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Limited-Memory Receive Filter Design for Massive MIMO Radar in signal-Dependent Interference

IEEE signal processing LETTERS 2022年 29卷 1536-1540页

作者： Deng, Minglong Cheng, Ziyang He, Zishu Univ Elect Sci & Technol China UESTC Sch Informat & Commun Engn Chengdu 611731 Peoples R China

This letter proposes a limited-memory receive filter design to maximize the output signal-to-interference-plus-noise ratio (SINR) of massive multiple-input multiple-output (MIMO) radar. The associated problem is a minimum variance distortionless response (MVDR) one which can be analytical solved, but the computation of the MVDR solution would be prohibitive in the context of massive MIMO radar, due to the huge memory consumption. To this end, we propose a low-complexity algorithm that exploits a reparameterization method to avoid the significant memory consumption, and solves the resulting problem with several simple algebraic operations. We show that the memory consumption of the proposed algorithm depends on the number of interference sources. Therefore, for the scenario with a large number of interference sources, we extend the proposed algorithm by the alternating direction method of multipliers (ADMM), which promotes a further memory reduction. Finally, numerical simulations are conducted to demonstrate the efficiency of the proposed algorithm.

关键词： Interference Memory management signal to noise ratio Radar signal processing algorithms Massive MIMO MIMO radar Massive MIMO radar SINR maximization receive filter design

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Delta-Operator-Based Reaching Laws for Sliding Mode Control Design

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2022年第4期69卷 2136-2140页

作者： Mishra, Jyoti P. Yu, Xinghuo RMIT Univ Sch Engn Melbourne Vic 3000 Australia

Most of the continuous-time algorithms are implemented in the discrete domain. Therefore, it is of utmost importance to have a smooth transition between discrete and continuous time models for enhancing numerical conditioning. Delta-operator is known to provide such flexibility where a delta-operator based dynamic model converges to its continuous counterpart as the sampling time converges to zero. In this brief, delta-operator based novel reaching laws are established. Most importantly, the quasi sliding mode band (QSMB) is calculated via suitable stability analysis in the delta-operator framework. It is shown that when the sampling time converges to zero, the delta-operator based stability analysis provides a similar conclusion to that of continuous-domain analysis. Finally, the theoretical findings are corroborated by some simulation results.

关键词： Circuits and systems Trajectory Sliding mode control Closed loop systems Stability criteria signal processing algorithms Mathematical models Robust control discretization delta-operator sliding mode control

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EnhanceFace: Adaptive Weighted SoftMax Loss for Deep Face Recognition

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IEEE signal processing LETTERS 2022年 29卷 65-69页

作者： Wang, Jiaqi Zheng, Chen Yang, Xiaohui Yang, Lijun Henan Univ Sch Math & Stat Kaifeng 475004 Peoples R China Henan Univ Henan Engn Res Ctr Artificial Intelligence Theory Kaifeng 475004 Peoples R China

Loss function is an important topic in the field of face recognition, while the margin-based loss function is one of the most useful methods to enhance discriminability. Recently, the method of dividing the samples into easy and hard ones effectively optimizes the margin-based loss function by emphasizing these two parts at different training stages. However, the hard samples contain not only intra-personal variations that can be easily misclassified but also wrong-labeled samples. To capture and distinguish their difference, an EnhanceFace method is proposed with a new adaptive weighted loss function in this letter to improve the accuracy of face recognition. Firstly, this method partitions the hard samples into semi-hard and harder samples. Then, a new loss function is developed to adaptively adjust corresponding weights for both semi-hard samples and harder samples during the training process. With this new loss function, their effects can be well balanced according to their importance. The EnhanceFace method is tested on different popular benchmarks. Finally, better performance is shown by the experimental results comparing with other state-of-the-art methods.

