Decentralized Mobile Target Tracking Using Consensus-Based Estimation with Nearly-Constant-Velocity Modeling

TL;DR

This paper introduces a decentralized target tracking method using consensus-based estimation with nearly-constant-velocity modeling, improving accuracy and robustness in noisy, communication-constrained environments.

Contribution

It develops a novel decentralized tracking framework combining CBEF with NCV modeling and saturation filtering, enhancing robustness and scalability over existing methods.

Findings

Reduces Mean Squared Estimation Error over time

Demonstrates robustness against measurement noise

Shows scalability in decentralized networks

Abstract

Mobile target tracking is crucial in various applications such as surveillance and autonomous navigation. This study presents a decentralized tracking framework utilizing a Consensus-Based Estimation Filter (CBEF) integrated with the Nearly-Constant-Velocity (NCV) model to predict a moving target's state. The framework facilitates agents in a network to collaboratively estimate the target's position by sharing local observations and achieving consensus despite communication constraints and measurement noise. A saturation-based filtering technique is employed to enhance robustness by mitigating the impact of noisy sensor data. Simulation results demonstrate that the proposed method effectively reduces the Mean Squared Estimation Error (MSEE) over time, indicating improved estimation accuracy and reliability. The findings underscore the effectiveness of the CBEF in decentralized environments, highlighting its scalability and resilience in the presence of uncertainties.