Infrared small target detection technology has sufficient applications in many engineering fields, such as infrared early warning, infrared tracking, and infrared reconnaissance. Due to the tiny size of the infrared small target and the lack of shape and texture information, existing methods often leave residuals or miss the target. To address these issues, a novel method based on a non-overlapping patch (NOP) joint l₀-l₁ norm is proposed with the introduction of sparsity regularized principal component pursuit (SRPCP). The NOP model makes the patch lighter in the first place, reducing time consumption. The adoption of the l₀ norm enhances the sparsity of the target, while the adoption of the l₁ norm enhances the robustness of the algorithm under clutter. As a smart optimization method, SRPCP solves the NOP model fittingly and achieves stable separation of low-rank and sparse components, thereby improving detection capacity while suppressing the background efficiently. The proposed method ultimately yielded favorable detection results. Adequate experiment results demonstrate that the proposed method is competitive in terms of background suppression and true target detection with respect to state-of-the-art methods. In addition, our method also reduces the computational time.

Aiming at the strong nonlinear friction caused by time-varying friction torque in horizontal optoelectronic tracking platforms, this paper proposes a control method combining Koopman-based nonlinear friction compensation and multi-objective optimized high-type control. The high-type control is embedded into the velocity loop of the traditional three-closed-loop structure to strengthen the low-frequency error suppression capability. High-type controller parameters are intelligently optimized via a multi-objective optimization method, while satisfying the constraints imposed by stability margin, bandwidth and time-domain performance. Nonlinear friction dynamics are lifted and linearized via the Koopman operator, whereby a high-precision model-based feedback compensation framework is established. The low-speed sinusoidal tracking results of the platform show that the proposed composite control scheme can effectively reduce the direction reversal errors during low-speed crawling.