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Systems of Signals Generating and Processing in the Field of on Board Communications

作者： A. A. Chugunov R. S. Kulikov V. B. Pudlovskiy N. I. Petukhov N. V. Masalkova National Research University MPEI Moscow Russia

In this article, an algorithm for determining RRS coordinates on the basis of Extended Kalman Filter with the processing of Time of Arrival (ToA) type measurements is proposed. The feature of the proposed algorithm is to determine the radio radiation source (RRS) time of transmission (ToT) in the MLAT system time scale and its filtration using priori information about the period of transmission. It's shown that evaluation of ToT and its filtration at a known period allows you to get closer to the accuracy provided by Time Of Fligth (ToF) method, which can not be implemented directly for MLAT systems. This hypothesis was confirmed by modeling several algorithms: one-step TDoA, filtered TDoA, filtered ToA and one-step ToF. Modeling was performed for both the known constant period and the non constant period. In both cases, the efficiency of the proposed ToA-F algorithm was the same. The efficiency of various algorithms was compared, including the traditional ones based on the calculation of arrival time differences and the proposed one based on ToA observations and using information about transmission period. The most important result was that the ToT estimation and filtering allows to achieve the properties of the ToF method in the MLAT system.

关键词： Location awareness Coordinate measuring machines Filtration Estimation filtering algorithms Information filters Time measurement

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Application of Polynomial filtering algorithms to Nonlinear Discrete Dynamical Systems in Navigation Data Processing

Application of Polynomial Filtering Algorithms to Nonlinear ...

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European Control Conference (ECC)

作者： Oleg A. Stepanov Vladimir A. Vasiliev Mihail V. Basin Viktor A. Tupysev Yulia A. Litvinenko Concern CSRI Elektropribor JSC ITMO University Saint Petersburg Russia JSC Concern CSRI Elektropribor Saint Petersburg Russia Department of Physical and Mathematical Sciences Autonomous University of Nuevo Leon San Nicolas de los Garza Mexico Concern CSRI Elektropribor JSC State University of Aerospace Instrumentation Saint Petersburg Russia Concern CSRI Elektropribor ITMO University JSC Saint Petersburg Russia

ISBN: (数字)9783907144022

ISBN: (纸本)9781728188133

A polynomial filtering algorithm is designed for estimating a Markov sequence with the use of linear measurements. The feature of the estimation problem is that the Markov sequence is described by a nonlinear shaping filter, which is a second-order polynomial with respect to the state vector components. The efficiency of the proposed algorithm is analyzed in comparison with the nonlinear optimal algorithm and an extended Kalman filter, when solving the problem of gyroscope drift model identification.

关键词： Heuristic algorithms Mathematical model Navigation Estimation Covariance matrices Electronic mail filtering algorithms

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Antenna Arrays Calibration Using Recursive Least Squares Adaptive filtering algorithms Based on Inverse QR Decomposition

Antenna Arrays Calibration Using Recursive Least Squares Ada...

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East-West Design & Test Symposium (EWDTS)

作者： Victor Djigan Vladislav Kurganov Department of Integrated Circuits Design Methodology Institute for Design Problems in Microelectronics of RAS Moscow Russia Institute of Microdevices and Control Systems National Research University of Electronic Technology Moscow Russia

ISBN: (数字)9781728198996

ISBN: (纸本)9781728199009

This paper considers the antenna arrays calibration by the using of the Recursive Least Squares (RLS) adaptive filtering algorithms. An algorithm, based on the inverse QR decomposition, has been selected among the diversity of the RLS algorithms. This is caused by its stable operation. Because the algorithm contains the computationally heavy square root operations, a square root free version of the algorithm is also presented. Both versions of the QR RLS algorithms are mathematically identical to each other if they operate in float point arithmetic. The proposed calibration can be used in the antenna arrays with digital beamforming, because the algorithms usage requires the access to the array channel signals. The calibration requires a known training signal, which can be easily provided not only in a laboratory environment, but also in a field operation, if an array is used as a directional antenna of the digital communication system equipment. In the second case, the calibration can be also conducted even in the presence of the interference signal sources. Simulation validates the proposed calibration algorithm, using linear antenna arrays with 4, 8 and 16 antennas with a half wavelength distance between the neighbor antennas. In this simulation, the array channel noise has been varied in 0 ... 30 dB range of the Signal-to-Noise Ratio. Two interference sources with the -30 dB Signal-to-Interference Ratio each have been simulated. These sources were located symmetrically relatively the required main lobe direction of the array radiation pattern. A training signal has been simulated as a random one with no specific autocorrelation properties. The signal has been modulated by the Phase Shift Keying (PSK) and the Quadrature Amplitude Modulation.

关键词： Calibration Interference filtering algorithms Signal processing algorithms Matrix decomposition Correlation Array signal processing

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Least Squares Criterion Adaptive filtering algorithms as Tools for Calibration of Arrays with Digital Beamforming

Least Squares Criterion Adaptive Filtering Algorithms as Too...

