Blind-Free Range Extension of FMICW Radar for Improving Reconstruction Accuracy of Weak Targets

Existing sparsity-driven blind-free range extension method for frequency-modulated interrupted continuous-wave (FMICW) radar faces the issues of inaccurate reconstruction and sizeable computational burden. This article proposes an accuracy-improved and efficient blind-free range extension method for FMICW radar via nonuniform sampling and density-guided sparse reconstruction to address the issues mentioned above. By improving the design of nonuniform sampling sequence, the spectral anti-aliasing performance can be improved. Then, density features are used to initialize the sparse regularization parameters of each scatterer. By utilizing different sparse regularization parameters within an observation scene, the proposed density-guided sparse reconstruction method is able to suppress the nonstructured noise caused by nonuniform sampling while retaining the information of weak targets. Compared to the existing sparsity-driven blind-free range extension method for FMICW radar, the proposed method improves the reconstruction accuracy and reduces the computational burden by reducing the number of iterations. Simulations and experiments on measured FMICW radar data demonstrate the effectiveness of the proposed method.