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Lightweight Marginalized Particle filtering With Enhanced Consistency for Terrain Referenced Navigation

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2022年第3期58卷 2493-2504页

作者： Choe, Yeongkwon Song, Jin Woo Park, Chan Gook Seoul Natl Univ Dept Aerosp Engn Seoul 08826 South Korea Seoul Natl Univ Automat & Syst Res Inst Seoul 08826 South Korea Sejong Univ Sch Intelligent Mechatron Engn & Convergence Engn Seoul 05006 South Korea Seoul Natl Univ Inst Adv Aerosp Technol Seoul 08826 South Korea

This article proposes a computationally lightweight marginalized particle filtering (MPF) algorithm with improved filter consistency. For mixed linear/nonlinear state-space models such as terrain referenced navigation (TRN) models, an MPF can improve the convergence and efficiency of the filters by separately applying a Kalman filter (KF) and a particle filter (PF) to each state space rather than using only one type of filter. However, an MPF still has a high degree of complexity because it requires the same number of KFs as the number of particles in the PF. To address this issue, our method simplifies the MPF through Gaussian approximation to use only one KF and improves the filter consistency compared to other MPF simplification methods. The proposed method further reduces the complexity by considering the dynamic model of a TRN system. This article presents a complexity analysis between the original MPF and other MPF simplification methods and numerical experiments for a range/bearing example and a TRN system. The results show that the proposed algorithm has better filter consistency than the existing simplification algorithms and can achieve performance comparable to an MPF with less computation.

关键词： filtering Complexity theory Nonlinear filters filtering algorithms Aerodynamics Particle filters Navigation Importance sampling (IS) marginalized particle filter (MPF) nonlinear filtering Rao-Blackwellization terrain referenced navigation (TRN)

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HIGH-SPEED ARCHITECTURES FOR 2-DIMENSIONAL STATE-SPACE RECURSIVE filtering

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS 1990年第6期37卷 831-836页

作者： ZHANG, JY STEENAART, W Department of Electrical Engineering University of Ottawa ONT Canada

A description is given of 2-D state-space filter algorithms, and two VLSI architectures for high-speed applications are proposed. The advanced state update architecture trades increased throughput rate and a simple input-output scheme for increased hardware. The technique is based on block processing. The state update matrix is changed by recursive multiplications, and the advanced state vector can be calculated. This architecture is a two-dimensional systolic array. It can process one pixel during each multiplication/addition clock period with a regular line-by-line scanning method. To achieve very high speed, the global speedup architecture is utilized by exploiting the inherent spatial concurrency. This architecture consists of a number of column array processors. Based on the computational independence between the samples along the diagonal lines in a first-quadrant 2-D system, it works on multiple columns of images concurrently. The architecture is very modular and flexible. The throughput rate can be very high and is adjustable by changing the number of column processors. Different high-speed architectures for image processing are compared.< >

关键词： Computer architecture Digital filters Very large scale integration Concurrent computing Image processing Throughput Clocks filtering algorithms Hardware Systolic arrays

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Parallelization Strategy of Non-Local Means filtering Algorithm for Real-Time Denoising of Forward-Looking Multi-Beam Sonar Images

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第12期34卷 13226-13243页

作者： Ye, Tao Deng, Xiangpeng Cong, Xiao Zhou, Hongkun Yan, Xiangming China Univ Min & Technol Beijing Sch Mech & Elect Engn Minist Emergency Management Beijing 100083 Peoples R China China Univ Min & Technol Beijing Key Lab Intelligent Min & Robot Minist Emergency Management Beijing 100083 Peoples R China China Ship Sci Res Ctr Wuxi 214028 Peoples R China

Obtaining clear sonar images is crucial for ocean exploration applications, such as marine resource detection and underwater target searches. Traditional filtering methods cannot effectively eliminate the noise generated by the complex underwater environment in sonar images and can potentially result in problems such as image blurring. Existing methods that effectively filter sonar image noise often lack real-time performance, making them impractical for ocean exploration. To address these limitations, this study proposes a real-time denoising technique for forward-looking multi-beam sonar images based on a non-local means filtering algorithm. The integral image is used to calculate the mean square error (MSE), which improves algorithm efficiency and ensures that the runtime remains unaffected by the neighbourhood window size. To further improve real-time performance, the algorithm is migrated to a graphics processing unit (GPU) and a block-wise computation method is proposed to calculate the integral image. Simultaneously, to enhance GPU thread utilisation, the three-dimensional thread structure from the compute unified device architecture (CUDA) programming model is utilised and additional threads are allocated to enhance computation. The captured images are filtered using an M1200d sonar device manufactured by Oculus. Extensive experiments demonstrate that the proposed method achieves excellent performance regarding both denoising accuracy and efficiency. Specifically, the proposed method achieves a peak signal-to-noise ratio higher than 25 dB and a structural similarity index of more than 0.85 at 50 frames per second, thus demonstrating its significant potential for real-time sonar image denoising.

