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Applied Sciences
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Statistical Signal Processing: Theory, Methods and Applications
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Statistical Signal Processing: Theory, Methods and Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: closed (25 April 2024) | Viewed by 7627

Special Issue Editors

Prof. Dr. Pyung Soo Kim

E-Mail Website
Guest Editor
Department of Electronics Engineering, Tech University of Korea, Siheung-si, Gyeonggi-do, Korea
Interests: statistical signal processing; optimal estimation filtering; wireless positioning systems and target tracking systems; fault detection, identification and tolerant systems
Prof. Dr. Bokyu Kwon

E-Mail Website
Guest Editor
Division of Electrical, Control & Instrumentation Engineering, Kangwon National University, Chuncheon 200-701, Korea
Interests: statistical signal processing; optimal estimation filtering; control system design

Special Issue Information

Dear Colleagues,

Through the years, problems addressed in the statistical signal processing field, a research area that broadly focuses on analyzing, modifying, and synthesizing signals such as sound, images, and scientific measurements, have become increasingly challenging. In addition, the received or measured signals are usually disturbed by environmental or intentional interferences, e.g., noise, electricity, and occlusion; therefore, the problem of filtered estimating some variables of interest from noisy observations is ubiquitous in the field of statistical signal processing. Consequently, new approaches, methods, theories, and tools are developed by the field’s community to account for modern complex, dynamic, and large-scale systems.

The main goal of this Special Issue is to introduce novel approaches from the statistical community to a wider signal processing audience, the focus including both theoretical and methodological aspects (introducing recently developed algorithms) and their applications, especially within the field of statistical signal processing. Prospective papers should be unpublished and present novel fundamental research, offering innovative contributions either from a methodological or an applications point of view.

Prof. Dr. Pyung Soo Kim
Prof. Dr. Bokyu Kwon
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • statistical signal processing
  • signal processing algorithms
  • convergence of statistical signal processing and machine learning
  • representation learning for statistical signal processing
  • estimation and filtering
  • adaptive filter and adaptive signal processing
  • statistical modeling techniques
  • measurement and observation

Published Papers (6 papers)

