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Sectoring Approach for Performance Enhancement of MTT and Its Application on JPDA Algorithm

IEEE ACCESS 2025年 13卷 67758-67768页

作者： Cobankaya, Mustafa Latif Elrouby, Alaa B. Secretariat Def Ind TR-06420 Ankara Turkiye Ankara Yildirim Beyazit Univ Elect & Elect Engn Dept TR-06010 Ankara Turkiye

Recent advancements in multi-target tracking (MTT) technologies and algorithms have led to improved accuracy and efficiency in tracking multiple objects in dynamic environments. However, as the number of targets increases, MTT methods often struggle with computational demands and tracking capacity. This paper presents a novel sectoring approach on the Joint Probabilistic Data Association (JPDA) algorithm to enhance its tracking performance in MTT scenarios. The sectoring method divides the tracking area into distinct sectors and assigns a separate JPDA tracker for each sector, yielding two primary benefits in environments with large numbers of targets. First, it increases the capacity of the JPDA tracker to handle a larger number of targets. Second, it reduces computational complexity when tracking a high number of targets. On the other hand, the approach introduces a trade-off by slightly increasing computational complexity in scenarios with fewer targets. Experimental results were obtained through simulations of 10 different scenarios, each with 37 varying numbers of targets and three different sector configurations, amounting to a total of 1110 simulations. The findings demonstrate that the sectoring approach achieves up to a 95% reduction in computational complexity and increases the number of tracked targets from 24 to 40 in randomly generated scenarios. Results also revealed that the effects of the sectoring approach become more pronounced as the number of sectors increases.

关键词： Target tracking signal processing algorithms Radar tracking Kalman filters Computational complexity Heuristic algorithms Computational modeling Approximation algorithms Surveillance Data models Multi-target tracking joint probabilistic data association computational complexity high number of maneuvering targets sectoring approach

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Fast and Efficient Single-Snapshot Range and Angle Estimation for FDA-MIMO Radar

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2024年 21卷 1页

作者： Chen, Geng Wang, Chunyang Gong, Jian Xu, Jingwei Tan, Ming Air Force Engn Univ Sch Air Def & AntiMissile Xian 710051 Peoples R China Beijing Aviat Meteorol Inst Beijing 10085 Peoples R China Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China Natl Univ Def Technol Coll Informat & Commun Wuhan 430010 Peoples R China

This letter proposes an iterative, fast, and efficient single-snapshot range and angle estimation method for frequency diverse array multiple-input multiple-output (FDA-MIMO) radar. The proposed approach transforms the angle and range estimation problem into the transmit and receive frequency estimation, and a 2-D frequency estimation algorithm is designed for multiple sinusoids (corresponding to multiple targets in radar). The frequency estimation is divided into coarse estimation and fine estimation. The coarse estimation can be achieved using the discrete Fourier transform, and fine estimation is estimated by the designed iterative structure. The designed algorithm achieves effective target parameters' estimation with low computational cost. Simulation results demonstrate the effectiveness of the algorithm under various situations.

关键词： Estimation Frequency estimation Radar Discrete Fourier transforms signal to noise ratio Vectors signal processing algorithms Fast Fourier transform frequency diverse array multiple-input multiple-output (FDA-MIMO) single-snapshot target parameters' estimation

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Sparse Adversarial Unrolling Network for Bearing Compound Fault Classification

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Yang, Chenxi Chen, Saisai Liu, Shuman Fan, Wei Chen, Chao Jiangsu Univ Sch Mech Engn Zhenjiang 212013 Peoples R China China Univ Min & Technol State Key Lab Fine Explorat & Intelligent Dev Coal Xuzhou 221116 Peoples R China

Bearing compound fault classification is a key task for machinery maintenance. Label-consistent K-means singular value decomposition (LC-KSVD) has been widely applied for single-fault classification due to its strong interpretability. However, its efficiency is limited in compound fault classification due to the need for manually adjusted parameters and the complexities of fault coupling. To address these limitations, we propose a sparse adversarial unrolling network (SAU-Net), which incorporates a hard-threshold iterative sparse classification model based on traditional LC-KSVD, unrolled into an interpretable network. The parameter matrix is automatically optimized using a deep conditional generative adversarial network (DCGAN). Experimental results demonstrate that the proposed SAU-Net achieves higher classification accuracy and greater stability compared to state-of-art methods in compound fault classification, while maintaining strong interpretability.

