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Distributed Multiple Model filtering for Markov Jump Systems With Measurement Outliers

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2023年第3期59卷 2823-2837页

作者： Li, Hui Yan, Liping Xia, Yuanqing Beijing Inst Technol Sch Automat Key Lab Intelligent Control Decis Complex Syst Beijing 100081 Peoples R China

In this article, a distributed filtering problem is studied for a Markov jump system over sensor networks, where measurements are partially disturbed by outliers. A local multiple model filter is designed based on variational Bayesian approaches and interacting multiple model methods, the designed filter is able to identify and exclude outliers automatically, so as to mitigate the impact of outliers. A distributed filter is proposed by combining the designed local filter with consensus on information methods. Furthermore, a sufficient condition is given to guarantee the stability of the designed distributed filter, in which the estimation errors of each sensor are bounded in the mean square sense. Finally, both simulations and experiments of target tracking systems are done to show the effectiveness of the designed distributed filter.

关键词： Information filters Markov processes Estimation Robot sensing systems Loss measurement Bayes methods filtering algorithms Distributed filter Markov jump systems (MJSs) outliers sensor networks stability

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Distributed Matrix-Weighted Fusion Consensus filtering With Two-Stage filtering for Sensor Networks

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IEEE SENSORS JOURNAL 2023年第5期23卷 5003-5013页

作者： Wang, Ruixi Sun, Shuli Heilongjiang Univ Sch Elect Engn Harbin Peoples R China

This article is concerned with a distributed consensus filtering problem in a sensor network (SN). In the SN, each sensor node plays a local fusion center. By diffusion and fusion of information among sensor nodes, estimates of all nodes in the SN tend to consensus. At each sensor node, a distributed consensus filtering algorithm with a two-stage filtering structure is devised. At each time, the first stage filtering is that each sensor node produces its local filter estimate based on its own measurement and previous fused estimate, and then transmits it to its neighbor nodes;and the second stage filtering is that each sensor node produces a fused estimate by using a matrix-weighted optimal fusion algorithm in the linear unbiased minimum variance (LUMV) criterion to iteratively fuse estimates of sensor itself and neighbor nodes, where cross-covariance matrices (CCMs) between sensor nodes at different fusion times are derived. Its estimation accuracy is gradually improved with the increase of fusion times and approximates a centralized fusion filter (CFF). To avoid calculation of CCMs between sensor nodes, a suboptimal distributed consensus filtering algorithm is also presented by minimizing an upper bound of the fusion filtering error covariance at each fusion in the second stage. It has reduced computational cost at the expense of accuracy loss. The performance of the proposed distributed consensus filters is analyzed. A tracking system is used to verify the effectiveness of the algorithms.

关键词： Sensors Sensor fusion Estimation filtering algorithms Information filters Kalman filters Covariance matrices Cross-covariance matrix (CCM) distributed consensus filtering linear unbiased minimum variance (LUMV) matrix-weighted fusion sensor network (SN)

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Robust Fractional-Order-Based Von Mises Subband Adaptive filtering for Feedback Cancellation in Hearing Aids

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2025年

作者： Devi, R. Vanitha Vasundhara Natl Inst Technol Warangal Dept Elect & Commun Engn Warangal 506004 India

The issue of acoustic feedback poses a consistent challenge in the context of hearing aids since it imposes restrictions on the attainable amplification levels and has the potential to significantly diminish the perceptual audio quality through the generation of whistling sounds. In recent studies, researchers have employed delay-less multiband structured band-wise acoustic feedback alleviation techniques for hearing aids. However, the resilience of this system in the existence of non-Gaussian noise has yet to be thoroughly addressed. To tackle this matter, a robust and new cost function has been introduced based on the modification of the von Mises distribution with a scaling parameter. In addition, fractional lower-order models provide resilience to heavy-tailed distributions, enhanced precision, heightened versatility and suitability for diverse data formats. In light of this viewpoint, a robust fractional order von Mises-based subband acoustic feedback compensation technique is introduced for hearing aids. The method's efficacy has been evaluated by computer simulations involving speech and music signals at various signal-to-noise ratio (SNR) levels. The evaluation results show 2-3 dB decrease in misalignment and 2-3 dB enhancement in added stable gain in contrast to prior methodologies. Moreover, perceptual evaluation of speech quality and hearing aid speech quality indexes have been improved by approximate to 2 % and approximate to 10 % respectively as employed with the proposed technique.

