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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2023年第3期19卷 2307-2316页

作者： Ha, Junhyoung An, Byungchul Kim, Soonkyum Korea Inst Sci & Technol Ctr Healthcare Robot Dept Artificial Intelligence Robot Seoul 02792 South Korea Karlsruhe Inst Technol Inst Anthropomat & Robot D-76131 Karlsruhe Germany

In a graph search algorithm, a given environment is represented as a graph comprising a set of feasible system configurations and their neighboring connections. A path is generated by connecting the initial and goal configurations through graph exploration, whereby the path is often desired to be optimal or suboptimal. The computational performance of the optimal path generation depends on the avoidance of unnecessary explorations. Accordingly, heuristic functions have been widely adopted to guide the exploration efficiently by providing estimated costs to the goal configurations. The exploration is efficient when the heuristic functions estimate the optimal cost closely, which remains challenging because it requires a comprehensive understanding of the environment. However, this challenge presents the scope to improve the computational efficiency over the existing methods. Herein, we propose reinforcement learning heuristic A* (RLHA*), which adopts an artificial neural network as a learning heuristic function to closely estimate the optimal cost, while achieving a bounded suboptimal path. Instead of being trained by precomputed paths, the learning heuristic function keeps improving by using self-generated paths. Numerous simulations were performed to demonstrate the consistent and robust performance of RLHA* by comparing it with the existing methods.

关键词： Costs Heuristic algorithms Path planning signal processing algorithms Robots Reinforcement learning Planning Graph search path planning reinforcement learning

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A Clutter Suppression Algorithm via Enhanced Sparse Bayesian Learning for Airborne Radar

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IEEE SENSORS JOURNAL 2023年第10期23卷 10900-10911页

作者： Wang, Degen Wang, Tong Cui, Weichen Zhang, Xinying Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China

The traditional space-time adaptive processing (STAP) method based on sparse Bayesian learning (SBL) has the problems of low computational efficiency and slow convergence speed. In this article, a novel SBL approach based on a statistical threshold is proposed to address these issues. To discriminate between the active and inactive atoms of the dictionary, we first develop an adaptive decision test. Next, the adaptive decision test is integrated into the SBL method, which increases its accuracy and convergence rate. In addition, the detection threshold has an important property that is independent of signal and noise power. Therefore, the adaptive decision test is robust to external changes. Numerous simulations demonstrate that the proposed algorithm has excellent clutter suppression performance and fast convergence rate.

关键词： Clutter signal processing algorithms Convergence Sensors Symbols Sparse matrices Matching pursuit algorithms Space--time adaptive processing (STAP) sparse Bayesian learning (SBL) statistical threshold

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Covariance Matrix Recovery From One-Bit Data With Non-Zero Quantization Thresholds: Algorithm and Performance Analysis

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IEEE TRANSACTIONS ON signal processing 2023年 71卷 4060-4076页

作者： Xiao, Yu-Hang Huang, Lei Ramirez, David Qian, Cheng So, Hing Cheung Shenzhen Univ State Key Lab Radio Frequency Heterogeneous Integr Shenzhen 518060 Peoples R China Univ Carlos III Madrid Dept Signal Theory & Commun Madrid 28911 Spain Gregorio Maranon Hlth Res Inst Madrid 28007 Spain IQVIA Inc Cambridge MA 02139 USA City Univ Hong Kong Dept Elect Engn Hong Kong 999077 Peoples R China

Covariance matrix recovery is a topic of great significance in the field of one-bit signal processing and has numerous practical applications. Despite its importance, the conventional arcsine law with zero threshold is incapable of recovering the diagonal elements of the covariance matrix. To address this limitation, recent studies have proposed the use of non-zero clipping thresholds. However, the relationship between the estimation error and the sampling threshold is not yet known. In this article, we undertake an analysis of the mean squared error by computing the Fisher information matrix for a given threshold. Our results reveal that the optimal threshold can vary considerably, depending on the variances and correlation coefficients. As a result, it is inappropriate to adopt a constant threshold to encompass parameters that vary widely. To mitigate this issue, we present a recovery scheme that incorporates time-varying thresholds. Our approach differs from existing methods in that it utilizes the exact values of the threshold, rather than its statistical properties, to increase the estimation accuracy. Simulation results, including those of the direction-of-arrival estimation problem, demonstrate the efficacy of the developed scheme, especially in complex scenarios where the covariance elements are widely separated.

