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Robust Constrained Normalized M-Estimate Subband Adaptive Filter: Algorithm Derivation and Performance Analysis

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2024年第8期71卷 4010-4014页

作者： Xu, Wenjing Zhao, Haiquan Lv, Shaohui Southwest Jiaotong Univ Key Lab Magnet Suspens Technol & Maglev Vehicle Minist Educ Chengdu 610031 Peoples R China Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Peoples R China

This brief presents a constrained normalized M-estimate subband adaptive filter (CNMSAF) algorithm that is robust to impulsive noise, which utilizes subband decomposition technique to whiten the colored input signals thereby achieving fast convergence speed and low computational complexity. In addition, the stability and theoretical mean square deviation performance of the algorithm are analyzed. In order to solve the constrained filtering problem with sparsity, the L-1 norm of the filter weight vector is used as an additional constraint, and we also propose the L-1 -CNMSAF algorithm. Computer simulations verify the accuracy of the theoretical analysis and the effectiveness of the proposed algorithms.

关键词： Vectors Adaptive filters filtering algorithms Convergence Computational complexity Performance analysis Optimization Constrained adaptive filter colored input subband adaptive filter M-estimate sparse system

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Robust Non-Linear Active Function-Based FxRLS for Impulsive Active Noise Control

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IEEE ACCESS 2024年 12卷 43222-43234页

作者： Turpati, Suman Roy, Ajay Swamy, K. C. T. Rahmani, Mohammad Khalid Imam Rehman, Masood Ur Jha, Sudan Ahmad, Sultan Nazeer, Jabeen Abdeljaber, Hikmat A. M. Aljohani, Yasser Eid G Pullaiah Coll Engn & Technol Dept Elect & Commun Engn Kurnool 518002 Andhra Prades India Lovely Profess Univ Dept Elect & Commun Engn Phagwara 144001 Punjab India Saudi Elect Univ Coll Comp & Informat Riyadh 11673 Saudi Arabia Kathmandu Univ Sch Engn Dept Comp Sci & Engn Kathmandu 45200 Nepal Prince Sattam Bin Abdulaziz Univ Coll Comp Engn & Sci Dept Comp Sci Al Kharj 11942 Saudi Arabia Chandigarh Univ Univ Ctr Res & Dev UCRD Dept Comp Sci & Engn Mohali 140413 Punjab India Appl Sci Private Univ Fac Informat Technol Dept Comp Sci Amman 11937 Jordan

Active Noise Control (ANC) is an effective technique for removing undesirable disturbances based on destructive interference between two noises (i.e., the superposition principle). To reduce the Non-Gaussian distribution of impulsive noises, the ANC is implemented using a prominent Filtered Cross Least Mean Square (FxLMS) method that relies on reducing the unwanted noise. The standard FxLMS method fails to adapt to its specifications, resulting in poor convergence and instability in the presence of impulsive noises and a non-linear response from the ANC system's components. The Least Square family of Recursive Least Square (RLS) increases ANC performance by offering superior convergence performance to traditional stochastic algorithms. This paper proposes a novel technique called the recursive non-linear active threshold-based and modified gain FXRLS (NAMGFxRLS) algorithm to overcome the inadequacies of impulsive noise and non-linearity issues in the ANC. The suggested technique aims to automatically modify weights by the various sample processes, i.e., employing the FxRLS algorithm's updated gain to adjust the error and reference signals and appropriately deploying the threshold. The potential of the suggested strategy is proved by simulated results of convergence speed, stability, and excellent Mean Noise Reduction of roughly 52.8 % for various noises when compared to previous approaches, notably large impulsive noises.

关键词： Noise reduction filtering algorithms Convergence Adaptive filters Pollution filtering theory Electronic mail Active noise control active functions FxLMS symmetric alpha stable distributions recursive least square

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NIRK-based mixed-type accurate continuous-discrete Gaussian filters with deterministically sampled expectation and covariance for state estimation in continuous-time stochastic process models with discrete measurements

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JOURNAL OF THE FRANKLIN INSTITUTE 2024年第7期361卷

作者： Kulikov, G. Yu. Kulikova, M. V. Univ Lisbon CEMAT Inst Super Tecn Ave Rovisco Pais P-1049001 Lisbon Portugal