关键词： Noise Cost function Hearing aids Filter banks filtering Adaptive filters Acoustics Prediction algorithms Convergence filtering algorithms Hearing aid prediction error method fractional order adaptive filtering feedback compensation

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Parameter Estimation Procedures for Deep Multi-Frame MVDR filtering for Single-Microphone Speech Enhancement

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2023年 31卷 3237-3248页

作者： Tammen, Marvin Doclo, Simon Carl von Ossietzky Univ Oldenburg Dept Med Phys & Acoust D-26129 Oldenburg Germany Carl von Ossietzky Univ Oldenburg Cluster Excellence Hearing4all D-26129 Oldenburg Germany

Aiming at exploiting temporal correlations across consecutive time frames in the short-time Fourier transform (STFT) domain, multi-frame algorithms for single-microphone speech enhancement have been proposed. Typically, the multi-frame filter coefficients are either estimated directly using deep neural networks or a certain filter structure is imposed, e.g., the multi-frame minimum variance distortionless response (MFMVDR) filter structure. Recently, it was shown that integrating the fully differentiable MFMVDR filter into an end-to-end supervised learning framework employing temporal convolutional networks (TCNs) allows for a high estimation accuracy of the required parameters, i.e., the speech inter-frame correlation vector and the interference covariance matrix. In this paper, we investigate different covariance matrix structures, namely Hermitian positive-definite, Hermitian positive-definite Toeplitz, and rank-1. The main differences between the considered matrix structures lie in the number of parameters that need to be estimated by the TCNs as well as the required linear algebra operations. For example, assuming a rank-1 matrix structure, we show that the MFMVDR filter can be written as a linear combination of the TCN outputs, significantly reducing computational complexity. In addition, we consider a covariance matrix estimation procedure based on recursive smoothing. Experimental results on the deep noise suppression challenge dataset show that the estimation procedure using the Hermitian positive-definite matrix structure yields the best performance, closely followed by the rank-1 matrix structure at a much lower complexity. Furthermore, imposing the MFMVDR filter structure instead of directly estimating the multi-frame filter coefficients slightly but consistently improves the speech enhancement performance.

关键词： Covariance matrices Noise measurement Speech enhancement Interference Estimation Transforms filtering algorithms Matrix structures multi-frame filtering MVDR filter speech enhancement supervised learning

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Know What You Don't Know: Consistency in Sliding Window filtering With Unobservable States Applied to Visual-Inertial SLAM

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IEEE ROBOTICS AND AUTOMATION LETTERS 2023年第6期8卷 3382-3389页

作者： Lisus, Daniil Cohen, Mitchell Forbes, James Richard McGill Univ Dept Mech Engn Montreal PQ H3A0G4 Canada

Estimation algorithms, such as the sliding window filter, produce an estimate and uncertainty of desired states. This task becomes challenging when the problem involves unobservable states. In these situations, it is critical for the algorithm to "know what it doesn't know", meaning that it must maintain the unobservable states as unobservable during algorithm deployment. This letter presents general requirements for maintaining consistency in sliding window filters involving unobservable states. The value of these requirements for designing navigation solutions is experimentally shown within the context of visual-inertial SLAM making use of IMU preintegration.