关键词： Correlation coefficient Estimation error Quantization (signal) Direction-of-arrival estimation Simulation signal processing algorithms Numerical simulation Covariance matrix estimation mean squared error analysis non-zero threshold one-bit sampling

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Sensorless Saliency-Based Control of Dual Induction Machines Under Dynamic Load Imbalances Using Three Current Sensors

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2023年第12期70卷 12093-12101页

作者： Montero, Eduardo Rodriguez Vogelsberger, Markus Wolbank, Thomas Vienna Univ Technol A- 1040 Vienna Austria ALSTOM Transport Austria GmbH Rolling Stock Platform Components Div DRIVES A-1220 Vienna Austria

To perform vector control of single-inverter dual induction motors, drives are standardly equipped with current and position sensors. In medium-power railway applications, a shaft encoder is usually mounted on each motor, and the inverter output current is measured using two current sensors. Yet, this configuration features some disadvantages. First, torque-sharing cannot be computed because stator current of each motor is unknown. Similarly, saliency extraction of each motor is not achievable since individual current drawn by each motor is not measured. An option that achieves torque-sharing calculation is to attach two current sensors to each motor, provided that both rotor positions are known. However, industrial inverter/control platforms considered offer a maximum of three input channels for current sensors. Besides, some industry fields tend towards encoderless speed control. Therefore, this article proposes to use three current sensors in total and no shaft encoders. Thanks to a special current sensor arrangement and a voltage step excitation method, saliency-based encoderless control is achieved. In addition, a method is presented to estimate torque-sharing. Experimental measurements, taken on a dual induction motor test bench, will show the capability of the proposed methodology to identify individual loading as well as excellent encoderless control performance.

关键词： Angular velocity control harmonic analysis induction motor sensorless control signal processing algorithms

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Leveraging Deep Neural Networks for Massive MIMO Data Detection

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IEEE WIRELESS COMMUNICATIONS 2023年第1期30卷 174-180页

作者： Nguyen, Ly V. Nguyen, Nhan T. Tran, Nghi H. Juntti, Markku Swindlehurst, A. Lee Nguyen, Duy H. N. San Diego State Univ San Diego CA 92182 USA Univ Oulu Oulu Finland Univ Akron Akron OH USA Univ Calif Irvine Irvine CA USA

Massive multiple-input multiple-output (MIMO) is a key technology for emerging next-generation wireless systems. Utilizing large antenna arrays at base-stations, massive MIMO enables substantial spatial multiplexing gains by simultaneously serving a large number of users. However, the complexity in massive MIMO signal processing (e.g., data detection) increases rapidly with the number of users, making conventional hand-engineered algorithms less computationally efficient. Low-complexity massive MIMO detection algorithms, especially those inspired or aided by deep learning, have emerged as a promising solution. While there exist many MIMO detection algorithms, the aim of this magazine article is to provide insight into how to leverage deep neural networks (DNN) for massive MIMO detection. We review recent developments in DNN-based MIMO detection that incorporate the domain knowledge of established MIMO detection algorithms with the learning capability of DNNs. We then present a comparison of the key numerical performance metrics of these works. We conclude by describing future research areas and applications of DNNs in massive MIMO receivers.

