Joint Estimation of Target Range and Velocity for 5G Radar Based on Compressive Sensing

‍ ‍With the development of 5G/6G technology in wireless communication， the use of 5G radar to detect low-altitude targets in urban environments has given rise to new business activities， and target parameter estimation has been one of the important tasks of 5G radar target detection. Currently， the target parameter estimation for 5G radar uses the high-resolution processing method based on subspace projection. Subspace-based methods require a large segment of consecutive samples and suffer from high computational complexity. The target parameter estimation performance of subspace-based methods is not ideal for practical engineering. Moreover， high-correlation targets necessitate correlation processing. In this study， the target range and Doppler estimation are reduced to sparse representation using the sparsity of the target range in a time domain and the sparsity of the target Doppler in a frequency domain. Further， a joint estimation method of target range and velocity for 5G radar based on compressed sensing was proposed to achieve radar sensing along with wireless communication. First， a signal model was formulated to represent the target echo， while the transmit waveform made use of orthogonal frequency division multiplexing communication signals. Subsequently， the proposed method achieved a sparse representation of the array covariance matrix based on an overcomplete basis. Then， an L1-norm-constrained optimization problem was constructed for the covariance vector. Finally， the second-order cone programming problem was efficiently solved to determine the sparse vector coefficient using the interior point method. The objective parameter estimation was achieved by jointly searching for the sparse coefficients of the array covariance vector. Compared with the subspace-based methods， the proposed method has high resolution and can estimate coherent signals based on an arbitrary array. More importantly， the proposed algorithm also provides a clear error suppression criterion when constructing optimization problems， rendering it statistically robust even in low signal-to-noise ratio （SNR） situations. Simulation results show that the proposed algorithm can achieve a jointly high-resolution estimation of target range and velocity for 5G radar without the need for decoherence processing. Moreover， the proposed algorithm can realize the target parameter estimation robustly in low-SNR environments.