This paper advances further the idea of overall continuous-discrete Gaussian filtering with deterministically sampled expectation and covariance towards efficient mixed -type accurate Kalman -like schemes based on self -regulated Ordinary Differential Equation (ODE) solvers with automatic local and global error controls intended for treating continuous -time nonlinear stochastic process models with discrete measurements. The universal state estimation algorithms of such sort designed recently by Kulikov and Kulikova (2022) possess three potential limitations, which might affect their successful applications in practice. First, these deal with complicated Moment Differential Equations (MDEs) and, hence, can be rather time-consuming in estimating stochastic process models of large size, that compromises their efficiency for online computations. Second, the aforementioned filters do not employ any kind of global error regulation and, hence, can be insufficiently accurate in solving difficult MDEs arisen. Third, these are hardly effective in treating stiff continuous-discrete stochastic systems. Below, all the listed issues are addressed and solved via utilization of the much simpler but more robust MDEs arisen in the usual continuous-discrete Extended Kalman Filter (EKF) within the mixedtype accurate filtering approach initiated by Kulikova and Kulikov (2015), which is rooted in the variable-stepsize Nested Implicit Runge-Kutta (NIRK) methods. For the sake of stability of our novel methods to the numerical integration and round -off errors, we devise square -root versions of the mixed -type Gaussian filters, which are based either on hyperbolic QR factorizations or on hyperbolic Singular Value Decompositions (SVDs). Performances of these new mixed -type squareroot Gaussian filters are assessed and compared to their original (non -mixed -type) counterparts as well as to their non -square -root versions in a simulated non -stiff ill -conditioned radar tracking scenario a

关键词： Stochastic systems State estimation Nonlinear filters filtering algorithms Numerical stability Radar tracking Stochastic Oregonator reaction

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Joint Design of Doppler Resilient Unimodular Discrete Phase Sequence Waveform and Receiving Filter for Multichannel Radar

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 4207-4221页

作者： Chen, Yun Chen, Zixuan Zhang, Yunhua Yang, Jiefang Li, Dong Chinese Acad Sci Natl Space Sci Ctr CAS Key Lab Microwave Remote Sensing Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China Guangdong Shenglu Telecommun Tech Co Ltd Foshan 528100 Peoples R China

Design of discrete phase sequence waveform (DPSW) with desirable co- and cross-ambiguity function (AF) properties has been a longstanding and critical challenge in the field of high-performance multichannel electronic systems, e.g. radar systems. This paper focuses on the joint design of Doppler-resilient DPSW and receiving filter with low weighted integrated sidelobe level (WISL) for multichannel radar system. This design aims to construct DPSWs of "thumbtack" shape and all-zero AFs within the desired Range-Doppler region for both co-channels and cross-channels, respectively. A peak constraint function, i.e. the penalty function, is incorporated into the objective function to control the signal-to-noise ratio loss (SNRL) due to mismatched filtering. In the design, unimodular and discrete phase constraints are imposed on each element of the sequences, while the receiving filters are subject to the energy constraint and the mismatch constraint of SNRL. Different constraints on transmitted sequences and receiving filters make the optimization problem difficult to solve. Here, an alternatively iterative algorithm based on the majorization-minimization (MM) and the coordinate descent (CD) frameworks is proposed to handle the differently constrained optimization problem. Moreover, by incorporating a general acceleration scheme and the fast Fourier transform (FFT), the computational efficiency of the proposed algorithm can be further improved. Simulation and practical experiments are conducted to validate the designed DPSWs showing superior performance when compared to that by the latest and representative methods.

关键词： Radar filtering algorithms Signal processing algorithms Radar imaging Filter banks filtering theory Radar polarimetry Multichannel radar discrete phase sequences receiving filters Doppler tolerance majorization-minimization coordinate descent alternatively iterative

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Terrain-Relative Navigation With Neuro-Inspired Elevation Encoding

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2024年第3期60卷 3368-3378页

作者： Michaelson, Kristen A. Wang, Felix Zanetti, Renato Univ Texas Austin Dept Aerosp Engn & Engn Mech Austin TX 78705 USA Sandia Natl Labs Albuquerque NM 87123 USA Univ Texas Austin Oden Inst Dept Aerosp Engn & Engn Mech Austin TX 78705 USA

Terrain-relative navigation (TRN) encompasses a wide variety of algorithms that perform localization with respect to the terrain below a flying vehicle. In traditional approaches, measurements of the terrain are matched to a map carried onboard. This work presents a TRN filter with a position measurement inspired by neural activity associated with positioning in nature. The filter is shown to produce accurate position measurements that outperform popular optimization and template matching methods given poor prior knowledge of the position. The proposed method is also better-suited to distributed implementation than optimization-based methods.

