Yongfeng Zhi

This paper investigates methods to accelerate the identification process of nonlinear systems operating in environments contaminated by non-Gaussian impulsive noise. The modified variable step-size Nesterov accelerated gradient (MVSSNAG) mechanism is proposed within the Wiener spline adaptive filtering (SAF) framework, where half-quadratic criterion (HQC) is employed as a robust optimization criterion. The resulting spline half-quadratic criterion adaptive filter with modified variable step-size Nesterov accelerated gradient algorithm (SAF-HQC-MVSSNAG) integrates spline-based nonlinear modeling, HQC-based robustness, and momentum-accelerated stochastic iteration with adaptive step-size control. Theoretical analysis establishes the stability and convergence properties of the proposed method. The simulation results indicate that SAF-HQC-MVSSNAG outperforms conventional SAF algorithms and existing accelerated stochastic gradient methods in terms of convergence speed and steady-state error reduction. These findings indicate that the proposed algorithm provides the robust and reliable solution for adaptive filtering tasks in electronic signal processing scenarios applications.

Nonlinear dynamics are inherent in a wide range of industrial processes and mechatronic systems, where impulsive noise—arising from sensor faults, switching transients, or electromagnetic interference—poses serious challenges to accurate measurement, system identification, and subsequent control performance. To address these challenges, this work presents a robust Hammerstein spline adaptive filtering method, termed HSAF-lncosh-MVC, aimed at achieving reliable nonlinear system identification in impulsive environments. The nonlinear module employs an lncosh-based adaptation mechanism that constrains the update direction within a bounded gradient region, thereby ensuring stable parameter estimation. Concurrently, the linear module integrates a maximum versoria criterion (MVC) that suspends weight updates in the presence of pronounced outliers, thus improving resilience against impulsive disturbances. This hybrid strategy of nonlinear and linear components is implemented through stochastic gradient operations without introducing significant additional computational cost. Theoretical analysis establishes the algorithm’s convergence and steady state characteristics. Extensive simulations, covering Gaussian, symmetric α-stable, and Cauchy noise scenarios as well as complex nonlinear dynamical systems, demonstrate that the proposed method achieves accelerated convergence and reduced steady state error relative to conventional Hammerstein spline filters, while maintaining computational efficiency. These results highlight the potential of HSAF-lncosh-MVC as an effective tool for robust signal processing and nonlinear system identification in measurement and instrumentation systems operating under impulsive noise conditions.

In signal processing applications such as navigation, radar, and communications, systems often operate in complex environments characterized by strong correlations and the presence of outliers. Such compound noise conditions pose significant challenges to spline-architecture-based adaptive filtering algorithms, limiting their performance in nonlinear system identification. To mitigate the interference of colored noise and heavy-tailed non- Gaussian noise on spline adaptive filtering process for nonlinear system identification, a spline prioritization optimization lncosh- GHS adaptive filtering (SPOAF-lncosh-GHS) is proposed. The proposed algorithm adopts the lncosh function as the cost function for the linear part to optimize the adaptive weights, while employing the generalized hyperbolic secant (GHS) function for the nonlinear part to optimize the control points. These two cost functions exhibit complementary characteristics, enabling an effective trade-off balance between convergence speed and steadystate error. This design significantly enhances the robustness of the spline filtering algorithm under colored noise and heavytailed non-Gaussian noise conditions. Moreover, the convergence of the SPOAF-lncosh-GHS algorithm is theoretically analyzed. Furthermore, to overcome the issue of extended filtering time caused by a large number of filter taps, a frequency domain spline prioritization optimization lncosh-GHS adaptive filtering (FDSPOAF-lncosh-GHS) algorithm is developed. Numerical simulations demonstrate that the proposed SPOAF-lncosh-GHS algorithm and its frequency domain counterpart both exhibit superior performance.