关键词： Classification algorithms Compounds Iterative methods Training Matching pursuit algorithms signal processing algorithms Optimization Generators Generative adversarial networks Dictionaries Algorithm unrolling bearing compound fault diagnosis deep conditional generative adversarial network (DCGAN) label-consistent K-means singular value decomposition (LC-KSVD) sparse classification

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Complex-Valued Neural Network With Multistep Training for Single-Snapshot DOA Estimation

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2024年 21卷 5-5页

作者： Zhang, Yuheng Zeng, Rong Zhang, Shuchen Wang, Jiachen Wu, Yifeng Sun Yat sen Univ Sch Elect & Commun Engn Shenzhen 518107 Peoples R China

Estimating the direction of arrival (DOA) for single snapshot signals is paramount in practical applications. In this letter, we present a method for single snapshot DOA estimation based on a neural network, namely, the complex-valued residual angle estimation network (CVRAEN). First, the CVRAEN network uses the initial feature extraction module including complex linear layer and complex convolutional layer to process the input signal and decompose it into multiple complex-valued feature signals for obtaining preliminary signal feature extraction. Subsequently, to extract depth features, the depth feature extraction module is used to further process the extracted features. Finally, the mapping module combines and maps the extracted features for DOA estimation. This letter uses a series of training steps, including pretraining, signal-to-noise ratio (SNR) stratified training, and SNR adaptive training, to continuously optimize the network model. The main simulation results indicate that within the simulated SNR range, the proposed method exhibits a decrease of approximately 30% in root mean square error compared with traditional methods.

关键词： Direction-of-arrival estimation Estimation Training Feature extraction signal processing algorithms signal to noise ratio Matching pursuit algorithms Complex-valued network direction of arrival (DOA) multistep training residual block single snapshot signal

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A New Twist on Low-Complexity Digital Backpropagation

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2025年第10期43卷 4679-4692页

作者： Civelli, Stella Jana, Debi Pada Forestieri, Enrico Secondini, Marco Cnr Ist Elettron & Ingn Informaz & Telecomunicaz C I-56122 Pisa Italy Scuola Super Sant Anna Telecommun Comp Engn & Photon Inst TeCIP I-56127 Pisa Italy Univ Cent Florida Coll Opt & Photon CREOL Orlando FL 32816 USA CNIT Natl Lab Photon Networks I-56124 Pisa Italy

This work proposes a novel low-complexity digital backpropagation (DBP) method, with the goal of optimizing the trade-off between backpropagation accuracy and complexity. The method combines a split step Fourier method (SSFM)-like structure with a simplified logarithmic perturbation method to obtain a high accuracy with a small number of DBP steps. Subband processing and asymmetric steps with optimized splitting ratio are also employed to further reduce the number of steps required to achieve a prescribed performance. The first part of the manuscript is dedicated to the derivation of a simplified logarithmic-perturbation model for the propagation of a dual-polarization multiband signal in an optical fiber, which serves as a theoretical background for the development of the proposed coupled-band enhanced split step Fourier method (CB-ESSFM) and for the analytical calculation of the model coefficients. Next, the manuscript presents a digital signal processing algorithm for the implementation of DBP based on a discrete-time version of the model and an overlap-and-save processing strategy. Practical approaches for the optimization of the coefficients used in the algorithm and of the splitting ratio of the asymmetric steps are also discussed. A detailed analysis of the computational complexity of the algorithm is also presented. Finally, the performance and complexity of the proposed DBP method are investigated through numerical simulations and compared to those of other methods. In a five-channel 100 GHz-spaced wavelength division multiplexing system over a 15x80 km single-mode-fiber link, the proposed CB-ESSFM achieves a gain of about 1 dB over simple dispersion compensation with only 15 steps (corresponding to 681 real multiplications per 2D symbol), with an improvement of 0.9 dB over conventional SSFM and almost 0.4 dB over our previously proposed ESSFM. Significant gains and improvements are obtained also at lower complexity. For instance, the gain reduces to a stil

关键词： Accuracy Computational modeling Optical fiber dispersion Computational complexity Photonics Numerical models Digital signal processing Wavelength division multiplexing Telecommunications signal processing algorithms Digital backpropagation nonlinear fiber channel optical fiber communication perturbation methods

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Computationally Efficient Lie Algebraic Particle Filters for State Estimation

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2025年第5期70卷 3370-3377页

作者： Jin, Yuqiang Zhang, Wen-An Lu, Xinyu Chen, Bo Yu, Li Zhejiang Univ Technol Coll Informat Engn Hangzhou 310023 Peoples R China

A set of particle filters on matrix Lie groups is presented for state estimation, where the particles live in Lie algebra. The dynamical equations in the Lie algebraic form and the log-linear property on the invariant error are introduced separately, to design two efficient particle time update schemes. All operations during the update are transferred to a vector space and at least only a single mean particle is propagated, the remaining particles are calculated by the error update on Lie algebra. This allows the tedious coordinate transformation in the original filter to be avoided, and significantly reduces the computational cost of numerical integration. Moreover, a detailed computational complexity analysis is provided, we compare scenarios in which these Lie algebraic particle filters can estimate efficiently, and discuss their respective drawbacks. The theoretical upper bounds of the relevant variables that can lead to efficiency improvements are also investigated. Finally, the experimental results show the effectiveness of the proposed Lie algebraic particle filters.