关键词： Dictionaries Inertial navigation Position measurement Phase measurement Matched filters filtering algorithms Encoding kalman filter neuro-inspired terrain-relative navigation (TRN)

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Random Filter Mappings as Optimization Problem Feature Extractors

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IEEE ACCESS 2024年 12卷 143554-143571页

作者： Petelin, Gasper Cenikj, Gjorgjina Jozef Stefan Inst Comp Syst Dept Ljubljana 1000 Slovenia Jozef Stefan Int Postgrad Sch Dept Comp Syst Ljubljana 1000 Slovenia

Characterizing optimization problems and their properties addresses a key challenge in optimization and is crucial for tasks such as creating benchmarks, selecting algorithms, and configuring them. Although several techniques have been proposed for extracting features from single-objective optimization problems, the proposed approach offers an alternative look at these problems and their properties. We propose an approach for creating problem representations by utilizing domain-specific filters. These filters have randomly initialized weights and are applied to samples of the optimization problem to extract relevant properties. Proposed features are subsequently used to classify problem instances from the Comparing Continuous Optimizers benchmark demonstrating that problem instances of the same problem tend to be situated near each other in a high-dimensional feature space. Additionally, we demonstrate that the proposed feature extraction method can be used to recognize complex characteristics of optimization functions, including multimodality and the presence of global and funnel structures. We also explore the extent to which these identified features can assist in the selection of algorithms. Our findings reveal that these features are suitable for constructing meta-models for algorithm selection, provided that the problems encountered do not substantially differ from those seen in the training phase. The proposed approach offers a versatile tool for feature extraction, highlighting its applicability across multiple tasks within the domain of optimization.

关键词： Feature extraction Optimization Information filters filtering algorithms Point cloud compression Machine learning Linear programming Representation learning Domain-specific filters random mapping feature extraction representation learning single objective numerical optimization

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An Anti-Spoofing Model Based on MVM and MCCM for a Loosely-Coupled GNSSINS LiDAR Kalman Filter

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第1期9卷 1744-1755页

作者： Chang, Jiachong Zhang, Ya Fan, Shiwei Huang, Feng Xu, Dingjie Hsu, Li-Ta Harbin Inst Technol Sch Instrumentat Sci & Engn Harbin 150006 Peoples R China Hong Kong Polytech Univ Dept Aeronaut & Aviat Engn Hong Kong Peoples R China Hong Kong Polytech Univ Dept Aeronaut & Aviat Engn Hong Kong Peoples R China

GNSS/INS/LiDAR-based Multi-Sensor Fusion (MSF) systems facilitate the efficient integration of multiple navigation sensors to deliver stable and dependable positioning outcomes in Autonomous Vehicles (AVs). Generally, AVs are bound to operate in diverse scenarios. However, state-of-the-art spoofing attack algorithms can identify vulnerable periods of MSF systems and execute aggressive GNSS spoofing in certain specific scenarios with high success rates. The present study proposes a security defense algorithm for MSF systems based on an enhanced Kalman filter, incorporating the Measurement Variance Monitoring (MVM) and Minimum Constraint of Covariance Matrix (MCCM) model. MVM effectively constrains the impact of GNSS signals with large positioning errors, thereby mitigating the adverse effects caused by spoofing attacks. MCCM fully leverages LiDAR information between two consecutive GNSS signals to enhance the credibility of LiDAR measurements, thus improving the positioning correction capability. Furthermore, considering the diverse application environments of AVs, this paper primarily focuses on sensor uncertainty variation and validates different models in varying scenarios. Ultimately, real-world data demonstrate the effectiveness of the proposed anti-spoofing model, which can significantly mitigate positioning errors caused by GNSS spoofing attacks without affecting navigation accuracy.