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Research

18 pages, 3019 KiB  
Article
Transient Analysis of a Selective Partial-Update LMS Algorithm
by Newton N. Siqueira, Leonardo C. Resende, Fabio A. A. Andrade, Rodrigo M. S. Pimenta, Diego B. Haddad and Mariane R. Petraglia
Appl. Sci. 2024, 14(7), 2775; https://doi.org/10.3390/app14072775 - 26 Mar 2024
Viewed by 357
Abstract
In applications where large-order filters are needed, the computational load of adaptive filtering algorithms can become prohibitively expensive. In this paper, a comprehensive analysis of a selective partial-update least mean squares, named SPU-LMS-M-min, is developed. By employing the partial-update strategy for a non-normalized [...] Read more.
In applications where large-order filters are needed, the computational load of adaptive filtering algorithms can become prohibitively expensive. In this paper, a comprehensive analysis of a selective partial-update least mean squares, named SPU-LMS-M-min, is developed. By employing the partial-update strategy for a non-normalized adaptive scheme, the designer can choose an appropriate number of update blocks considering a trade-off between convergence rate and computational complexity, which can result in a more than 40% reduction in the number of multiplications in some configurations compared to the traditional LMS algorithm. Based on the principle of minimum distortion, a selection criterion is proposed that is based on the input signal’s blocks with the lowest energy, whereas typical Selective Partial Update (SPU) algorithms use a selection criterion based on blocks with highest energy. Stochastic models are developed for the mean weights and mean and mean squared behaviour of the proposed algorithm, which are further extended to accommodate scenarios involving time-varying dynamics and suboptimal filter lengths. Simulation results show that the theoretical predictions are in good agreement with the experimental outcomes. Furthermore, it is demonstrated that the proposed selection criterion can be easily extended to active noise cancellation algorithms as well as algorithms utilizing variable filter length. This allows for the reduction of computational costs for these algorithms without compromising their asymptotic performance. Full article
(This article belongs to the Special Issue Statistical Signal Processing: Theory, Methods and Applications)
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23 pages, 11697 KiB  
Article
Filtering Organized 3D Point Clouds for Bin Picking Applications
by Marek Franaszek, Prem Rachakonda and Kamel S. Saidi
Appl. Sci. 2024, 14(3), 961; https://doi.org/10.3390/app14030961 - 23 Jan 2024
Viewed by 1038
Abstract
In robotic bin-picking applications, autonomous robot action is guided by a perception system integrated with the robot. Unfortunately, many perception systems output data contaminated by spurious points that have no correspondence to the real physical objects. Such spurious points in 3D data are [...] Read more.
In robotic bin-picking applications, autonomous robot action is guided by a perception system integrated with the robot. Unfortunately, many perception systems output data contaminated by spurious points that have no correspondence to the real physical objects. Such spurious points in 3D data are the outliers that may spoil obstacle avoidance planning executed by the robot controller and impede the segmentation of individual parts in the bin. Thus, they need to be removed. Many outlier removal procedures have been proposed that work very well on unorganized 3D point clouds acquired for different, mostly outdoor, scenarios, but these usually do not transfer well to the manufacturing domain. This paper presents a new filtering technique specifically designed to deal with the organized 3D point cloud acquired from a cluttered scene, which is typical for a bin-picking task. The new procedure was tested on six different datasets (bins filled with different parts) and its performance was compared with the generic statistical outlier removal procedure. The new method outperforms the general procedure in terms of filtering efficacy, especially on datasets heavily contaminated by numerous outliers. Full article
(This article belongs to the Special Issue Statistical Signal Processing: Theory, Methods and Applications)
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16 pages, 1729 KiB  
Article
Low-Complexity Data-Reuse RLS Algorithm for Stereophonic Acoustic Echo Cancellation
by Ionuț-Dorinel Fîciu, Cristian-Lucian Stanciu, Constantin Paleologu and Jacob Benesty
Appl. Sci. 2023, 13(4), 2227; https://doi.org/10.3390/app13042227 - 09 Feb 2023
Cited by 1 | Viewed by 1323
Abstract
Stereophonic audio devices employ two loudspeakers and two microphones in order to create a bidirectional sound effect. In this context, the stereophonic acoustic echo cancellation (SAEC) setup requires the estimation of four echo paths, each one corresponding to a loudspeaker-to-microphone pair. The widely [...] Read more.
Stereophonic audio devices employ two loudspeakers and two microphones in order to create a bidirectional sound effect. In this context, the stereophonic acoustic echo cancellation (SAEC) setup requires the estimation of four echo paths, each one corresponding to a loudspeaker-to-microphone pair. The widely linear (WL) model was proposed in recent literature in order to simplify the handling of the SAEC mathematical model. Moreover, low complexity recursive least- squares (RLS) adaptive algorithms were developed within the WL framework and successfully tested for SAEC scenarios. This paper proposes to apply a data-reuse (DR) approach for the combination between the RLS algorithm and the dichotomous coordinate descent (DCD) iterative method. The resulting WL-DR-RLS-DCD algorithm inherits the convergence properties of the RLS family and requires an amount of mathematical operations attractive for practical implementations. Simulation results show that the DR approach improves the tracking capabilities of the RLS-DCD algorithm, with an acceptable surplus in terms of computational workload. Full article
(This article belongs to the Special Issue Statistical Signal Processing: Theory, Methods and Applications)
