传统的基于帧间非相参积累的检测前跟踪方法对单帧信噪比要求较高，信号有效积累时间较短。提出了一种基于相参积累的粒子滤波检测前跟踪算法，在跟踪算法部分分别对目标时延、速度和强度信息进行采样，得到目标的状态估计; 在检测算法部分根据目标时延估计重新设计匹配滤波器，并对回波信号再次进行匹配滤波，将回波信号的时延补偿掉，之后进行积累检测。仿真结果表明：在-5 dB这个极低信噪比条件下，该算法具有较高的跟踪精度，延长了信号有效积累时间，提高了信号检测性能。

The traditional track-before-detect algorithms based on non-coherent integration demand high SNR for each single frame data, and the effective time for signal integration is short. In this paper, a novel and efficient track-before-detect algorithm is proposed based on the particle filter with coherent integration. The target states are estimated by the samples of the delay, velocity, and power information of target. Then, the matched filters are redesigned according to the target delay, and the echoes are filtered again to compensate for their delay. The simulation results show that: at the low SNR of -5 dB, the algorithm still has fine tracking and signal detection performances by extending time for signal integration.