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Kernel‐risk‐sensitive loss (KRSL) achieves an efficient performance surface, which has been applied in the kernel adaptive filters (KAFs) successfully. However, the KRSL based KAFs use the stochastic gradient descent (SGD) method in the optimization, which usually suffer from inadequate accuracy with the slow convergence speed. In this letter, the conjugate gradient method is adopted in the optimization of KRSL function, and the problem of non‐convexity in KRSL is addressed by twice half‐quadratic (HQ) methods. For sparsification, a novel Student's‐t distribution based random Fourier feature (St‐RFF) method for performance improvement of the conventional RFF method. Thus, a novel Student's‐t distribution based random Fourier features kernel‐risk‐sensitive conjugate gradient (St‐RFFKRSCG) algorithm is proposed. Simulations on Mackey‐Glass time series prediction under non‐Gaussian noises confirm the superiorities in terms of accuracy performance, robustness, and computational cost. [ABSTRACT FROM AUTHOR]

Compressive 2D near-field millimetre-wave (MMW) imaging is a compressed sensing-based technique, which reconstructs an image from a few under-sampled measurements. Although this technique can extremely improve imaging process efficiency, resolution of the reconstructed image is still limited by the scanning grid interval, which is always hard to be improved in practice due to hardware constraint. To reconstruct a higher resolution image from under-sampled measurements, a compressive 2D near-field MMW super-resolution (SR) imaging model is established. Meanwhile, the corresponding algorithm, which is based on the primal-dual framework, is also proposed. Experimental results show that the proposed algorithm can effectively reconstruct the SR MMW image in superior performance. [ABSTRACT FROM AUTHOR]