Enhanced Vehicle Tracking with Discretization Error Control for OFDM-based Radar System

Abstract

Orthogonal frequency division multiplexing (OFDM)-based radar systems are considered a key technology for enhancing spectral efficiency in 6G wireless networks by enabling simultaneous target detection and data transmission using shared frequency resources. However, the discrete sample acquisition process inherent in OFDM radar introduces quantization errors in the measured distance and relative velocity. The quantization errors pose significant challenges for vehicle tracking applications, as directly relying on discretized measurements can lead to considerable inaccuracies in position and velocity estimation. The impact of the discretization errors is especially pronounced in low-resolution scenarios, where coarse quantization bins severely limit measurement precision. In this paper, we propose a vehicle tracking algorithm designed to correct discretization errors arising during the radar sample acquisition process. The algorithm adaptively computes measurement correction factors by leveraging the statistical properties of quantization bins in the two-dimensional radar image profile. The proposed error correction technique is integrated into the measurement update step of the Kalman filter-based vehicle tracking framework, enhancing state estimation accuracy. Furthermore, we investigate how radar resolution constraints affect vehicle state estimation accuracy by analyzing variations in the derived measurement correction factors. Numerical results demonstrate that the estimation performance of the proposed algorithm outperforms that of existing algorithms that rely on uncorrected discrete measurement samples.