorithm at low signal-to-noise ratio (SNR). Secondly, a quasi-Monte Carlo (QMC) integral with faster error convergence is used to replace the traditional Monte Carlo (MC) integral method in particle filter, and an improved algorithm is proposed: Gao Si particle filter (QMC-GPF) based on quasi-Monte Carlo. Because the QMC integral can be used less and the distributed sample points can achieve the accuracy of MC integral, the algorithm can save a lot of computational burden on the premise of ensuring the accuracy. Finally, based on the QMC-GPF algorithm, the judgment logic is constructed by using the convergence characteristics of the filtered state covariance matrix in the tracking process, and the target detection is realized. The algorithm has the advantages of simple structure and small amount of computation. Simulation experiments and measured data experiments show that the algorithm has good tracking and detection ability for targets above 3dB.
【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2009
【分類號】：TP391.41
本文編號：2494880
[Abstract]:The detection and tracking of weak and small targets is a key technology in infrared early warning system, precision guidance system and satellite remote sensing system. Under the influence of long distance attenuation and strong noise, the signal-to-noise ratio (SNR) of the target received by the sensor is very low, so the traditional target detection and tracking method is difficult to meet the requirements. In recent years, the pre-detection tracking (TBD) method provides an effective way to solve this problem. This method integrates detection and tracking, and makes full use of the sensor data without threshold processing. The energy of the target is accumulated by time, so as to improve the signal-to-noise ratio (SNR) and realize the detection and tracking of weak and small targets. The TBD algorithm based on particle filter (PF) has excellent performance, but particle filter generally needs a large number of random samples to ensure its performance, and a large number of random samples predict. Update and resampling calculation make it difficult for particle filter to meet the real-time requirements of engineering. In this paper, the TBD algorithm based on particle filter is studied. A variety of techniques are used to reduce the computational burden of the algorithm and improve the real-time performance of the algorithm. Firstly, through the research and analysis of infrared small and weak target model, a TBD algorithm based on marginalized particle filter is proposed. The characteristic of this algorithm is that the target speed state with linear Gao Si characteristics in the target state is separated by using the marginalization method, and the linear optimal Kalman filter is used for the target state, and the target position is used. The nonlinear states such as intensity are still treated by particle filter. This not only reduces the dimension of particle filter estimation state and greatly reduces the amount of computation, but also improves the detection performance and tracking accuracy of the algorithm at low signal-to-noise ratio (SNR). Secondly, a quasi-Monte Carlo (QMC) integral with faster error convergence is used to replace the traditional Monte Carlo (MC) integral method in particle filter, and an improved algorithm is proposed: Gao Si particle filter (QMC-GPF) based on quasi-Monte Carlo. Because the QMC integral can be used less and the distributed sample points can achieve the accuracy of MC integral, the algorithm can save a lot of computational burden on the premise of ensuring the accuracy. Finally, based on the QMC-GPF algorithm, the judgment logic is constructed by using the convergence characteristics of the filtered state covariance matrix in the tracking process, and the target detection is realized. The algorithm has the advantages of simple structure and small amount of computation. Simulation experiments and measured data experiments show that the algorithm has good tracking and detection ability for targets above 3dB.
【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2009
【分類號】：TP391.41
【引證文獻(xiàn)】
相關(guān)碩士學(xué)位論文 前7條
1 楊心力;基于粒子濾波的視頻目標(biāo)跟蹤研究[D];湘潭大學(xué);2011年
2 郭輝;基于非線性濾波的目標(biāo)跟蹤算法研究[D];西安電子科技大學(xué);2010年
3 李倩;基于FPGA的非線性濾波算法實(shí)現(xiàn)研究[D];西安電子科技大學(xué);2010年
4 郭姍姍;基于改進(jìn)粒子濾波的紅外弱小目標(biāo)檢測前跟蹤算法[D];哈爾濱工程大學(xué);2012年
5 鄒其兵;多伯努利濾波器及其在檢測前跟蹤中的應(yīng)用[D];西安電子科技大學(xué);2012年
6 劉錚;自適應(yīng)顏色直方圖的粒子濾波算法[D];武漢理工大學(xué);2012年
7 楊瑞興;基于粒子濾波的雷達(dá)弱目標(biāo)TBD算法研究[D];西安電子科技大學(xué);2013年
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