Abstract: The existing multi-sensor multi-target tracking algorithms are mostly based on Markov-Bayes model, which requires prior information such as target motion, clutter, and sensor detection probability, but in harsh environments, these prior information are not accurate and lead to a decrease in target tracking accuracy. To solve the MTT problem in such situation, we propose an efficient distributed multi-target tracking algorithm, which uses a flooding consensus algorithm to iteratively transmit and share the measurement set information between sensors in the distributed network, and cluster the measurement set through an Improved density peaks clustering (IDPC) algorithm. The number of clusters obtained by clustering is the number of targets, and the center of the clusters is target’s position. We compare the IDPC algorithm with cutting edge distributed probability density hypothesis (PHD) filters in three scenarios, the experimental results prove the effectiveness and reliability of the IDPC algorithm.