改进的多模型粒子滤波弱小目标检测前跟踪方法
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摘要
检测前跟踪是解决目标信噪比(SNR)较低的情况下目标检测与跟踪的有效方法。目前主要的研究方法有多模型粒子滤波弱小目标检测前跟踪方法(MMPF-TBD),该方法在目标出现较强的机动时,目标的检测性能会严重下降甚至出现漏检。该文针对该问题提出了一种改进的多模型粒子滤波弱小目标检测前跟踪方法(IMMPF-TBD),该方法可以降低模型之间转移计算复杂度,并且有效地提高模型的使用效率和目标的检测性能。仿真实验结果表明相比于MMPF-TBD,IMMPF-TBD能够有效地提高机动目标的检测性能。
Tarck-before-detect is an effective method to solve the target detection and tracking under the conditions of low SNR. At present,the main research method for maneuvering weak target is multiple model particle filter trackbefore-detect algorithm MMPF-TBD. In this way,when the target has a strong maneuver,the detection performance of the target will be seriously degraded or even missed. Therefore,this paper proposes an improved multi-model particle filter track-before-detect IMMPF-TBD algorithm. This method can solve the transfer computational complexity between models and effectively improve the efficiency of the model. This method can reduce the transfer computational complexity between models and effectively improve the efficiency of the model and the detection performance of the target. Simulation results show that IMPM-TBD can effectively improve the detection performance of maneuvering targets compared to MMPF-TBD.
Tarck-before-detect is an effective method to solve the target detection and tracking under the conditions of low SNR. At present,the main research method for maneuvering weak target is multiple model particle filter trackbefore-detect algorithm MMPF-TBD. In this way,when the target has a strong maneuver,the detection performance of the target will be seriously degraded or even missed. Therefore,this paper proposes an improved multi-model particle filter track-before-detect IMMPF-TBD algorithm. This method can solve the transfer computational complexity between models and effectively improve the efficiency of the model. This method can reduce the transfer computational complexity between models and effectively improve the efficiency of the model and the detection performance of the target. Simulation results show that IMPM-TBD can effectively improve the detection performance of maneuvering targets compared to MMPF-TBD.
引文
[1] Rollason M,Salmond D.Particle filter for track-before-detect of a target with unknown amplitude viewed against a structured scene[J].Iet Radar Sonar&Navigation,2018,12(6):603-609.
[2]高洁,杜劲松,张清石,等.一种基于动态规划的机动目标检测前跟踪方法[J].电子技术应用,2018(3):64-67.
[3]朱源才,王红,曲智国,等.图像处理Hough变换的慢小目标航迹起始方法[J].现代防御技术,2018(1):156-161.
[4]杨丹,姬红兵,张永权.未知杂波条件下样本集校正的势估计概率假设密度滤波算法[J].电子与信息学报,2018(4):912-919.
[5]胡洪涛,敬忠良,胡士强.基于辅助粒子滤波的红外小目标检测前跟踪算法[J].控制与决策,2005,20(11):1208-1211.
[6] Rollason M,Salmond D.Particle filter for track-before-detect of a target with unknown amplitude viewed against a structured scene[J].Iet Radar Sonar&Navigation,2018,12(6):603-609.
[7]黄大羽,薛安克,郭云飞.一种基于MMPF-TBD的机动弱目标检测方法[J].光电工程,2009,36(11):29-34.
[8]耿林玉,吴楠,孟凡坤,等.基于非对称交互多模型算法的上升段弹道估计[J].指挥控制与仿真,2017,39(5):120-125.
[9]吴瑕,陈建文,鲍拯,等.混合估计多模粒子滤波的机动弱目标检测前跟踪算法[J].控制与决策,2014(3):523-527.
[10]龚亚信,杨宏文,胡卫东,等.基于多模粒子滤波的机动弱目标检测前跟踪[J].电子与信息学报,2008,30(4):941-944.
[11] Wu D,Zhang L,Lin L.Based on the moving average and target motion information for detection of weak small target[C]//International Conference on Intelligent Transportation,Big Data&Smart City,IEEE,2018:641-644.
[12]李渝,黄普明,林晨晨.基于复似然比的粒子滤波改进算法[J].现代雷达,2016,38(1):47-50.
[13] Mingjie L,Xiaofei L,Fuquan Z,et al.Multi target detection and tracking algorithm based on particle filtering and background subtraction[J].Application Research of Computers,2018.
[2]高洁,杜劲松,张清石,等.一种基于动态规划的机动目标检测前跟踪方法[J].电子技术应用,2018(3):64-67.
[3]朱源才,王红,曲智国,等.图像处理Hough变换的慢小目标航迹起始方法[J].现代防御技术,2018(1):156-161.
[4]杨丹,姬红兵,张永权.未知杂波条件下样本集校正的势估计概率假设密度滤波算法[J].电子与信息学报,2018(4):912-919.
[5]胡洪涛,敬忠良,胡士强.基于辅助粒子滤波的红外小目标检测前跟踪算法[J].控制与决策,2005,20(11):1208-1211.
[6] Rollason M,Salmond D.Particle filter for track-before-detect of a target with unknown amplitude viewed against a structured scene[J].Iet Radar Sonar&Navigation,2018,12(6):603-609.
[7]黄大羽,薛安克,郭云飞.一种基于MMPF-TBD的机动弱目标检测方法[J].光电工程,2009,36(11):29-34.
[8]耿林玉,吴楠,孟凡坤,等.基于非对称交互多模型算法的上升段弹道估计[J].指挥控制与仿真,2017,39(5):120-125.
[9]吴瑕,陈建文,鲍拯,等.混合估计多模粒子滤波的机动弱目标检测前跟踪算法[J].控制与决策,2014(3):523-527.
[10]龚亚信,杨宏文,胡卫东,等.基于多模粒子滤波的机动弱目标检测前跟踪[J].电子与信息学报,2008,30(4):941-944.
[11] Wu D,Zhang L,Lin L.Based on the moving average and target motion information for detection of weak small target[C]//International Conference on Intelligent Transportation,Big Data&Smart City,IEEE,2018:641-644.
[12]李渝,黄普明,林晨晨.基于复似然比的粒子滤波改进算法[J].现代雷达,2016,38(1):47-50.
[13] Mingjie L,Xiaofei L,Fuquan Z,et al.Multi target detection and tracking algorithm based on particle filtering and background subtraction[J].Application Research of Computers,2018.