关键词： Detectors Massive MIMO Symbols signal processing algorithms Detection algorithms Deep learning Antenna arrays

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Subgradient Descent Learning Over Fading Multiple Access Channels With Over-the-Air Computation

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IEEE ACCESS 2023年 11卷 94623-94635页

作者： Gez, Tamir L. S. Cohen, Kobi Ben Gurion Univ Negev Sch Elect & Comp Engn IL-8410501 Beer Shev Israel

We focus on a distributed learning problem in a communication network, consisting of $N$ distributed nodes and a central parameter server (PS). The PS is responsible for performing the computation based on data received from the nodes, which are transmitted over a multiple access channel (MAC). The objective function for this problem is the sum of the local loss functions of the nodes. This problem has gained attention in the field of distributed sensing systems, as well as in the area of federated learning (FL) recently. However, current approaches to solving this problem rely on the assumption that the loss functions are continuously differentiable. In this paper, we first address the case where this assumption does not hold. We develop a novel algorithm called Sub-Gradient descent Multiple Access (SGMA) to solve the learning problem over MAC. SGMA involves each node transmitting an analog shaped waveform of its local subgradient over MAC, and the PS receiving a superposition of the noisy analog signals, resulting in a bandwidth-efficient over-the-air (OTA) computation used to update the learned model. We analyze the performance of SGMA and prove that it has a convergence rate that approaches that of the centralized subgradient algorithm in large networks. Simulation results using real datasets show the effectiveness of SGMA.

关键词： Fading channels Bandwidth Task analysis Noise measurement Computational modeling Simulation signal processing algorithms Decentralized control Learning systems Federated learning Multiple access interference Distributed learning gradient descent (GD)-type learning subgradient methods federated learning (FL) multiple access channel (MAC) over-the-air (OTA) computation

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Distributed Set-Membership Fusion Filtering for Nonlinear 2-D Systems Over Sensor Networks: An Encoding-Decoding Scheme

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第1期53卷 416-427页

作者： Zhu, Kaiqun Wang, Zidong Han, Qing-Long Wei, Guoliang Univ Shanghai Sci & Technol Dept Control Sci & Engn Shanghai 200093 Peoples R China Brunel Univ London Dept Comp Sci Uxbridge UB8 3PH Middx England Swinburne Univ Technol Sch Sci Comp & Engn Technol Melbourne Vic 3122 Australia Univ Shanghai Sci & Technol Coll Sci Shanghai 200093 Peoples R China

In this article, the distributed set-membership fusion filtering problem is investigated for a class of nonlinear 2-D shift-varying systems subject to unknown-but-bounded noises over sensor networks. The sensors are communicated with their neighbors according to a given topology through wireless networks of limited bandwidth. With the purpose of relieving the communication burden as well as enhancing the transmission security, a logarithmic-type encoding-decoding mechanism is introduced for each sensor node so as to encode the transmitted data with a finite number of bits. A distributed set-membership filter is designed to determine the local ellipsoidal set that contains the system state by only utilizing the data from the local sensor node and its neighbors, where the proposed filter scheme is truly distributed with desirable scalability. Then, a new ellipsoid-based fusion rule is developed for the designed set-membership filters in order to form the fused ellipsoidal set that has a globally smaller volume than all local ellipsoidal sets. With the aid of the mathematical induction technique, the set theory, and the convex optimization approach, sufficient conditions are derived for the existence of the desired distributed set-membership filters and the fusion weights. Then, the filter parameters and the fusion weights are acquired by solving a set of constrained optimization problems. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed fusion filtering algorithm.

关键词： Symmetric matrices Decoding Topology signal processing algorithms Network topology Wireless networks Two dimensional displays Distributed set-membership filtering (SMF) encoding-decoding mechanism (EDM) fusion filtering sensor networks two-dimensional (2-D) systems

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Information Geometry-Based Track-Before-Detect Algorithm for Slow-Moving Fluctuating Target Detection

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IEEE TRANSACTIONS ON signal processing 2023年 71卷 1833-1848页

作者： Wu, Hao Cheng, Yongqiang Chen, Xixi Yang, Zheng Li, Xiang Natl Univ Def Technol Coll Elect Sci & Technol Changsha 410000 Peoples R China Air Force Engn Univ Air & Missile Def Coll Xian 710051 Peoples R China