关键词： Simultaneous localization and mapping Estimation Jacobian matrices Observability Covariance matrices Linear programming filtering algorithms Visual-Inertial SLAM sensor fusion autonomous vehicle navigation

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Subband Adaptive filtering Based on the Squared Sine Error Criterion: Analysis and Applications

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IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2025年 33卷 1949-1959页

作者： Gong, Zhikai Li, Yingsong Huang, Xinqi Li, Liping Huang, Zhixiang de Lamare, Rodrigo. C. Zhu, Yingying Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Peoples R China Anhui Univ Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230601 Peoples R China Pontifical Catholic Univ Rio de Janeiro PUC Rio Ctr Telecommun Res CETUC BR-22451900 Gavea Brazil Univ York Sch Phys Engn & Technol York YO10 5DD England

In this work, we present a subband adaptive filtering (SAF) algorithm with good robustness for impulsive noise environments. The proposed squared sine normalized SAF (SS-NSAF) algorithm employs a squared sine function and is inspired by the idea of normalization. A statistical analysis of the proposed SS-NSAF algorithm is carried out under impulsive noise environments, which includes stability conditions for the convergence and analytical formulas to predict the mean-square deviation of the SS-NSAF algorithm. Numerical simulations for system-identification and echo cancellation applications are conducted to illustrate the performance of the SS-NSAF algorithm and competing techniques in impulsive noise environments.

关键词： Noise Prediction algorithms Signal processing algorithms filtering algorithms Nonlinear filters Maximum likelihood detection Convergence Vectors Mathematical models Information filters Echo cancellation impulsive noise subband adaptive filtering system-identification

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A Novel Three-Stage Robust Adaptive filtering Algorithm for Visual-Inertial Odometry in GNSS-Denied Environments

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IEEE SENSORS JOURNAL 2023年第15期23卷 17499-17509页

作者： Yue, Zhe Tang, Chengkai Gao, Yuting Henan Polytech Univ Sch Surveying & Land Informat Engn Jiaozuo 454003 Henan Peoples R China Northwestern Polytech Univ Sch Elect & Informat Xian 710072 Peoples R China Xian Univ Sci & Technol Sch Geomat Xian 710038 Peoples R China

Visual-inertial odometry (VIO) has been widely applied in the autonomous navigation and positioning of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) in global navigation satellite system (GNSS)-denied environments. However, existing VIO filtering algorithms have the defects of low positioning accuracy and weak robustness, especially in complex and changeable environments. Therefore, a novel robust adaptive VIO algorithm is proposed here. First, the H infinity criterion is introduced into the popular cubature multistate constraint Kalman filter (CMSCKF), which improves the robustness of the VIO system. Second, this article designs a characterization method to judge the uncertainty degree of VIO based on the limited memory exponential weighting theory. Finally, inspired by the idea of the Institute of Geodesy and Geophysics (IGG) III model, we further put forward a three-stage robust adaptive H infinity filtering algorithm and test the performance with the numerical simulation and the publicly available real-world Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) dataset. The experimental results demonstrate that the proposed algorithm has better filtering accuracy and robustness.

关键词： Cameras filtering algorithms Uncertainty Robustness Visualization Sensors Navigation Adaptive filters multistate constraint Kalman filter (MSCKF) sensor fusion visual-inertial odometry (VIO)

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Received signal strength indicator-based set-membership filtering indoor localization under multipath effect

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IET CONTROL THEORY AND APPLICATIONS 2023年第2期17卷 160-171页

作者： Yang, Bo Dong, Rongrong Shang, Junqiang Liu, Yadong Shanxi Univ Sch Math Sci Taiyuan 030006 Peoples R China Shanxi Univ Sch Automat & Software Engn Taiyuan Peoples R China

The indoor moving target localization based on the received signal strength indicator is studied in this paper. By introducing filtering algorithms, a novel dynamic parameters set-membership filtering (DPSMF) is proposed to reduce the interference of the multipath effect and noises. First, the higher-order remainder is included in the bounded range based on the interval mathematics method to improve the linearization accuracy of the Taylor series expansion of the measurement function. Second, the parameters to be determined in the channel model are set to local dynamic parameters, which are obtained by solving a particular semi-definite programming problem. Third, the optimized localization estimations of the target node at each sampling instant and the ellipsoid set containing the real state of the system are obtained based on the DPSMF. Finally, an example is given to verify the effectiveness of the proposed DPSMF algorithm.