关键词： Training Face recognition Loss measurement Weight measurement signal processing algorithms Optimization Feature extraction Face recognition loss function adaptive weight

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Large-Scale Multi-View Clustering via Fast Essential Subspace Representation Learning

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IEEE signal processing LETTERS 2022年 29卷 1893-1897页

作者： Zheng, Qinghai Fuzhou Univ Coll Comp & Data Sci Fuzhou 350108 Peoples R China

Large-scale Multi-View Clustering (LMVC) is a hot research problem in the fields of signal processing and machine learning, and many anchor-based multi-view subspace clustering algorithms are proposed in recent years. However, most existing methods usually concentrate on the issue of reducing the time cost and ignore the exploration of the complementary information during the clustering process. To this end, we propose a Fast Essential Subspace Representation Learning (FESRL) method for large-scale multi-view subspace clustering. Specifically, FESRL introduces the orthogonal transformation to investigate both the complementary and consensus information across multiple views. The essential subspace representation can be learned in a linear time cost. Experiments conducted on several benchmark datasets illustrate the competitiveness of the proposed method.

关键词： signal processing algorithms Optimization Costs Representation learning Time complexity Clustering algorithms Convergence Large-scale multi-view subspace clustering linear computational complexity

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Layer Pruning for Obtaining Shallower ResNets

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IEEE signal processing LETTERS 2022年 29卷 1172-1176页

作者： Zhang, Ke Liu, Guangzhe Northwestern Polytech Univ Sch Astronaut Natl Key Lab Aerosp Flight Dynam Xian 710072 Peoples R China

Network pruning has become an effective scheme to cut down the network complexity and speed up the inference. Current work mainly focuses on filter pruning, which deletes filters in a structured way. Although the calculation amount is reduced after pruning, the acceleration effect is not obvious. In this letter, we present a layer pruning method to obtain shallower ResNets (LPSR) and significantly reduce the inference latency. Taking advantage of the characteristics of the residual module, we disconnect the unimportant residual mapping and reserve only the identity mapping, thereby equivalently removing the corresponding residual module and achieving the purpose of layer pruning. We adopt Taylor expansion to estimate the impact of disconnecting BN layers in the residual mapping. These impacts are then normalized and used to identify unimportant residual blocks to prune. Comprehensive experimental results demonstrate that LPSR not only reduces the number of parameters and calculations, but also significantly speed up the inference. LPSR improves the accuracy of ResNet-56 on CIFAR-10 from 93.21% to 93.40%, while reducing 44.71% parameters, 52.68% calculations and 44.89% inference latency.

关键词： Training Taylor series Convolution Cost function Sorting signal processing algorithms Kernel Layer pruning model compression network acceleration network pruning

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Radio Map Assisted Path Planning for UAV Anti-Jamming Communications

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IEEE signal processing LETTERS 2022年 29卷 607-611页

作者： Dong, Yangrui He, Chen Wang, Ziteng Zhang, Lin Northwest Univ Sch Informat Sci & Technol Xian 710127 Peoples R China

The communications quality of unmanned aerial vehicle (UAV) has been brought into focus increasingly, which can be severely affected by other users, as well as complex topography. In this letter, we propose a path planning algorithm for UAV anti-jamming communications with the joint assistance of two kinds of radio maps-signal power map and signal to interference plus noise ratio (SINR) map, which contain the abundant information of path loss caused by the topography and interference from jammers. We show how to generate these maps from real topographic data, and provide the optimal UAV path planning by adopting the power and the SNR maps to the heuristic and graphic based A* algorithm. Simulations with various settings for areas with complex topography in real world, show that the proposed algorithm can considerably improve the efficiency by minimizing the UAV flying distance for anti-jamming communications.

关键词： Interference signal to noise ratio Autonomous aerial vehicles Path planning Jamming signal processing algorithms Base stations UAV path planning anti-jamming communi-cation radio map

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Proximal Based Strategies for Solving Discrete Mumford-Shah With Ambrosio-Tortorelli Penalization on Edges

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IEEE signal processing LETTERS 2022年 29卷 952-956页

作者： Le, Hoang Trieu Vy Foare, Marion Pustelnik, Nelly Univ Lyon Lab Phys CNRS Ens Lyon F-69342 Lyon France Univ Lyon France CNRS Ens Lyon INRIA Lyon France CPE Lyon Lyon France UC Louvain ISPGrp ICTEAM Louvain Belgium

This work is dedicated to joint image restoration and contour detection considering the Ambrosio-Tortorelli functional. Two proximal alternating minimization schemes with convergence guarantees are provided, PALM-AT and SL-PAM-AT, as well as closed-form expressions of the involved proximity operators. A thorough numerical study is conducted in order to evaluate the performance of both numerical schemes as well as comparisons to state-of-the-art Mumford-Shah strategies.