关键词： Sonar filtering algorithms Real-time systems Graphics processing units Noise reduction filtering Mathematical models Sonar sensors non-local means filtering real-time processing GPU parallelisation

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ADAPTIVE filtering IN SUBBANDS WITH CRITICAL SAMPLING - ANALYSIS, EXPERIMENTS, AND APPLICATION TO ACOUSTIC ECHO CANCELLATION

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 1992年第8期40卷 1862-1875页

作者： GILLOIRE, A VETTERLI, M COLUMBIA UNIV CTR TELECOMMUN RESDEPT ELECT ENGNNEW YORKNY 10027

Adaptive filtering in subbands is a new technique for the real-time identification of large impulse responses like the ones encountered in acoustic echo cancellation. This technique generally allows computational savings as well as better convergence behavior. We give first an exact analysis of the critically subsampled two band modelization scheme. We demonstrate that adaptive cross-filters between the subbands are necessary for modelization with small output errors;moreover, we show that perfect reconstruction filter banks can yield exact modelization. We extend those results to the critically subsampled multiband schemes, and we show that important computational savings can be achieved by using good quality filter banks. Then we consider the problem of adaptive identification in critically subsampled subbands, and we derive an appropriate adaptation algorithm. We give a detailed analysis of the computational complexity of all the discussed schemes, and we verify experimentally the theoretical results that we have obtained. Finally, we discuss the adaptive behavior of the subband schemes that we have tested. We generally observe some degradation of the convergence performance in comparison with conventional schemes;however, the overall performance could be acceptable in practical use.

关键词： Adaptive filters Sampling methods Filter bank Echo cancellers Acoustic applications Convergence filtering algorithms Computational complexity Yield estimation Least squares approximation

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Multi-focus image fusion with alternating guided filtering

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SIGNAL IMAGE AND VIDEO PROCESSING 2019年第4期13卷 727-735页

作者： Zhang, Yongxin Wei, Wei Yuan, Yating Luoyang Normal Univ Luoyang 471022 Peoples R China Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Shaanxi Peoples R China Northwest Univ Sch Informat Sci & Technol Xian 710048 Shaanxi Peoples R China

Due to depth-of-field limitations, existing optical sensors cannot usually obtain a focused image that contains all of the objects in the same scene at different distances. These partially focused images cannot effectively represent the scene and may potentially hinder a complete and accurate understanding of the scene, or even the performance of further computer processes. Multi-focus image fusion can address this problem by fusing multiple images with different focus settings from the same scene into one focused image. This paper proposes a novel multi-focus image fusion method with alternating guided filtering (AGF). First, the source images are decomposed by AGF into base layers containing large-scale variations in intensity and detail layers containing small-scale details. Second, the base and detail layers are fused with saliency maps, which have been filtered using the different layers gradient features. The final fused image is obtained through a recombination of the fused base and detail layers. The experimental results demonstrate that, in terms of visual and quantitative evaluations, the proposed method is capable of representing source images well and demonstrates a significant improvement in fusion quality in comparison with other existing fusion methods.

关键词： Image fusion filtering algorithms Image decomposition Alternating guided filtering

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A SIMPLE TREATMENT FOR SUBOPTIMAL KALMAN filtering IN CASE OF MEASUREMENT DATA MISSING

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 1990年第2期26卷 413-415页

作者： CHEN, GR Dept. of Electr. & Comput. Eng. Rice Univ. Houston TX USA

A very simple yet efficient suboptimal Kalman filtering algorithm is proposed for the standard filtering process in an environment where some measurement data are missing. A convergence analysis for the algorithm is g... 详细信息

关键词： Kalman filters Computer aided software engineering Convergence State estimation filtering algorithms Measurement standards Information filtering Information filters Algorithm design and analysis Smoothing methods