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11 pages, 548 KiB  
Article
A Novel Approach for Selecting Effective Threshold Values in Ternary State Estimation Using Particle Swarm Optimization
by Somayeh Davar and Thomas Fevens
Appl. Sci. 2022, 12(21), 10693; https://doi.org/10.3390/app122110693 - 22 Oct 2022
Cited by 1 | Viewed by 1211
Abstract
Inspired by recent breakthroughs in cyber-physical systems (CPSs) and their applications, in this paper, we propose a novel multi-objective method to optimize the threshold values within the ternary event-based framework. To reduce communication overhead, the particle swarm optimization (PSO) approach is applied as [...] Read more.
Inspired by recent breakthroughs in cyber-physical systems (CPSs) and their applications, in this paper, we propose a novel multi-objective method to optimize the threshold values within the ternary event-based framework. To reduce communication overhead, the particle swarm optimization (PSO) approach is applied as an optimizer to identify Pareto optimal set values of the threshold. The proposed optimization technique is subject to constraints to ensure its feasibility. The simulation results confirm the efficiency of the recommended method. Furthermore, the simulation results demonstrate that the proposed framework is comprehensive and capable of finding a wide variety of Pareto optimal ternary event-based state estimations for each predefined threshold. Full article
(This article belongs to the Special Issue Statistical Signal Processing: Theory, Methods and Applications)
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29 pages, 1243 KiB  
Article
Novel Unbiased Optimal Receding-Horizon Fixed-Lag Smoothers for Linear Discrete Time-Varying Systems
by Bokyu Kwon and Pyung Soo Kim
Appl. Sci. 2022, 12(15), 7832; https://doi.org/10.3390/app12157832 - 04 Aug 2022
Cited by 1 | Viewed by 946
Abstract
This paper proposes novel unbiased minimum-variance receding-horizon fixed-lag (UMVRHF) smoothers in batch and recursive forms for linear discrete time-varying state space models in order to improve the computational efficiency and the estimation performance of receding-horizon fixed-lag (RHF) smoothers. First, an UMVRHF smoother in [...] Read more.
This paper proposes novel unbiased minimum-variance receding-horizon fixed-lag (UMVRHF) smoothers in batch and recursive forms for linear discrete time-varying state space models in order to improve the computational efficiency and the estimation performance of receding-horizon fixed-lag (RHF) smoothers. First, an UMVRHF smoother in batch form is proposed by combining independent receding-horizon local estimators for two separated sub-horizons. The local estimates and their error covariance matrices are obtained based on an optimal receding horizon filter and the smoother in terms of the unbiased minimum variance; they are then optimally combined using Millman’s theorem. Next, the recursive form of the proposed UMVRHF smoother is derived to improve its computational efficiency and extendibility. Additionally, we introduce a method for extending the proposed recursive smoothing algorithm to a posteriori state estimations and propose the Rauch–Tung–Striebel receding-horizon fixed-lag smoother in recursive form. Furthermore, a computational complexity reduction technique that periodically switches the two proposed recursive smoothing algorithms is proposed. The performance and effectiveness of the proposed smoothers are demonstrated by comparing their estimation results with those of previous algorithms for Kalman and receding-horizon fixed-lag smoothers via numerical experiments. Full article
(This article belongs to the Special Issue Statistical Signal Processing: Theory, Methods and Applications)
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26 pages, 1132 KiB  
Article
Efficient Algorithms for Linear System Identification with Particular Symmetric Filters
by Ionuţ-Dorinel Fîciu, Jacob Benesty, Laura-Maria Dogariu, Constantin Paleologu and Silviu Ciochină
Appl. Sci. 2022, 12(9), 4263; https://doi.org/10.3390/app12094263 - 23 Apr 2022
Cited by 5 | Viewed by 1502
Abstract
In linear system identification problems, it is important to reveal and exploit any specific intrinsic characteristic of the impulse responses, in order to improve the overall performance, especially in terms of the accuracy and complexity of the solution. In this paper, we focus [...] Read more.
In linear system identification problems, it is important to reveal and exploit any specific intrinsic characteristic of the impulse responses, in order to improve the overall performance, especially in terms of the accuracy and complexity of the solution. In this paper, we focus on the nearest Kronecker product decomposition of the impulse responses, together with low-rank approximations. Such an approach is suitable for the identification of a wide range of real-world systems. Most importantly, we reformulate the system identification problem by using a particular symmetric filter within the development, which allows us to efficiently design two (iterative/recursive) algorithms. First, an iterative Wiener filter is proposed, with improved performance as compared to the conventional Wiener filter, especially in challenging conditions (e.g., small amount of available data and/or noisy environments). Second, an even more practical solution is developed, in the form of a recursive least-squares adaptive algorithm, which could represent an appealing choice in real-time applications. Overall, based on the proposed approach, a system identification problem that can be conventionally solved by using a system of L=L1L2 equations (with L unknown parameters) is reformulated as a combination of two systems of PL1 and PL2 equations, respectively, where usually P≪L2 (i.e., a total of PL1+PL2 parameters). This could lead to important advantages, in terms of both performance and complexity. Simulation results are provided in the framework of network and acoustic echo cancellation, supporting the performance gain and the practical features of the proposed algorithms. Full article
(This article belongs to the Special Issue Statistical Signal Processing: Theory, Methods and Applications)
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