低信噪比條件下MIMO天波雷達多幀TBD方法研究
發(fā)布時間：2019-01-23 18:14
【摘要】：先檢測后跟蹤算法是傳統(tǒng)的目標跟蹤算法,利用目標檢測統(tǒng)計量高于噪聲檢測統(tǒng)計量這一特性完成對目標的檢測。然而,當積累信噪比低于13d B時,目標和噪聲的統(tǒng)計量不可分辨,導致無法發(fā)現(xiàn)目標。檢測前跟蹤(TBD,Track-Before-Detect)利用現(xiàn)有雷達系統(tǒng),能夠?qū)Χ鄮紨?shù)據(jù)進行積累,實現(xiàn)對目標的檢測。本文圍繞低信噪比條件下MIMO天波雷達多幀檢測前跟蹤方法,開展如下工作:首先,介紹了高頻雷達的噪聲模型、海雜波模型以及目標回波信號模型,并引入MIMO雷達數(shù)據(jù)模型,仿真低信噪比目標的回波譜數(shù)據(jù)。進一步的數(shù)據(jù)分析表明,檢測統(tǒng)計量的構(gòu)造和門限的求解是低信噪比條件下實現(xiàn)目標檢測的關鍵。其次,根據(jù)目標數(shù)據(jù)模型,利用對未知參數(shù)的極大似然估計參數(shù)的方法推導了GLRT(The Generalized Likelihood Ratio Test)檢測統(tǒng)計量,并且給出了門限的計算。利用信息論準則中的MDL(Minimum description length)準則,推導目標數(shù)目和方向估計算法�；贕LRT的推導,為了抑制檢測幀中高斯噪聲的干擾,提出多幀檢測——MIMO-EL算法,該算法可充分挖掘并利用陣列采樣數(shù)據(jù)的方向信息,利用最大似然估計得到信號幅度和角度的估計,進而得到目標檢測統(tǒng)計量表達式。在低信噪比條件下,該方法比單幀GLRT檢測效果更穩(wěn)健,能夠更好地抑制噪聲從而提高目標檢測能力。最后,利用目標運動特性推導檢測前跟蹤算法。根據(jù)勻速直線運動目標的軌跡呈直線的特性,給出基于Hough變換的TBD算法和基于粒子濾波的TBD算法�；贖ough變換的TBD算法的第一門限選取利用了結(jié)合GLRT檢測器、MDL檢測器以及MIMO-EL檢測器的復合單幀檢測算法。以單幀檢測統(tǒng)計量和門限來進行第一門限檢測。以第一門限檢測結(jié)果作為Hough變換TBD算法的輸入,比直接運用速度-距離譜數(shù)據(jù)作為第一門限檢測數(shù)據(jù)相比較,具有更好的低信噪比條件下的檢測能力。仿真結(jié)果表明,檢測前跟蹤算法在距離-多普勒-方向積累后的信噪比低于13d B時依舊能夠?qū)崿F(xiàn)對目標的有效檢測。
[Abstract]:The first detection and then tracking algorithm is a traditional target tracking algorithm. The target detection statistics are higher than the noise detection statistics to complete the target detection. However, when the cumulative signal-to-noise ratio (SNR) is less than 13dB, the statistics of target and noise are indistinguishable and the target cannot be found. Pre-detection tracking (TBD,Track-Before-Detect) can accumulate multiple frames of raw data and realize target detection using existing radar systems. The main work of this paper is as follows: firstly, the noise model, sea clutter model and target echo signal model of high frequency radar are introduced, and the MIMO radar data model is introduced. The echo spectrum data of low SNR target are simulated. Further data analysis shows that the construction of detection statistics and the solution of threshold are the key to achieve target detection under low signal-to-noise ratio (SNR). Secondly, according to the target data model, the GLRT (The Generalized Likelihood Ratio Test) detection statistics are derived by using the method of maximum likelihood estimation of unknown parameters, and the threshold is calculated. By using the MDL (Minimum description length) criterion in the information theory criterion, an algorithm for estimating the number and direction of targets is derived. Based on the derivation of GLRT, in order to suppress the interference of Gao Si noise in the detection frame, a multi-frame detection (MIMO-EL) algorithm is proposed, which can fully mine and utilize the direction information of array sampling data. The maximum likelihood estimation is used to estimate the amplitude and angle of the signal, and then the expression of the target detection statistic is obtained. Under the condition of low SNR, this method is more robust than single frame GLRT detection, and can suppress noise better and improve the detection ability of target. Finally, the pre-detection tracking algorithm is derived by using the moving characteristics of the target. The TBD algorithm based on Hough transform and the TBD algorithm based on particle filter are presented according to the characteristic that the trajectory of the moving target is linear. The first threshold selection of TBD algorithm based on Hough transform uses a composite single-frame detection algorithm combining GLRT detector, MDL detector and MIMO-EL detector. First threshold detection is performed with single frame detection statistics and thresholds. Using the first threshold detection result as the input of the Hough transform TBD algorithm has better detection capability under low SNR than using the velocity-range spectrum data as the first threshold detection data. Simulation results show that the pre-detection tracking algorithm can still achieve effective target detection when the signal-to-noise ratio after range-Doppler direction accumulation is less than 13dB.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN958
本文編號：2414076
[Abstract]:The first detection and then tracking algorithm is a traditional target tracking algorithm. The target detection statistics are higher than the noise detection statistics to complete the target detection. However, when the cumulative signal-to-noise ratio (SNR) is less than 13dB, the statistics of target and noise are indistinguishable and the target cannot be found. Pre-detection tracking (TBD,Track-Before-Detect) can accumulate multiple frames of raw data and realize target detection using existing radar systems. The main work of this paper is as follows: firstly, the noise model, sea clutter model and target echo signal model of high frequency radar are introduced, and the MIMO radar data model is introduced. The echo spectrum data of low SNR target are simulated. Further data analysis shows that the construction of detection statistics and the solution of threshold are the key to achieve target detection under low signal-to-noise ratio (SNR). Secondly, according to the target data model, the GLRT (The Generalized Likelihood Ratio Test) detection statistics are derived by using the method of maximum likelihood estimation of unknown parameters, and the threshold is calculated. By using the MDL (Minimum description length) criterion in the information theory criterion, an algorithm for estimating the number and direction of targets is derived. Based on the derivation of GLRT, in order to suppress the interference of Gao Si noise in the detection frame, a multi-frame detection (MIMO-EL) algorithm is proposed, which can fully mine and utilize the direction information of array sampling data. The maximum likelihood estimation is used to estimate the amplitude and angle of the signal, and then the expression of the target detection statistic is obtained. Under the condition of low SNR, this method is more robust than single frame GLRT detection, and can suppress noise better and improve the detection ability of target. Finally, the pre-detection tracking algorithm is derived by using the moving characteristics of the target. The TBD algorithm based on Hough transform and the TBD algorithm based on particle filter are presented according to the characteristic that the trajectory of the moving target is linear. The first threshold selection of TBD algorithm based on Hough transform uses a composite single-frame detection algorithm combining GLRT detector, MDL detector and MIMO-EL detector. First threshold detection is performed with single frame detection statistics and thresholds. Using the first threshold detection result as the input of the Hough transform TBD algorithm has better detection capability under low SNR than using the velocity-range spectrum data as the first threshold detection data. Simulation results show that the pre-detection tracking algorithm can still achieve effective target detection when the signal-to-noise ratio after range-Doppler direction accumulation is less than 13dB.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN958
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