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A LEVINSON-TYPE ALGORITHM FOR A CLASS FOR NON-TOEPLITZ SYSTEMS WITH APPLICATIONS TO MULTICHANNEL IIR filtering

IEEE TRANSACTIONS ON SIGNAL PROCESSING 1994年第12期42卷 3309-3320页

作者： PAN, JQ LEVINE, WS UNIV MARYLAND SYST RES CTRCOLLEGE PKMD 20742 UNIV MARYLAND DEPT ELECT ENGNCOLLEGE PKMD 20742

A very flexible Levinson-type recursion for a class of non-Toeplitz systems of linear equations is demonstrated. A complete solution is expressed as a linear combination of a partial solution and three auxiliary solutions. The class of systems possesses a special structure in that the coefficient matrices can be partitioned into four block Toeplitz submatrices. The number of multiplications and additions required to compute an n-dimensional solution is O(n2). The recursion is then applied to multichannel IIR filtering. Specifically, a lattice structure is established for linear minimum mean square error predictors having independently and arbitrarily specified numbers of poles and zeros. Next the recursion is used to develop a fast time and order recursive least-squares algorithm for ARX system identification. The novelty of the algorithm is that it can be used to efficiently determine parameter estimates of a family of ARX models.

关键词： IIR filters Signal processing algorithms Equations Nonlinear filters Lattices Mean square error methods filtering algorithms Adaptive filters Poles and zeros System identification

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Using empirical mode decomposition and fractional Fourier transform-segment filtering algorithm to suppress high-power interference in non-linear chirp radars

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IET RADAR SONAR AND NAVIGATION 2016年第5期10卷 892-900页

作者： Elgamel, Sherif A. Mil Tech Coll Dept Elect Warfare Cairo Egypt

A jamming filtering algorithm in linear frequency modulation (FM) chirp radar systems is introduced before depending on the conjunction work of empirical mode decomposition (EMD) and fractional Fourier transform (FrFT). A new jamming filtering algorithm (EMD-FrFT-segment filtering) based on EMD-FrFT is carried out for non-linear (logarithmic) chirp radar systems. EMD-FrFT-segment filtering algorithm is used as a pre-filtering stage to decrease the additive high-power interference signal to the radar useful signal. The idea of the proposed EMD-FrFT-segment filtering algorithm is to break down the logarithmic chirp into a set of approximately linear chirps. Each approximate linear chirp segment is filtered using FrFT filtering before appending all these filtered segments to reconstruct the received filtered vector. Up to 18 dB improvement in the signal-to-f is achieved for different target signal-to-interference noise ratio.

关键词： Chirp modulation Empirical mode decomposition Power Disturbances filtering algorithms Radar systems Fourier Transform HIGH POWER

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Point-Mass filtering With Boundary Flow and Its Application to Terrain Referenced Navigation

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2021年第6期57卷 3600-3613页

作者： Choe, Yeongkwon Park, Chan Gook Seoul Natl Univ Dept Aerosp Engn Seoul 08826 South Korea Seoul Natl Univ Automat & Syst Res Inst Seoul 08826 South Korea Seoul Natl Univ Inst Adv Aerosp Technol Seoul 08826 South Korea

Point-mass filter (PMF) is a numerical Bayesian filtering algorithm that estimates the probability density of state variables using a deterministically defined grid on state space. An important factor that determines the performance of the PMF is how accurately one guesses the probabilistically significant region (called a nonnegligible region) where a grid will be placed. In this article, we introduce the concept of boundary to express the nonnegligible region and propose a method to define a grid tailored to the nonnegligible region of posterior density by transporting boundary according to log-homotopy induced flow. The proposed method ensures accurate estimation performance and robustness especially for uncertain prior information. Terrain referenced navigation (TRN) is composed of a nonlinear observation model defined by terrains and an uncertain initial position. Therefore, it is a major application of the PMF, which can handle nonlinearity and has a more robust characteristic than other numerical filters. In order to compare the proposed PMF with the conventional PMFs, this article applies the proposed method to TRN. Monte Carlo simulation results show that the proposed algorithm is more accurate and robust than the conventional PMFs.