关键词： filtering methods in signal processing Optimisation techniques ellipsoid set local dynamic parameters optimized localization estimations dynamic parameters set-membership filtering indoor radio RSSI multipath effect filtering theory filtering algorithms received signal strength indicator-based set-membership filtering interval mathematics method higher-order remainder indoor navigation wireless sensor networks target localization DPSMF Wireless sensor networks indoor localization

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Statistical Analysis of Maximum Correntropy Criterion Subband Adaptive filtering Algorithm

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IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 2025-2029页

作者： Xu, Wenjing Zhao, Haiquan Lv, Shaohui Minist Educ Key Lab Magnet Suspens Technol & Maglev Vehicle Beijing 100816 Peoples R China Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Peoples R China

The correntropy contains all the even-order information of data and is well suited for dealing with non-Gaussian noise. The subband adaptive filtering algorithm combined with the maximum correntropy criterion (MCC-SAF) has been proposed to efficiently handle colored input signals and impulsive noise. Compared with other similar algorithms, the MCC-SAF algorithm exhibits faster convergence speed and lower steady-state error in non-Gaussian noise environments. However, to the best of our knowledge, the transient and steady-state statistical performance of this algorithm is still missing. In this letter, the mean-square performance of the MCC-SAF is analyzed under Gaussian, binary, and uniform noises using vectorization method, in which the mathematical expectation of the exponential term is computed by means of the moment generating function (MGF), and finally the theoretical prediction models for mean square deviation (MSD) are derived and verified by computer simulations.

关键词： Signal processing algorithms Adaptive filters filtering algorithms Convergence Prediction algorithms filtering theory Attenuation Transient analysis Steady-state Gaussian noise Maximum correntropy criterion non-Gaussian noise performance analysis moment generating function (MGF)

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Optimal and efficient filtering algorithms for table constraints

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CONSTRAINTS 2014年第1期19卷 77-120页

作者： Mairy, Jean-Baptiste Van Hentenryck, Pascal Deville, Yves Catholic Univ Louvain Dept Comp Sci & Engn ICTEAM B-1348 Louvain Belgium NICTA Canberra ACT 2601 Australia Australian Natl Univ Canberra ACT 2601 Australia

filtering algorithms for table constraints can be classified in two categories: constraint-based and value-based. In the constraint-based approaches, the propagation queue only contains information on the constraints that must be reconsidered. For the value-based approaches, the propagation queue also contains information on the removed values. This paper proposes five efficient value-based algorithms for table constraints. Two of them (AC5TCOpt-Tr and AC5TCOpt-Sparse) are proved to have an optimal time complexity of O(r center dot t+r center dot d) per table constraint. Substantial experimental results are presented. An empirical analysis is conducted on the effect of the arity of the tables. The experiments show that our propagators are the best when the arity of the table is 3 or 4. Indeed, on instances containing only binary constraints, our algorithms are outperformed by classical AC algorithm AC3rm. AC3rm is dedicated to binary constraints. However, all our algorithms outperform existing state-of-the-art constraint based STR2+ and MDD (c) and the optimal value-based STR3 algorithms on those instances. On instances with small arity tables (up to arity 4), all our algorithms are generally faster than STR2+, MDD (c) and than STR3. AC5TCOpt-Sparse is globally the best propagator on those benchmarks. On benchmarks containing large arity tables (arity 5 or more), each of the three existing state-of-the-art algorithms is the winning strategy on one different benchmark.

关键词： Propagation Consistency filtering algorithms Table constraints

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