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Joint tracking and identification of the unresolved towed decoy and aircraft using the labeled particle probability hypothesis density filter

机译:使用标记的粒子概率假设密度滤波器对未解决的拖曳诱饵和飞机进行联合跟踪和识别

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For the distance and velocity deception from the new towed decoy, echo signal from the target and decoy appear as one target on time domain and frequency domain because of aliasing. Therefore, independent measurements for the decoy and aircraft are unavailable for conventional algorithm, neither identification and tracking. In this paper is proposed a new algorithm for joint identification and tracking of the decoy and aircraft which are unresolved within radar beam, innovations for which include: First, construction of echo signal model in the three interfering stages. Once stage decided with the decoy presence detection algorithm, the proposed particle filter based measurement generating algorithm sequentially estimates the aircraft and decoy character parameters on different stages, separating the aircraft signal and decoy signal. Secondly, based on the improved labeled particle probability hypothesis density (IL-P-PHD) filter, an algorithm for joint identification and tracking of vertically the unresolved aircraft and decoy is proposed, realizing real time identification and sequential estimation of movement state. Simulation experiment demonstrates that the proposed algorithm behaves in a manner consistent with our expectations.
机译:对于新拖曳诱饵的距离和速度欺骗,由于混叠,来自目标和诱饵的回波信号在时域和频域上显示为一个目标。因此,常规算法无法获得诱饵和飞机的独立测量结果,而无法进行识别和跟踪。本文提出了雷达波束内未解决的诱饵和飞机联合识别和跟踪的新算法,其创新之处包括:第一,在三个干扰阶段建立回波信号模型。一旦通过诱饵存在检测算法确定了阶段,建议的基于粒子滤波器的测量生成算法便会在不同阶段依次估计飞机和诱饵特征参数,从而分离出飞机信号和诱饵信号。其次,基于改进的带标记粒子概率假设密度(IL-P-PHD)滤波器,提出了一种对未分辨飞机和诱饵进行垂直联合识别和垂直跟踪的算法,实现了运动状态的实时识别和顺序估计。仿真实验表明,所提算法的行为符合我们的期望。

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