This study aims to reduce the influence of measurement errors on the positioning of a multi-target in dual-stations. By using the spatio-temporal distribution characteristics of the motion track points of a target over a short time period， an infrared motion multi-target dual-station positioning method is proposed based on the maximum track density estimation. First， single frame multi-target matching is performed based on the elevation difference along direction-finding rays of dual-stations. Then， based on the two-dimensional direction histogram， the target track direction is preliminarily estimated， following which the maximum density of the target track direction is determined based on the mean shift. Finally， the authenticity of the track point is validated based on the target track direction to reduce the influence of measurement errors on the target positioning result. The experimental results reveal that the proposed method effectively eliminates the mismatch point and reduces the error deviation point. The maximum fit error of the track is less than 0.5 m， and the average fit error is less than 0.3 m， which represent improvements on existing algorithms. For targets that exhibit both mismatched points and larger error deviations compared with those of the histogram method， the maximum fitting error of the proposed method is reduced by more than 50%， and the average fitting error is reduced by 27%. Thus， the proposed method can effectively reduce the positioning error， which has important applications in military and civilian fields， such as three-dimensional positioning， target prediction， and hooting training evaluation.