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4th Asia-Pacific Artificial Intelligence and Big Data Forum, AIBDF 2024

作者： Li, Mengjiao Han, Dan Tianjin Key Laboratory for Advanced Signal Processing Civil Aviation University of China Tianjin China Tianjin Air Traffic Control Sub-Branch North China Regional Air Traffic Management Bureau CAAC Tianjin China

ISBN: (纸本)9798400710865

Advanced Receiver Autonomous Integrity Monitoring (ARAIM) algorithm is one of the satellite navigation integrity enhancement technologies to assist civil aviation approach. The basic ARAIM algorithm distributes the integrity risk (IR) and continuity risk (CR) equally, leading to a conservative vertical protection level (VPL) and limiting the availability of ARAM. In this paper, a dynamic risk optimization allocation method PID-based search algorithm (PSA) is proposed. Using the minimum VPL as the optimization objective, iteratively search for the best risk allocation method assigned to each subset of faults while satisfying the constraints. And different optimization variables and objective functions are constructed to analyze the extent to which optimizing the allocation of both IR and CR risks versus optimizing the allocation of a single risk affects the level of protection. Experiments are conducted with static data from multi-GNSS experiment (MGEX) stations and vehicle-mounted dynamic data, and the experimental results show that under GPS and BDS dual-constellation observation, the ARAIM risk optimization allocation method based on the PSA algorithm can effectively reduce the VPL. The smallest VPL is obtained by optimizing the allocation of two risks at the same time, with an average optimization rate of up to 16.1%. And optimizing IR plays a more critical role in the overall protection level optimization, the magnitude of optimizing IR accounts for 68% of the complete optimization. © 2024 Copyright held by the owner/author(s).

关键词： Optimization algorithms

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Mainlobe Deceptive Interference Suppression with Planar Frequency Diverse Array MIMO Radar

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Zhang, Mengdi Lu, Jiahao Xu, Jingwei Li, Shiyin China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Xidian University National Laboratory of Radar Signal Processing Xi'an710071 China

Frequency diversity array (FDA) is capable of suppressing mainlobe deceptive interference with delays exceeding one pulse relative to the true target, known as inter-pulse interference. However, current FDA-based interference suppression methods are ineffective when both inter-pulse and intra-pulse interferences coexist. To this end, we propose a two-stage mainlobe deceptive interference suppression method with planar FDA multiple-input-multiple-output (PFDA-MIMO) radar: 1) Horizontal transmit spatial frequency compensation (HTSFC) is applied using the range information of each range bin to differentiate between inter-pulse interferences and target. Subsequently, data-independent beamforming is performed to suppress inter-pulse interferences in horizontal spatial frequency domain;2) To distinguish the target from intra-pulse interferences, vertical transmit spatial frequency compensation (VTSFC) is applied utilizing the initial range of the range bin containing target. Afterwards, an adaptive beamforming (ABF) approach based on transmit-receive spatial smoothing (TRSS) is applied to suppress intra-pulse interference while avoiding target cancellation. Furthermore, the frequency increments in both rows and columns of PFDA are elaborately designed to effectively distinguish between true target and interferences. Extensive simulations are conducted to evaluate the excellent suppression effect of the proposed scheme against mainlobe deceptive interference. © 2025 IEEE.

关键词： Frequency domain analysis

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Hybrid signal fusion for target detection in distributed PA-MIMO radar systems on moving platforms

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Zhou, Dingsen Yang, Minglei Lian, Hao Greco, Maria Sabrina Gini, Fulvio Xidian University National Key Laboratory of Radar Signal Processing Shaanxi Xi'an710071 China Science and Technology on Underwater Test and Control Laboratory Dalian116013 China University of Pisa Department of Information Engineering Pisa56122 Italy

Distributed phased multiple-input multiple-output (PA-MIMO) radar systems on moving platforms combine the spatial diversity of MIMO radars with the coherent gain of phased-array radars, making them ideal for dynamic environments. However, challenges such as motion-induced channel variability, Doppler shifts, and complex parameter estimation often hinder target detection. Traditional methods like non-coherent fusion (NCF) and coherent fusion (CF) struggle to adapt to such variability, leading to suboptimal performance. In this paper, we propose a hybrid signal fusion (HSF) scheme that integrates joint space-time-phase compensation (JSTPC) with a compressed fusion network. The JSTPC module mitigates motion-induced phase errors and ensures precise alignment of received signals, while the compressed fusion network combines CF and NCF stages, leveraging their respective strengths to adaptively manage channel correlation variability. We also develop a joint parameter estimation algorithm to accurately extract direction of departure, direction of arrival, and Doppler frequency, thereby ensuring precise signal reconstruction and enhancing the detection process by estimating key parameters in real time. Theoretical analyses of detection thresholds and probabilities, derived using moment-matching techniques, further validate the reliability of the proposed scheme. Monte Carlo simulations and theoretical analyses for diverse radar configurations and channel correlation scenarios show that the proposed HSF scheme achieves 2 dB detection gain improvement over NCF. © 2025 IEEE.

