Abstract: This paper proposes an improved multi-source constrained clustering algorithm to solve the problem of multi-sensor multi-target detection (Multi-Object Tracking/Estimation, MOTD). The MOTD problem corresponds to clustering measurements of multiple sensors without prior information such as noise and target motion models. To solve the problem that the existing algorithm is sensitive to the measurement of the selected sensor, firstly, the proposed algorithm sorts and filters the selected sensor’s measurements based on the local density; secondly, for each sorted data point, calculate the Gaussian kernel distance measured by other sensors, and each sensor returns the data point with the smallest distance; finally, calculate the number of data points within the cutoff distance, when the number of data points is greater than a given threshold, it indicates that all the data points are the target-generated measurements and formed a cluster, the center (number) of the cluster is the target position (number). Experimental results show that, compared with the existing multi-source clustering algorithm, the proposed algorithm improves the clustering accuracy and clustering speed in the scene where the sensor target detection rate is high.