Spline adaptive filters (SAFs) have been extensively investigated in identification systems for their real-time online processing capability. Nevertheless, their performance is often suboptimal in practical environments characterized by extreme values resulting from heavy-tailed non-Gaussian distributions. To enhance robustness against heavy-tailed non-Gaussian noise while ensuring high estimation accuracy, this paper proposes a spline prioritization optimization adaptive filter algorithm based on the lncosh algorithm, termed SPOAF-lncosh. Furthermore, to provide a theoretical basis for the proposed SPOAF-lncosh, a detailed learning rate range analysis based on statistical characteristics is performed. Moreover, simulations are compared with other similar algorithms, and it is verified that the convergence performance of the SPOAF-lncosh algorithm is better with or without heavy-tailed non-Gaussian noise, while maintaining a relatively short computation time. Specifically, under the alpha-stable noise (α = 1.2) in Experiment 2, the steady-state error of the SPOAF-lncosh algorithm reaches –20.00 dB, representing an improvement of about 1.1 dB compared to traditional SAF-type algorithms. In the Cauchy noise with a sudden change of Experiment 3, the maximum difference in steady-state error among the algorithms based on the SPOAF architecture is 0.3049 dB, with the SPOAF-lncosh algorithm achieving a steady-state error of -29.0661 dB. In addition, the SPOAF-lncosh algorithm has a runtime comparable to that of stochastic gradient-based filters and is approximately twice as fast as improved stochastic gradient algorithms, such as SAF-ARC-MMSGD and SAF-LHC-MNAG. The application of the second-order electro-hydraulic servo system in aeroengines under alpha heavy-tailed non-Gaussian noise also proves the feasibility of SPOAF-lncosh.

Differential beamforming, with its compact size and high directivity factor (DF), offers significant advantages for addressing various acoustic problems. While white noise gain (WNG) serves as a fundamental performance criterion for the robustness of differential microphone arrays (DMAs), simultaneously achieving both high WNG and frequency-invariant DF remains a significant challenge. This study presents a new method for the design of nonuniform linear robust DMAs. Our method effectively eliminates high-frequency extra nulls in the mainlobe of robust DMAs, thus preserving robustness and ensuring both frequency-invariant DF and consistent mainlobe width. The proposed approach achieves a WNG comparable to the maximum WNG method while mitigating its shortcomings in high-frequency, frequency-dependent variations of mainlobe width and DF, demonstrating its potential to enhance acoustic performance in complex environments.

The achievement of rapid and reliable image object tracking has long been crucial and challenging for the advancement of image-guided technology. This study investigates real-time object tracking by offering an image target based on nuclear correlation tracking and detection methods to address the challenge of real-time target tracking in complicated environments. In the tracking process, the nuclear-related tracking algorithm can effectively balance the tracking performance and running speed. However, the target tracking process also faces challenges such as model drift, the inability to handle target scale transformation, and target length. In order to propose a solution, this work is organized around the following main points: this study dedicates its first part to the research on kernelized correlation filters (KCFs), encompassing model training, object identification, and a dense sampling strategy based on a circulant matrix. This work developed a scale pyramid searching approach to address the shortcoming that a KCF cannot forecast the target scale. The tracker was expanded in two stages: the first stage output the target’s two-dimensional coordinate location, and the second stage created the scale pyramid to identify the optimal target scale. Experiments show that this approach is capable of resolving the target size variation problem. The second part improved the KCF in two ways to meet the demands of a long-term object tracking task. This article introduces the initial object model, which effectively suppresses model drift. Secondly, an object detection module is implemented, and if the tracking module fails, the algorithm is redirected to the object detection module. The target detection module utilizes two detectors, a variance classifier and a KCF. Finally, this work includes trials on object tracking experiments and subsequent analysis of the results. Initially, this research provides a tracking algorithm assessment system, including an assessment methodology and the collection of test videos, which helped us to determine that the suggested technique outperforms the KCF tracking method. Additionally, the implementation of an evaluation system allows for an objective comparison of the proposed algorithm with other prominent tracking methods. We found that the suggested method outperforms others in terms of its accuracy and resilience.

Radar detection is a technology frequently used to detect objects and measure the range, angle, or velocity of those objects. Several studies have been performed to improve the accuracy and performance of detection methods, but they encountered a strong challenge, which was the minimization of false alarms and the distinguishing of real targets from false alarms, especially in nonhomogeneous environments. We propose a new detection method that uses time-frequency analysis tools to improve detection performance and maintain a low constant false alarm rate. Different from existing works, this paper combines the clutter map constant false alarm rate technique with the Gabor transform for accurate target detection in cluttered environments. We suggest the combination of a CFAR detector with a time-frequency method that enables us to tackle challenging scenarios involving near targets. The proposed method allows for locating the exact position of the target by reducing the impact of clutter and maintaining a low rate of false alarms, while the Gabor transform facilitates the extraction of pertinent target characteristics and improves differentiation from clutter. Through experiments and simulations in different scenarios and clutter models, we demonstrate that the method is efficient in measurements and performs well in cluttered environments. This research has a major impact on signal processing and significantly improves target detection in cluttered environments, allowing this method to be deeply developed and implemented.