Improved Target Tracking and Fusion Using Optimally Quantized Measurement Channels

Abstract

Nowadays, autonomous underwater vehicle (AUV) technologies provide localization of AUV s, high-precision 3D measurement mapping, and underwater target tracking. Usually, the AUV consists of various sensors to acquire the dense measurements of the underwater scene to perform target tracking and functionalities. In in-water mobility, the bandwidth is a significant bottleneck, allowing communication with other AUV s and performing centralized target tracking and fusion. Since the communication modules within the AUV are compact, low power, and have low bandwidth, the quantized measurements are transmitted to the fusion center (FC). The sensing devices provide different measurements like range, range rate, azimuth, elevation, and directional cosines corresponding to the scene. Whereas the range measurements are in meters, azimuth measurements range from 0 to 360°• Hence, using a single quantizer with a predefined step size leads to tremendous errors. This paper proposes to deploy an optimal quantizer for every measurement channel and then transmit it to the FC. To explicitly study the quantization effect, we have used linear and optimal quantization techniques which can adaptively choose the levels of the measurements. The extended Kalman filter (EKF) in combination with correlation-free covariance intersection (CI) fusion algorithm is used to attain the global track information. The performance of the proposed method is quantified using the position root mean square error (PRMSE) and compared with the no-quantization state-of-art. © 2023 IEEE.