关键词： Global navigation satellite system Laser radar Navigation Kalman filters filtering algorithms Data models Receivers Anti-spoofing attack multi-sensors fusion loosely-coupled Kalman filter autonomous vehicles

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A Novel Digital Equalizer Based on RF Sampling Beyond GHz

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IEEE ACCESS 2024年 12卷 92560-92572页

作者： Canese, Lorenzo Cardarilli, Gian Carlo La Cesa, Riccardo Di Nunzio, Luca Fazzolari, Rocco Giardino, Daniele Re, Marco Spano, Sergio Tor Vergata Univ Rome Dept Elect Engn I-00133 Rome Italy IT Syst Srl I-00185 Rome Italy

Hardware implementations represent the major challenges when digital signal processors for ultra-wideband (UWB) signals must be developed. Due to the limitation of the maximum clock rate in digital devices, systems with high sampling rates (above GHz) cannot easily be implemented. In the literature, several works propose parallel architectures for the implementation of UWB. They are implemented on Field-Programmable Gate Arrays (FPGAs) and Application-Specific Integrated Circuits (ASICs) and try to overcome the limitation on the maximum clock frequency. In this work, a novel parallel architecture for digital UWB equalizer and an optimized version of the Least Mean Square Block (LMS) based on the Fast FIR algorithms (FFA) are presented. Circuit simulations show that the proposed equalizer can process a UWB signal with a bandwidth reaching several GHz, using the typical clock frequencies available in FPGAs. The proposed version of the Block LMS is compared with the Fast Block LMS in terms of computational complexity. It exhibits better results and greater hardware design flexibility. Finally, the hardware implementation based on a Xilinx Kintex Ultrascale to process a UWB signal sampled at 1.6 GHz is described.

关键词： Finite impulse response filters Equalizers Radio frequency Transfer functions filtering algorithms Parallel architectures Hardware Least squares approximations Channel estimation RF sampling equalization fast FIR algorithm (FFA) fast block least mean square (LMS) channel estimation

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Dual-Domain Defenses for Byzantine-Resilient Decentralized Resource Allocation

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2024年 10卷 804-819页

作者： Wang, Runhua Ling, Qing Tian, Zhi Sun Yat sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Sun Yat sen Univ Guangdong Prov Key Lab Computat Sci Guangzhou Peoples R China George Mason Univ Dept Elect & Comp Engn Fairfax VA 22030 USA

This paper investigates the problem of decentralized resource allocation in the presence of Byzantine attacks. Such attacks occur when an unknown number of malicious agents send random or carefully crafted messages to their neighbors, aiming to prevent the honest agents from reaching the optimal resource allocation strategy. We characterize these malicious behaviors with the classical Byzantine attacks model, and propose a class of Byzantine-resilient decentralized resource allocation algorithms augmented with dual-domain defenses. The honest agents receive messages containing the (possibly malicious) dual variables from their neighbors at each iteration, and filter these messages with robust aggregation rules. Theoretically, we prove that the proposed algorithms can converge to neighborhoods of the optimal resource allocation strategy, given that the robust aggregation rules are properly designed. Numerical experiments are conducted to corroborate the theoretical results.

关键词： Resource management Vectors Cost function Information processing Generators Smart grids filtering algorithms filtering Costs Collaboration Resource allocation decentralized multi-agent network Byzantine-resilience

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MSCEqF: A Multi State Constraint Equivariant Filter for Vision-Aided Inertial Navigation

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第1期9卷 731-738页

作者： Fornasier, Alessandro van Goor, Pieter Allak, Eren Mahony, Robert Weiss, Stephan Univ Klagenfurt Control Networked Syst Grp A-9020 Klagenfurt Austria Australian Natl Univ Syst Theory & Robot Lab Canberra ACT 0200 Australia

This letter re-visits the problem of visual-inertial navigation system (VINS) and presents a novel filter design we dub the multi state constraint equivariant filter (MSCEqF, in analogy to the well known MSCKF). We define a symmetry group and corresponding group action that allow specifically the design of an equivariant filter for the problem of visualinertial odometry (VIO) including IMU bias, and camera intrinsic and extrinsic calibration states. In contrast to state-of-the-art invariant extended Kalman filter (IEKF) approaches that simply tack IMU bias and other states onto the SE2 (3) group, our filter builds upon a symmetry that properly includes all the states in the group structure. Thus, we achieve improved behavior, particularly when linearization points largely deviate from the truth (i.e., on transients upon state disturbances). Our approach is inherently consistent even during convergence phases from significant errors without the need for error uncertainty adaptation, observability constraint, or other consistency enforcing techniques. This leads to greatly improved estimator behavior for significant error and unexpected state changes during, e.g., long-duration missions. We evaluate our approach with a multitude of different experiments using three different prominent real-world datasets.

关键词： filtering theory Cameras Calibration Algebra Tuning filtering algorithms Robustness Vision-based navigation visual-inertial SLAM

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