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Wavelet-based linear system modeling and adaptive filtering

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 1996年第5期44卷 1156-1167页

作者： Doroslovacki, MI Fan, H UNIV CINCINNATI DEPT ELECT & COMP ENGNCINCINNATIOH 45221

It is shown how linear time-varying systems can be modeled in several different ways by discrete-time wavelets or, more generally, by some set of functions. Interpretation of physical meanings, possible efficiency, and other characteristics of the modeling are considered. System identification minimizing the mean square output error is studied, Optimal coefficients and the corresponding minimum mean square error are found, and they are, in general, time varying. Least-mean-square adaptive filtering algorithms are derived for on-line filtering and system identification. Theoretically and by simulations, the advantages of using wavelet-based filtering are shown: separation of adaptation effects from unknown time-varying system behavior and fast convergence. Adaptive coefficients estimated by a recursive-least-square algorithm can tend toward constants, even in the case of time-varying systems. Time-invariant system identification and adaptive filtering is given as a special case of the general time-varying setting.

关键词： Linear systems Adaptive filters Time varying systems System identification Discrete wavelet transforms Power system modeling Senior members Mean square error methods filtering algorithms Convergence

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Fast algorithm for FIR filtering in the transform domain

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 1996年第1期44卷 126-129页

作者： Chiang, HC Liu, JC Department of Electrical Engineering Tatung Institute of Technology Taipei Taiwan

In this correspondence, a more efficient algorithm than the overlap-and-add (or overlap-and save) method is developed for quickly computing the convolution involving very long impulse response. By dividing the input and the impulse response into segments of proper length, the algorithm requires fewer multiplications than the existing methods and therefore reduces the complexity of the filtering process.

关键词： Finite impulse response filter filtering algorithms Signal processing algorithms Convolution Algorithm design and analysis Data communication Computational complexity Adaptive filters Nonlinear filters Speech processing

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An Algorithm Fusing State Estimation and TDOA filtering for UAV Tracking Enhancement

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2024年第2期60卷 2251-2266页

作者： Zhang, Zhaohang Huang, Zhen Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

In wireless sensor network positioning algorithms, tracking highly dynamic targets, such as unmanned aerial vehicles (UAVs), is challenging. In a poor communication environment, the accuracy of intermediate parameter estimation, such as the time difference of arrival (TDOA), is degraded, and the fact that the state transition equation for TDOA has no sequential solution makes it difficult to construct filtering algorithms. In this study, a data-driven multiple-model Kalman filter is proposed to enhance the tracking performance of highly dynamic UAVs in poor communication scenarios. The proposed algorithm builds an approximate model of motion states and constructs an augmented state transition model based on the measurements, which is applicable regardless of whether the state variables have an explicit sequential solution. In addition, considering the nonorthogonal characteristics between measurement noise and process noise, the process equation and Kalman gain of the proposed algorithm are derived to consider the influence of nonorthogonal terms, and the corresponding Cramer-Rao lower bound for the proposed algorithm is provided. Through simulation experiments, the convergence speed, filtering error, signal-to-noise ratio robustness, and computational complexity of the proposed algorithm are compared with those of traditional filtering algorithms, and its superiority in multiple-model motion scenarios is also verified using real-world data.

关键词： Location awareness filtering algorithms Wireless sensor networks Mathematical models filtering Signal to noise ratio Kalman filters Kalman filtering localization enhancement time difference of arrival (TDOA) tracking

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CONJUGATE-GRADIENT METHOD IN ADAPTIVE BILINEAR filtering

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 1995年第6期43卷 1503-1508页

作者： BOSE, T CHEN, MQ THE CITADEL DEPT MATH & COMP SCICHARLESTONSC 29409

The application of the Conjugate Gradient (CG) method for the identification of bilinear systems is investigated. An algorithm based on the CG method is developed for adaptive bilinear digital filtering, In this algorithm, the optimization is done over blocks of input and output data rather than a single pair of data. However, only one iteration and coefficient update is done for every sample of data. This, coupled with the fact that the CG method used-here does not require a line search makes it very efficient in computation. Simulation results show that this algorithm outperforms the LMS and RLS algorithms in terms of speed of convergence. A preconditioning technique is also applied to further accelerate the convergence in some cases.

关键词： Gradient methods Adaptive filters Character generation Convergence Nonlinear systems filtering algorithms Digital filters Computational modeling Least squares approximation Resonance light scattering

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