Adaptive Kalman Filter Algorithm for Target Localization in Multi-Sensor Information Fusion

In multi-sensor information fusion, various methodologies exist for target localization. This paper introduces an adaptive Kalman filter algorithm capable of automatically adjusting the filter's covariance matrix according to data provided by different sensors. This approach enhances target localization accuracy, particularly when sensor data contains significant measurement errors. The algorithm implementation typically involves real-time estimation of process noise covariance using innovation sequences or residual-based adaptation techniques. Key functions would include sensor data preprocessing, innovation covariance calculation, and adaptive weighting mechanisms for optimal filter performance. Furthermore, this algorithm can be extended to other multi-sensor fusion applications such as target tracking and navigation systems. In summary, the adaptive Kalman filter algorithm serves as a valuable technique that plays a crucial role in diverse multi-sensor information fusion scenarios, with core implementation involving dynamic parameter tuning through statistical analysis of measurement discrepancies.