Implementation of Kalman Filter Using TDC and PLL for Object Detection and Tracking in Signal Processing

Abstract

The measurement uncertainty of GPS receivers is dependent on a wide range of external factors, including receiver clock precision, thermal noise, atmospheric influences, and minute variations in satellite positions. Estimating hidden states precisely and accurately in the face of uncertainty is one of the main problems facing tracking and control systems. Among the most significant and widely used estimate methods is the Kalman Filter. It uses imprecise and erratic measurements to generate estimations of hidden variables. Furthermore, it forecasts the system's future condition using historical estimates. This research introduces an innovative blend of the Kalman filter with Phase-Locked Loop (PLL) and Time-to-Digital Converter (TDC) techniques, aiming to elevate object detection and tracking capabilities within signal processing. By combining the precision of PLL and TDC methodologies with the predictive nature of the Kalman filter, this approach seeks to significantly improve the accuracy and consistency of localizing and tracking objects. Leveraging PLL’s phase coherence maintenance and TDC’s precise time measurements complements the Kalman filter’s predictive strengths, forming a comprehensive framework for robust object detection and tracking. This blend of methods proposes a promising solution to tackle the challenges in accurately detecting and tracking objects. It offers a practical approach suitable for real-world applications that require precision, reliability, and quick responses. This research establishes a strong starting point for further investigation and practical use, advancing the methodologies for detecting and tracking objects in signal processing.