关键词： Lie groups Manifolds Mathematical models Algebra signal processing algorithms Particle filters Bayes methods Vectors Computational modeling State estimation Matrix Lie group particle filter (PF) pose estimation

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Fast Gridless DOA Estimation Algorithm for MA-ANS Scenarios Using a Modified FastIPM

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IEEE signal processing LETTERS 2024年 31卷 1845-1849页

作者： Gao, Yiding Wu, Min Li, Zihao Hao, Chengpeng Orlando, Danilo Chinese Acad Sci Inst Acoust Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China Beihang Univ Hangzhou Innovat Inst Hangzhou 310000 Peoples R China Univ Pisa Dipartimento Ingn Informaz I-56122 Pisa Italy

This letter presents a fast gridless direction-of-arrival (DOA) estimation algorithm that improves the Fast Interior-Point Method (FastIPM). The proposed algorithm effectively achieves a significant reduction in computational load for large-scale arrays while maintaining an accurate estimation. It reduces the complexity of gridless DOA estimation to $\mathcal {O}(N<^>{2})$ per iteration (according to the Landau notation). Compared to the original FastIPM, we extend the received signal data model to account for more general scenarios that include missing array elements and arbitrary number of snapshots. By formulating the problem as an optimization with an obstacle function, we iteratively minimize the dual gap to obtain the optimal solution for atomic norm minimization problem. Extensive numerical simulations demonstrate the algorithm's computational superiority over the state-of-the-art gridless DOA estimation methods, providing excellent resolution and accuracy.

关键词： signal processing algorithms Estimation Direction-of-arrival estimation Vectors Array signal processing Optimization Convex functions atomic norm minimization barrier function fast gridless DOA estimation interior point method

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Tampering Attack Detection for Remote State Estimation With Quantized Observations

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IEEE signal processing LETTERS 2024年 31卷 2185-2189页

作者： Li, Mengqi Hu, Yanpeng Guo, Jin Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Minist Educ Key Lab Knowledge Automat Ind Proc Beijing 100083 Peoples R China

Aiming at the security problem of cyber physical system (CPS), this paper designs and evaluates a tampering attack detection scheme for remote state estimation with quantized observations. When the transmitted information is quantized and the attacker randomly tampers it with a certain probability, we design a detection algorithm with a given error threshold based on the received data. Moreover, we proposes detection performance indexes, leakage detection rate and false detection rate, for the above algorithm, which are used to measure the algorithm performance to discriminate the tampered data, and further analyzes the impact of the attack strategy and the error threshold on performance indexes. Finally, simulations are designed to verify the theoretical results.

关键词： Estimation State estimation Technological innovation Detectors signal processing algorithms Sensors Cyberattack Attack detection binary quantized observations data tampering performance evaluation state estimation

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Sparse Regularization With Reverse Sorted Sum of Squares via an Unrolled Difference-of-Convex Approach

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF signal processing 2025年 6卷 57-67页

作者： Sasaki, Takayuki Hayase, Kazuya Kitahara, Masaki Ono, Shunsuke NTT Corp Yokosuka 2390847 Japan Inst Sci Tokyo Yokohama 2260026 Japan

This paper proposes a sparse regularization method with a novel sorted regularization function. Sparse regularization is commonly used to solve underdetermined inverse problems. Traditional sparse regularization functions, such as L1-norm, suffer from problems like amplitude underestimation and vanishing perturbations. The reverse ordered weighted L1-norm (ROWL) addresses these issues but introduces new challenges. These include developing an algorithm grounded in theory, not heuristics, reducing computational complexity, enabling the automatic determination of numerous parameters, and ensuring the number of iterations remains feasible. In this study, we propose a novel sparse regularization function called the reverse sorted sum of squares (RSSS) and then construct an unrolled algorithm that addresses both the aforementioned two problems and these four challenges. The core idea of our proposed method lies in transforming the optimization problem into a difference-of-convex programming problem, for which solutions are known. In experiments, we apply the RSSS regularization method to image deblurring and super-resolution tasks and confirmed its superior performance compared to conventional methods, all with feasible computational complexity.

关键词： Inverse problems Perturbation methods Vectors Computational complexity Programming Convergence signal processing algorithms Optimization Convex functions signal processing Deep unrolling difference-of-convex inverse problem non-convex optimization proximal splitting method sparse regularization

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RFI-Aware and Low-Cost Maximum Likelihood Imaging for High-Sensitivity Radio Telescopes

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IEEE signal processing LETTERS 2024年 31卷 2960-2964页

作者： Wang, J. El Korso, M. N. Bacharach, L. Larzabal, P. Univ Paris Saclay SATIE F-91190 Gif Sur Yvette France Univ Paris Saclay L2S F-91190 Gif Sur Yvette France

This paper addresses the challenge of interference mitigation and reduction of computational cost in the context of radio interferometric imaging. We propose a novel maximum-likelihood-based methodology based on the antenna sub-array switching technique, which strikes a refined balance between imaging accuracy and computational efficiency. In addition, we tackle robustness regarding radio interference by modeling the additive noise as t-distributed. Through simulation results, we demonstrate the superiority of the t-distributed noise model over the conventional Gaussian noise model in scenarios involving interferences. We evidence that our proposed switching approach yields similar imaging performances with far fewer visibilities compared to the full array configuration, thus, diminishing the computational complexity.

关键词： Noise Imaging Vectors Antennas Computational modeling Antenna measurements Antenna arrays Switches signal processing algorithms Sensors Antenna array processing expectation-maximization algorithm interferometry radio-astronomical imaging

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