关键词： filtering algorithms Mathematical model Bayes methods Current measurement Approximation algorithms Probability density function Information filters Log-homotopy induced flow particle flow filter point-mass filter (PMF) recursive estimation terrain referenced navigation (TRN)

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CONVERGENCE ANALYSIS FOR INEXACT MECHANIZATION OF KALMAN filtering

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 1992年第3期28卷 612-621页

作者： CHEN, GR Department of Electrical Engineering University of Houston Houston TX USA

A computational aspect of real-time estimation algorithm implementation is considered, in which the estimation algorithm to be used is defined having the standard optimal Kalman filtering structure but with the actual inverse matrix within the Kalman gain being replaced by an expedient approximation at each time instant. In real-time applications, most Kalman filtering schemes are approximate to a degree as a consequence of computational errors in calculating matrix inversion due to numerical roundoff. Convergence properties and error estimates of such approximate mechanizations of the Kalman filtering algorithm are obtained here to provide a theoretical basis for gauging the utility of using computational approximation of the Kalman gain matrix at each time instant viz use of exponentially convergent sequences for comparison. A new exponentially convergent scheme is also suggested for approximating the inverse matrix within the Kalman gain. Conditions are offered when on-line approximate matrix inversion can be eliminated as the scapegoat cause of Kalman filter divergence in real-time mechanization leaving only model mismatch as the likely culprit when divergence occurs.

关键词： Convergence Kalman filters Approximation algorithms filtering algorithms

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A fast least-squares algorithm for linearly constrained adaptive filtering

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 1996年第5期44卷 1168-1174页

作者： Resende, LS Romano, JMT Bellanger, MG CONSERVATOIRE NATL ARTS & METIERS LAB ELECT & COMMUNPARISFRANCE

An extension of the field of fast least-squares techniques is presented. It is shown that the adaptation gain, which is updated with a number of operations proportional to the number of transversal filter coefficients, can be used to update the coefficients of a linearly constrained adaptive filter. An algorithm that is robust to round-off errors is derived. It is general and flexible. It can handle multiple constraints and multichannel signals. Its performance is illustrated by simulations and compared with the classical LMS-based Frost algorithm.

关键词： filtering algorithms Adaptive filters Robustness Signal processing Transversal filters Roundoff errors Art Signal analysis Adaptive arrays Array signal processing

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Iterative Self-Guided Image filtering

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第8期34卷 7537-7549页

作者： He, Lei Xie, Yongfang Xie, Shiwen Jiang, Zhaohui Chen, Zhipeng Hunan Univ Sci & Technol Sch Informat & Elect Engn Xiangtan 411100 Peoples R China Cent South Univ Sch Automat Changsha 410083 Peoples R China Peng Cheng Lab Shenzhen Peoples R China

Edge preserving filter is the basis of many computational photography and image processing. This can be achieved by global optimization method or local filtering method. Generally, the filtering results of global optimization methods are better than that of local filtering methods, and local filtering methods usually run much faster than global optimization methods. In this paper, a globally optimized method called iterative self-guided image filter (isGIF) is extended based on the assumptions of the guided image filter (GIF), which can produce high-quality edge-preserving filtering results by using the input image itself as the guidance image. Some comparisons with other edge-aware filters are presented to show the advantages of our method. Extensive experiments demonstrate that our filter generates images with better visual quality, while reducing/avoiding halo artifacts in the final image, and the running time is competitive.

