Innovative Techniques for Error Mitigation in Target Tracking via Kalman Filter with UTD and Hamming Coding

This paper investigates an innovative method to mitigate errors in target tracking by integrating the uniform theory of diffraction (UTD) and Kalman filtering, addressing Gaussian noise and diffraction effects. The proposed technique employs Hamming code to encode measurements before applying the Kalman filter, reducing the impact of noise and errors on the measurements. Our integrated approach reduces tracking errors by modelling and mitigating signal distortions associated with diffraction scenarios and noise with precision. The proposed solution undergoes thorough validation through extensive simulations and empirical analyses. Results show that the technique leads to a substantial improvement in tracking accuracy, a reduction in noise and diffraction effects, and an increase in reliability and performance across diverse operational environments.