A High-Precision Collaborative Positioning and Track Smoothing Method for Passive Reconnaissance

Passive reconnaissance systems typically rely on direction-finding, cross-localisation or time-difference-of-arrival (TDOA) techniques for collaborative positioning, along with polynomial fitting for trajectory smoothing. However, traditional methods have high requirements for data integrity and consistency, resulting in poor positioning accuracy and continuity of trajectory. To address these limitations, this study introduces an advanced approach based on neural networks and sliding weighted mean filtering. This enables high-precision collaborative positioning and smooth trajectory estimation. After sufficient training and validation with actual measured data, the method proposed in this paper can effectively realise multi-station passive reconnaissance data fusion and trajectory fitting to achieve high-precision positioning.