关键词： Frequency estimation

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A Cross-Task Transfer SAR Target Recognition Method Based on AMFFNet and IACGAN

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Wan, Xunyang Du, Xiaolin Cui, Guolong Chen, Xiaolong Zheng, Jibin Yantai University School of Computer and Control Engineering Yantai264005 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Naval Aviation University Yantai264001 China Xidian University National Key Laboratory of Radar Signal Processing Xi'an710071 China

Convolutional neural networks (CNN) have shown excellent capability in synthetic aperture radar (SAR) target recognition. However, obtaining sufficient training samples for improving network recognition is difficult due to the deficiency of labeled SAR image data. To tackle the problem, a cross-task transfer method for SAR target recognition is proposed in this paper, which is based on a designed attention multi-scale feature fusion network (AMFFNet) and an improved auxiliary classifier generative adversarial network (IACGAN). In this method, firstly, a data augmentation method based on you only cut twice (YOCT) is devised for expanding the training samples. Secondly, the partial weight parameters of AMFFNet are initialized by the pre-trained IACGAN discriminator. Finally, AMFFNet is fine-tuned using the training samples. Additionally, on the network structure of AMFFNet, multi-scale features are extracted by adopting a multi-scale depth separable convolution module, which allows the model to acquire more informative features. Attention mechanisms are employed in this network to enhance its ability to capture important features while reducing the effects of noise. A multi-level feature fusion structure is used to further raise the representational capability of the model. Experimental results on multiple open-source real SAR datasets show that the proposed method can achieve better recognition performance under different percentages of training data, and the effectiveness and superiority of the method are confirmed by its robustness to noise. © 1965-2011 IEEE.

关键词： Convolutional neural networks

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A Gradient-Based Algorithm for Joint Beamforming and Reflection Design in Ris-Assisted Mobile Communications

SSRN

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SSRN 2025年

作者： Pitz, Ciro André Matsuo, Marcos Vinicius Seara, Rui Department of Control Automation and Computation Engineering Federal University of Santa Catarina Santa Catarina Blumenau89065-100 Brazil LINSE–Circuits and Signal Processing Laboratory Department of Electrical and Electronics Engineering Federal University of Santa Catarina Santa Catarina Florianópolis88040-900 Brazil

Reconfigurable intelligent surfaces (RIS) have emerged as a transformative technology for shaping the propagation environment in next-generation mobile communication systems, creating the need for efficient optimization strategies. In this context, this paper introduces a gradient-based algorithm for jointly optimizing beamforming and reflection design in RIS-assisted multi-user (MU) multiple-input single-output (MISO) systems. The proposed approach addresses the sum-rate capacity maximization problem while significantly reducing computational complexity. To achieve this, the optimization problem is reformulated by using eigenvalue minimization, thereby reducing computational burden. This reformulation reduces computational demands without compromising the sum-rate performance. The proposed algorithm incorporates both steepest descent and the damped quasi-Newton Broyden–Fletcher–Goldfarb–Shanno (BFGS) approximation, enabling flexible trade-offs between computational efficiency and convergence rate. Simulation results confirm the effectiveness of the proposed algorithm in enhancing the sum-rate capacity. © 2025, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

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Segmentation of Welding Defects using Level Set Methods

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2012年第6期7卷 1001-1008页

作者： Mohammed, Halimi Naim, Ramou Image and Signal Processing Laboratory. Welding and Control Research Center

Non-destructive testing (NDT) is a technique used in science and industry to evaluate the properties of a material without causing damage. In this paper we propose a method for segmenting radiographic images of welding in order to extract the welding defects which may occur during the welding process. We study different methods of level set and choose the model adapted to our application. The methods presented here take the property of local segmentation geodesic active contours and have the ability to change the topology automatically. The computation time is considerably reduced after taking into account a new level set function which eliminates the re-initialization procedure. Satisfactory results are obtained after applying this algorithm both on synthetic and real images.

关键词： Image segmentation Level set Weld defects Radiographic image

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A nonlinear adaptive H-infinity tracking control design in robotic systems via neural networks

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IEEE TRANSACTIONS ON control SYSTEMS TECHNOLOGY 1997年第1期5卷 13-29页

作者： Chang, YC Chen, BS IEEE Control and Signal Processing Laboratory Department of Electrical Engineering National Tsing Hua University

An adaptive neural-network tracking control with a guaranteed H-infinity performance is proposed for robotic systems with plant uncertainties and external disturbances. A neural-network system is introduced to learn these unknown (or uncertain) dynamics by an adaptive algorithm. Moreover, the effects on the tracking error due to the approximation error via the adaptive neural network must be attenuated to a prescribed level, i.e., an H-infinity tracking performance is achieved. Hence, in this study, both the H-infinity tracking theory and adaptive neural-network control scheme are combined together to achieve the nonlinear adaptive H-infinity tracking control design for uncertain or unknown robotic systems. The developed control scheme is smooth and semiglobal as well as very simple and computationally efficient, since it does not require a knowledge of either the mathematical model or the parameterization of the robotic dynamics. Finally, extensive simulations are given to illustrate the tracking performance of a two-link robotic manipulator with the proposed adaptive neural H-infinity control design.