To treat the slow-moving fluctuating target detection issues, the information geometry-based track-before-detect algorithms, which combines the performance advantages of information geometry detectors and the multi-frame information integration ability of dynamic programming algorithm, are proposed in this article. The proposed methods are based on the Hermitian positive definite (HPD) manifold and power spectrum (PS) manifold, that the geometric properties of the target feature track and clutter track on the HPD or PS manifold are exploited for the integration of inter-frame target information. Specifically, the IG-based scoring function is designed to evaluate the candidate trajectories, and a general IG-based DP algorithm is deduced to obtain the trajectories with the maximum integration of scoring functions. Moreover, to avoid the performance degradation resulting from the illegal candidate trajectories, a stricter constraint for the trajectories of slow-moving target is established, and a corresponding efficient IG-based DP algorithm is deduced by utilizing the skipping transition. Experimentally, the superiority of the proposed methods have been validated with the real-recorded ship target echo data and sea clutter data. The experimental results show that the proposed methods can provide a signal-to-clutter ratio improvement of approximately 5.5 dB.

关键词： Clutter Detectors Trajectory Target tracking Manifolds Covariance matrices signal processing algorithms Information geometry information geometry detector track-before-detect (TBD) dynamic programming Hermitian positive definite manifold power spectrum manifold

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Finite-Time Analysis of Markov Gradient Descent

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2023年第4期68卷 2140-2153页

作者： Doan, Thinh T. Virginia Tech Bradley Dept Elect & Comp Engn Blacksburg VA 24060 USA

Motivated by its broad applications in system identifications, stochastic control, and machine learning, we study the popular stochastic gradient descent (SGD) when the gradient samples of the underlying objective function are generated from Markov processes. This Markov sampling leads to the gradient samples being biased and not independent. The existing results for the convergence of SGD under Markov settings are often established under the assumption on the boundedness of either the iterates or the gradient samples. This assumption can be guaranteed through an impractical projection step to a compact set often defined over the unknown optimal solution of the underlying problem. In this article, we show that this projection step is unnecessary in many settings. In particular, we study the convergence of SGD under Markov samples without requiring any projection step for different objective functions, ranging from strongly convex to nonconvex objectives satisfying Polyak-Lojasiewicz conditions. We show that SGD converges nearly at the same rate with Markovian gradient samples as with independent gradient samples. The only difference is a logarithmic factor that accounts for the mixing time of the Markov process. Finally, we provide numerical experiments in robust identification problems to illustrate our theoretical results, where we consider SGD with samples generated by a Markov process.

关键词： Markov processes Convergence Optimization Linear programming TV Convex functions signal processing algorithms Gradient methods optimization

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Adaptive Frame-Rate Partitioned Video SAR

IEEE TRANSACTIONS ON RADAR SYSTEMS

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IEEE TRANSACTIONS ON RADAR SYSTEMS 2025年 3卷 576-590页

作者： Sun, Zhengyang Wen, Liwu Ding, Jinshan Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China

Video synthetic aperture radar (ViSAR) is a promising technology for the surveillance of ground-moving targets. Traditionally, ViSAR imaging and moving target tracking are performed sequentially, where high-frame-rate imaging is applied to the entire SAR scene. However, this approach generates redundant information that is often unnecessary for ViSAR applications. We propose a partitioned adaptive frame-rate (PAFR) ViSAR processing strategy, which adaptively partitions the SAR scene, applying high-frame-rate imaging to potential target regions and low frame rate to large static areas. An integrated imaging and tracking algorithm that synthesizes back-projection (BP) and track-before-detect (TBD) techniques has been derived for efficient bidirectional information exchange. BP imaging provides high-resolution measurements to refine tracking parameters, while TBD tracking offers predictive data to guide the adaptive partitioning of the imaging area. Additionally, we enhance the traditional dynamic programming-based TBD (DP-TBD) algorithm by incorporating the morphological features of target shadows, allowing for more accurate corrections and refinements of predicted states. This enhancement significantly improves both tracking accuracy and speed. The experimental results from airborne radar data have proven the capability of the proposed algorithm to achieve both efficient PAFR imaging and fast target tracking simultaneously, which paves the way for more potential applications in ViSAR.

关键词： Target tracking Radar tracking Imaging Radar imaging Synthetic aperture radar Radar Prediction algorithms Heuristic algorithms signal processing algorithms Radar polarimetry Ground-moving target indication (GMTI) radar imaging target tracking track before detect (TBD) video synthetic aperture radar (ViSAR)

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