Detection of the loose particles is urgently required in the production of sealed electronic equipment. Particle impact noise detection is a national aerospace electronic component loose particles detection method. Aiming at the problem of the large volume of the sealed electronic equipment and the difficulty in determining the position of the loose particle, this paper uses the parameter optimized kNN algorithm to locate the loose particle. After building a positioning experiment system and designing a specimen model, a multichannel loose particle signal is obtained, and the time domain and frequency domain features with excellent performance are extracted as the data set for kNN algorithm learning. The grid search method is used to find the optimal k value selection, distance measurement and weight setting of the kNN algorithm, then the kNN algorithm of parameter optimization is used to establish the plane and space positioning models respectively. The experimental results show that using the kNN algorithm of parameter optimization for loose particle positioning, the accuracy of plane and space positioning reaches 8118% and 7934% respectively, which effectively improves the positioning accuracy under traditional conditions.