Angle-association-based multi-information fusion for multi-target tracking

In multi-missile cooperative angle-based detection and tracking systems for multiple targets, the uncertainty in measurement origins inevitably leads to the generation of numerous spurious localization points during multi-path direction-finding cross-localization. These spurious points further form persistent false tracks over time sequences, severely degrading the precision performance of multi-target tracking. As the number of loitering munitions and targets increases, the spurious points generated by direction-finding cross-localization grow exponentially. Additionally, the complex battlefield electromagnetic environment introduces measurement noise and substantial clutter interference, with decoy targets even emerging in certain mission scenarios. Simultaneously, measurement errors cause direction-angle rays from loitering munitions to disperse within a localized ambiguity region after positioning for the same target. These ambiguous points overlap with spurious association points, significantly increasing the difficulty of distinguishing false locations. When clutter and decoy targets are observable, their cross-localization points exhibit geometric characteristics identical to those of true targets, forming stubborn ghost points that are indistinguishable. This imposes stricter requirements on tracking accuracy. Consequently, the rapid and accurate identification and elimination of spurious points remain a critical unresolved challenge in multi-missile multi-target angle cooperative tracking systems.

To mitigate the degradation of tracking accuracy induced by numerous false association ghost points, this paper introduced a dual-level ghost point elimination and target tracking algorithm, which integrated angular measurement with target motion characteristics. The proposed algorithm employed a cooperative localization strategy that prioritizes association before estimation. By establishing a mapping relationship between angular measurement noise and localization error, a field-of-view grid map and an energy accumulation matrix were constructed. Through a detailed analysis of the spatial geometric distribution characteristics of real targets and false association ghost points within the field of view, a novel elimination criterion based on Hough transform theory was developed, facilitating the primary coarse elimination of ghost points. Additionally, by examining the distribution characteristics of target localization ambiguity regions and motion features, a predictive tracking gate was constructed using motion parameter identification, enabling the secondary fine elimination of ghost points at the kinematic level.

In terms of tracking accuracy, the proposed algorithm demonstrates significant superiority over conventional methods in both axis-specific tracking errors and the overall Optimal Subpattern Assignment (OSPA) distance. Specifically, the proposed algorithm achieves a first-level tracking OSPA distance of 2.640 meters and a second-level tracking OSPA distance of 1.018 meters, resulting in an overall tracking accuracy improvement of approximately 61.44%. Notably, under identical experimental conditions, the average OSPA distance of the comparative algorithm is 7.644 m. Through dual-stage optimization, our algorithm achieves an 86.68% enhancement in overall tracking accuracy. Specifically, at discrete time during the experiments, quantitative comparative data reveal that the total number of associated points containing clutter within the field of view is approximately 1,476. Through primary energy matrix elimination, this number is reduced to approximately 592, achieving a first-stage rejection rate of 58.866%. Subsequent secondary prediction gate elimination further reduces the count to approximately 122, corresponding to a second-stage rejection rate of 91.723%. From the perspective of tracking effectiveness, each stage of the algorithm demonstrates progressive elimination of false associated targets at varying degrees. It is particularly noteworthy that, in terms of time complexity, the proposed algorithm incurs an 18.6% increase in computational overhead (from 0.43 s to 0.51 s). However, this is counterbalanced by a simultaneous enhancement in ghost-point elimination rate from 89.456% to 91.723% and an approximate 86.68% improvement in target tracking accuracy. Such a performance trade-off robustly validates the effectiveness and superiority of the proposed algorithm in complex environments characterized by spurious association points and clutter interference.

Therefore, this study has demonstrated the effectiveness and superiority of the proposed algorithm through simulation experiments, effectively solving the problem of false associations in multi-target tracking.