关键词： Image edge detection Smoothing methods Information filters Optimization methods Nonlinear filters Maximum likelihood detection filtering algorithms Edge-preserving filtering guided image filter (GIF) global optimization local filtering methods

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Transfer Learning for Heterogeneous One-Class Collaborative filtering

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IEEE INTELLIGENT SYSTEMS 2016年第4期31卷 43-49页

作者： Pan, Weike Liu, Mengsi Ming, Zhong Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China

Heterogeneous one-class feedback has been recognized as an important source of information in recommendation systems. Joint similarity learning has the merit of being able to connect two seemingly unrelated items alon... 详细信息

关键词： filtering theory Algorithm design and analysis Negative feedback filtering algorithms Learning systems Feedback INSPEC: Controlled Indexing recommender systems collaborative filtering learning (artificial intelligence) INSPEC: Non-Controlled Indexing sparse positive feedback memory-based collaborative filtering algorithms model-based collaborative filtering algorithms multiclass feedback recommendation systems heterogeneous one-class collaborative filtering HOCCF transfer via joint similarity learning TJSL Author Keywords intelligent systems transfer learning collaborative filtering heterogeneous one-class feedback

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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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Depth Map Super Resolution Using Structure-Preserving Guided filtering

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IEEE SENSORS JOURNAL 2022年第13期22卷 13144-13152页

作者： Khoddami, Ali Asghar Moallem, Payman Kazemi, Mohammad Univ Isfahan Dept Elect Engn Esfahan *** Iran

Due to the limited resolution of depth maps captured by RGB-D sensors, depth map Super Resolution (SR) techniques have received a lot of attention. Intensity guided depth map SR methods based on bilateral filter or guided image filter are commonly used for depth upsampling. Although promising edge-preserving results have been reported in these methods, texture-copying artifacts caused by structure discrepancy between depth map and associated intensity image cannot be addressed, easily. In this paper we aim to balance the trade-off between preserving structure and suppressing texture defects. Based on this, a structure-preserving guided filter is presented that not only keeps the advantages of aforementioned methods, but also overcomes texture-copying artifacts. Unlike conventional guided filtering-based methods which rely on only one guidance, we emphasize on the use of both intensity and depth information as guidance to alleviate the deficiencies of the existing works. We replace the mean filtering scheme in guided filters with a weighted average strategy, where the weights are described by the local depth kernel depended on the input depth map. This enables our method to considerably reduce texture-copying artifacts while preserving 3D structural details. Visual evaluation of results shows that the algorithm can also avoid halo artifacts near the edges whereas traditional guided filters suffer from it. Quantitative results of comprehensive experiments demonstrate the effectiveness of our approach over prior depth map SR works.

关键词： Image edge detection Information filters filtering algorithms Sensors Color Three-dimensional displays Tensors Depth map super-resolution guided image filter texture-copying structure-preserving filter

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Affine-Projection-Like M-Estimate Adaptive Filter for Robust filtering in Impulse Noise

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2019年第12期66卷 2087-2091页

作者： Song, Pucha Zhao, Haiquan Southwest Jiaotong Univ Key Lab Magnet Suspens Technol & Maglev Vehicle Minist Educ Chengdu 610031 Sichuan Peoples R China Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Sichuan Peoples R China

In this brief, an affine-projection-like M-estimate (APLM) algorithm is proposed for robust adaptive filtering. To eliminate the adverse effects of impulsive noise in case of the impulse interference environment on the filter weight updates. The proposed APLM algorithm uses a robust cost function based on M-estimate and is derived by using the unconstrained minimization method. More importantly, the APLM algorithm has lower computational complexity than the M-estimate affine projection algorithm, since the direct or indirect inversion of the input signal matrix does not need to be calculated. In order to further improve the performance of the APLM algorithm, namely convergence speed and steady-state misalignment, the convex combination of the APLM (C-APLM) algorithm is presented. Simulation results verify that the proposed APLM and C-APLM algorithms are effective in system identification and echo cancellation scenarios. It also demonstrates that the C-APLM algorithm improves the filter performance in terms of the convergence speed and the normalized mean squared deviation in the presence of impulse noise.

关键词： Signal processing algorithms Convergence Cost function Adaptive filters filtering algorithms Steady-state Computational complexity Affine-projection-like M-estimate impulse noise computational complexity convex combination

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