Kalman Filter Approach for Radar Data Fusion Problems

In this paper, we explore how the Kalman filter method can be applied to solve radar data fusion problems. Our implementation focuses on synchronous fusion and provides relevant code examples using MATLAB to help readers better understand the practical application. The Kalman filter algorithm implemented here follows a standard prediction-correction cycle, where the prediction step estimates the system's state based on previous observations, while the correction step updates the estimate using new measurement data. Additionally, we introduce complementary information about infrared data fusion to expand readers' knowledge in multi-sensor data integration. Through reading this paper, readers will gain deeper insights into Kalman filter applications in radar data fusion and understand the advantages and limitations of different data fusion types. The code implementation demonstrates key functions such as state prediction, measurement update, and covariance matrix handling. We hope this paper will provide valuable assistance for readers' research and applications in related fields.