关键词： nonlinear adaptive control H-infinity tracking control neural network robotic control

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Roll-pitch-yaw autopilot design for nonlinear time-varying missile using partial state observer based global fast terminal sliding mode control

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Chinese Journal of Aeronautics 2016年第5期29卷 1302-1312页

作者： Ahmed Awad Wang Haoping Sino-French International Joint Laboratory of Automatic Control and Signal Processing (LaFCAS) School of AutomationNanjing University of Science & Technology

The acceleration autopilot design for skid-to-turn （STT） missile faces a great challenge owing to coupling effect among planes, variation of missile velocity and its parameters, inexistence of a complete state vector, and nonlinear aerodynamics. Moreover, the autopilot should be designed for the entire flight envelope where fast variations exist. In this paper, a design of integrated roll-pitch-yaw autopilot based on global fast terminal sliding mode control （GFTSMC） with a partial state nonlinear observer （PSNLO） for STT nonlinear time-varying missile model, is employed to address these issues. GFTSMC with a novel sliding surface is proposed to nullify the integral error and the singularity problem without application of the sign function. The proposed autopilot consisting of two-loop structure, controls STT maneuver and stabilizes the rolling with a PSNLO in order to estimate the immeasurable states as an output while its inputs are missile measurable states and control signals. The missile model considers the velocity variation, gravity effect and parameters＇ variation. Furthermore, the environmental conditions＇ dynamics are mod- eled. PSNLO stability and the closed loop system stability are studied. Finally, numerical simulation is established to evaluate the proposed autopilot performance and to compare it with existing approaches in the literature.

关键词： Flight control system Global fast terminal slidingmode control Integrated autopilot Nonlinear state observer Skid-to-turn missile

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ADAPTIVE SET-MEMBERSHIP IDENTIFICATION IN O (M) TIME FOR LINEAR-IN-PARAMETERS MODELS

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IEEE TRANSACTIONS ON signal processing 1993年第5期41卷 1906-1924页

作者： DELLER, JR ODEH, SF Department of Electrical Engineering Control Systems and Signal Processing Group: Speech Processing Laboratory Michigan State University East Lansing MI USA Department of Electrical Engineering Control Systems and Signal Processing Group: Speech Processing Laboratory Michigan State University East Lansing MI USA Department of Electrical Engineering University of Jordan Amman Jordan

This paper describes some fundamental contributions to the theory and applicability of optimal bounding ellipsoid (OBE) algorithms for signal processing. All reported OBE algorithms are placed in a general framework which fruitfully demonstrates the relationship between the set-membership principles and least square error identification. Within this framework, flexible measures for adding explicit adaptation capability are formulated and demonstrated through simulation. Computational complexity analysis of OBE algorithms reveals that they are of O (m2) complexity per data sample with m the number of parameters identified, in spite of their well-known propensity toward highly selective updating. Two very different approaches are described for rendering a specific OBE algorithm, the set-membership weighted recursive least squares algorithm, of O (m) complexity. The first approach involves an algorithmic solution in which a suboptimal test for innovation is employed. The performance is demonstrated through simulation. The second method is an architectural approach in which complexity is reduced through parallel computation.

关键词： signal processing algorithms Samarium Least squares methods signal processing Speech processing Adaptive signal processing Computational complexity Algorithm design and analysis Testing Technological innovation

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Optimal design of deconvolution filters for stochastic multirate signal systems

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signal processing 1995年第3期47卷 287-305页

作者： Chen, BS Lin, CW Control and Signal Processing Laboratory Department of Electrical Engineering National Tsing Hua University Hsinchu Taiwan 30043 ROC

Multirate digital signal processing techniques are widely applied to data compression and storage in communications, speech processing, video processing, etc. signal reconstruction in conventional multirate signal processing systems is mainly based on the property of perfect reconstruction from the viewpoint of inverse filtering. signal corruption due to quantization and channel noise is neglected in the conventional design of multirate systems. Therefore, the performance of conventional multirate systems is deteriorated by channel noise. The signal reconstruction problem in multirate systems with consideration of channel noise is discussed in this paper from a more practical viewpoint. This is a so-called deconvolution filtering problem in stochastic multirate signal processing. A multirate state-space model is first introduced to unify signals with different sampling rates into a signal system model. Channel noise is modeled as a measurement noise in this state-space form. The deconvolution problem turns out to be a problem of optimal state estimation. A multirate Kalman deconvolution filter is then proposed to solve this problem. Three types of stochastic multirate systems including filter banks and transmultiplexers are discussed to illustrate the design procedure of multirate deconvolution filters. The proposed deconvolution filters are compared with conventional systems without consideration of channel noise. The performance of the proposed design is significantly better than the conventional design in the case of channel noise.

关键词： stochastic multirate systems multirate state space model optimal deconvolution filters

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