In addressing the demand for efficiency analysis in multi-radar collaborative target tracking，the mainstream fusion algorithms based on density estimation are modeled and their limitations are analyzed based on the entropy error theory of Gaussian-Uniform mixture distribution. A multi-radar collaborative tracking method based on Bernoulli filtering of sequential Monte Carlo approximation and K-rank fusion is designed. Additionally，an error decreasing function is adopted as an indicator to quantitatively analyze the collaboration effectiveness of multi-radar target tracking. The simulation experiments show that:1) When the process noise of multi-radar target tracking is either too high or too low，there is negligible improvement in fusion tracking performance，indicating minimal efficiency gains from increasing radar quantity under such conditions; and 2) When the process noise is appropriate，there is significant improvement in fusion tracking performance，and the improvement showing a positive decreasing relationship with the increase of number of radars. The study provides a guidance for determining the number of radar nodes.