A Subspace-Based Azimuth-Range-Doppler Estimator with Super-Resolution for OFDM Radar

Orthogonal frequency division multiplexing (0 FD M) radar plays an significant role in target sensing applications. For example, it embodies characteristics conducive to both communication and sensing, making it suitable for integrated sensing and communication (ISAC) applications. In OFDM radar, high-resolution angle or range-Doppler estimation is an important requirement. Existing super-resolution methods focus on the estimation of single or dual parameters. However, when we perform super-resolution estimation on angle-range-Doppler parameters of multiple targets simultaneously, intricate matching issues necessitate careful handling. To address this problem, in this paper, we propose a novel estimator aim to achieve multi-target and multi-dimensional super-resolution parameter estimation in monostatic OFDM radar. We establish multiple-antenna signal reconstruction observation matrices, and design a two-stage smoothing operation to increase the number of snapshots. We derived a spectral function via a subspace-based method and obtained estimates of multi-dimensional parameters simultaneously, effectively avoid the parameter matching problem. Moreover, this estimator is adaptable to various communication protocols and easy to expand to complex array configurations, making it suitable for future ISAC applications. Numerical results demonstrate that the proposed method can achieve high accuracy even under conditions of limited bandwidth and small aperture.