关键词： Image restoration Image edge detection signal processing algorithms Minimization Convergence TV Degradation Image restoration edge detection Mumford-Shah Ambrosio-Tortorelli proximal algorithms

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Azimuth-Elevation Direction Finding With a Pair of Acoustic Vector Sensors in the Presence of a Reflecting Boundary

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IEEE signal processing LETTERS 2022年 29卷 2138-2142页

作者： He, Jin Shu, Ting Truong, Trieu-Kien Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Key Lab Intelligent Sensing & Recognit Shanghai 200240 Peoples R China

This paper is aiming at addressing an azimuth-elevation direction-finding algorithm using a pair of identically oriented acoustic vector sensors located near a reflecting boundary. Two fourth-order cumulant matrices related in terms of a translationally invariant structure are formed for recovering the acoustic vector sensor steering vectors in the particle-velocity coarray domain. Afterward, source azimuth-elevation directions are extracted in closed-form from the estimates of the coarray steering vectors. The identifiability study shows that the algorithm proposed herein can uniquely resolve up to 13 sources, thereby being applicable to underdetermined scenarios, where the number of sources exceeds that of the sensors. This capability constitutes the primary advantage over the many seminal works Hawkes and Nehorai (2000);Wu et al. (2016);Ahmadi-Shokouh and Keshavarz (20007);Tao et al. (2007);Xu and Liu (2007) that pioneered AVS array processing involving reflecting boundaries. Finally, numerical examples are provided to verify the theoretical analysis and demonstrate the efficacy of the proposed algorithm.

关键词： Sensors signal processing algorithms Acoustics Transmission line matrix methods Tensors Matrix decomposition Velocity measurement Direction finding acoustic vector sensor coarray fourth-order cumulant

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Adaptive Kernel Kalman Filter Based Belief Propagation Algorithm for Maneuvering Multi-Target Tracking

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IEEE signal processing LETTERS 2022年 29卷 1452-1456页

作者： Sun, Mengwei Davies, Mike E. Proudler, Ian K. Hopgood, James R. Univ Edinburgh Inst Digital Commun Edinburgh EH93FG Midlothian Scotland Univ Strathclyde Dept Elect & Elect Engn Ctr Signal & Image Proc CeSIP Glasgow G1 1XW Lanark Scotland

This letter incorporates the adaptive kernel Kalman filter (AKKF) into the belief propagation (BP) algorithm for Multi-target tracking (MTT) in single-sensor systems. The algorithm is capable of tracking an unknown and time-varying number of targets, in the presence of false alarms, clutter and measurement-to-target association uncertainty. Experiment results reveal that the proposed method has a favourable tracking performance using the generalized optimal sub-patten assignment (GOSAP) metrics at substantially less computation cost than the particle filter (PF) based Multi-target tracking (MTT) BP algorithm.

关键词： signal processing algorithms Target tracking Probability density function Kalman filters Kernel Proposals Covariance matrices Adaptive kernel Kalman filter belief propagation data association multi-target tracking

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Full Mean-Square Analysis of Affine Combination of Two Complex-Valued LMS Filters for Second-Order Non-Circular Inputs

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IEEE signal processing LETTERS 2022年 29卷 1157-1161页

作者： Peng, Yishu Zhang, Sheng Zhou, Zhengchun Xia, Yili Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 611756 Sichuan Peoples R China Southeast Univ Sch Informat Sci & Engn Nanjing 210096 Peoples R China

The affine combination of two complex-valued least-mean-squares filters (aff-CLMS) addresses the trade-off between fast convergence rate and small steady-state misadjustment error. However, a rigorous analysis of the aff-CLMS algorithm for second-order non-circular inputs is still under investigation. To this end, the focus in this letter is on the full mean-square analysis of the aff-CLMS algorithm, in which the transient analyses of the mixing parameter, as well as the standard and complementary weight-error covariance matrices, are completed. In addition, we derive the closed-form solutions of the steady-state weight-error power and its complementary version of the aff-CLMS. Finally, the effectiveness of the theoretical analysis is supported by computer simulations.

关键词： Steady-state signal processing algorithms Performance analysis Covariance matrices Convergence Transient analysis Standards Adaptive filter affine combination non-circularity full mean-square convergence analysis

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