High-Resolution Joint Range-Velocity Estimation for OFDM-Based Integrated Sensing and Communication

Orthogonal frequency division multiplexing (OFDM)-based integrated sensing and communication (ISAC) is promising for the future sixth-generation mobile communication systems. The joint sensing of target range and velocity is crucial in OFDM-based ISAC systems. When the targets are highly correlated with similar range and velocity, it is challenging for the conventional two-dimensional subspace-based sensing methods to achieve accurate joint range-velocity estimation (JRVE), particularly in the low signal-to-noise ratio (SNR) region. As such, this paper proposes a high-resolution JRVE method. Specifically, the proposed method first applies equal interval sampling smoothing to the observation signal, introducing ambiguity in range-velocity-induced phase pairs. It then utilizes the translation invariance of the signal subspace to extract these ambiguous phase pairs. Finally, it leverages the orthogonality between the constructed steering vector pair and the noise subspace to resolve the ambiguity and achieve accurate estimation. Under a 5G New Radio parameter setup, simulation results demonstrate that the proposed method significantly outperforms conventional methods in terms of both resolution and accuracy. At an SNR of -10 dB, the proposed method reduces the root mean square error of range and velocity estimation by 83.7 % and 87.3 %, respectively, compared to the benchmark scheme, while its computational cost is less than 50 % of that of the benchmark.