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2020 IEEE Ukrainian Microwave Week (UkrMW)

作者： Victor Djigan Vladislav Kurganov Institute for Design Problems in Microelectronics of RAS Moscow Russia

ISBN: (数字)9781728173139

ISBN: (纸本)9781728173146

This paper explores the application of the adaptive filtering algorithms, based on Least Squares (LS) criterion for the calibration of the receiving arrays with digital beamforming. The digital beamforming assumes the access to the signals in the array channels as a requirement, which has to be provided if the adaptive signal processing is used. In this case, the adaptive filtering algorithms, based on the minimization of the LS error between the desired and array output signals, are able to estimate the channel parameter spread, which values can be stored and used for correction of the array weights during the beam steering. If a set of steered beam positions is known, for example in scanning arrays, the proposed algorithm allows to create a codebook of the array weights, which ensures the random variation of channel's parameters compensation and correct beam steering. The architecture of the proposed solution and mathematical description of a Recursive Least Squares (RLS) adaptive filtering algorithm, on which the calibration is based on, are presented. The algorithm uses a dynamic regularization of the array input signal correlation matrix. No specific correlation properties are required for the algorithm training signal. The simulation results demonstrate the ability of the calibration algorithm to compensate the channel's parameters spread. If the array channel Signal-to-Noise Ratio (SNR) is 0 ... 30 dB, then the achieved spread of the calibrated channels gain errors is about -20 dB. The norm of the ideal and calibrated array difference is also about -20 dB. The results are obtained with the Phase Shift Keying (PSK) and Quadrature Amplitude Modulated (QAM) training signals. The ability of the proposed calibration algorithm to operate in interference conditions is also demonstrated.

关键词： Training Array signal processing Heuristic algorithms Signal processing algorithms Adaptive arrays filtering algorithms Calibration

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Linear-time filtering algorithms for the disjunctive constraint and a quadratic filtering algorithm for the cumulative not-first not-last

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CONSTRAINTS 2018年第3期23卷 272-293页

作者： Fahimi, Hamed Ouellet, Yanick Quimper, Claude-Guy Univ Laval Quebec City PQ Canada

We present new filtering algorithms for Disjunctive and Cumulative constraints, each of which improves the complexity of the state-of-the-art algorithms by a factor of log n. We show how to perform Time-Tabling and Detectable Precedences in linear time on the Disjunctive constraint. Furthermore, we present a linear-time Overload Checking for the Disjunctive and Cumulative constraints. Finally, we show how the rule of Not-first/Not-last can be enforced in quadratic time for the Cumulative constraint. These algorithms rely on the union find data structure, from which we take advantage to introduce a new data structure that we call it time line. This data structure provides constant time operations that were previously implemented in logarithmic time by the Iy-tree data structure. Experiments show that these new algorithms are competitive even for a small number of tasks and outperform existing algorithms as the number of tasks increases. We also show that the time line can be used to solve specific scheduling problems.

关键词： Scheduling Global constraint filtering algorithms Disjunctive Cumulative Data structure

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Systematic Evaluation of Protein Sequence filtering algorithms for Proteoform Identification Using Top-Down Mass Spectrometry

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PROTEOMICS 2018年第3-4期18卷

作者： Kou, Qiang Wu, Si Liu, Xiaowen Indiana Univ Purdue Univ Dept BioHlth Informat Indianapolis IN 46202 USA Univ Oklahoma Dept Chem & Biochem Norman OK 73019 USA Indiana Univ Sch Med Ctr Computat Biol & Bioinformat Indianapolis IN 46202 USA

Complex proteoforms contain various primary structural alterations resulting from variations in genes, RNA, and proteins. Top-down mass spectrometry is commonly used for analyzing complex proteoforms because it provides whole sequence information of the proteoforms. Proteoform identification by top-down mass spectral database search is a challenging computational problem because the types and/or locations of some alterations in target proteoforms are in general unknown. Although spectral alignment and mass graph alignment algorithms have been proposed for identifying proteoforms with unknown alterations, they are extremely slow to align millions of spectra against tens of thousands of protein sequences in high throughput proteome level analyses. Many software tools in this area combine efficient protein sequence filtering algorithms and spectral alignment algorithms to speed up database search. As a result, the performance of these tools heavily relies on the sensitivity and efficiency of their filtering algorithms. Here, we propose two efficient approximate spectrum-based filtering algorithms for proteoform identification. We evaluated the performances of the proposed algorithms and four existing ones on simulated and real top-down mass spectrometry data sets. Experiments showed that the proposed algorithms outperformed the existing ones for complex proteoform identification. In addition, combining the proposed filtering algorithms and mass graph alignment algorithms identified many proteoforms missed by ProSightPC in proteome-level proteoform analyses.

关键词： filtering algorithms spectral identification top-down mass spectrometry

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A Survey of Collaborative filtering algorithms for Social Recommender Systems

A Survey of Collaborative Filtering Algorithms for Social Re...

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International Conference on Semantics, Knowledge and Grid (SKG)

作者： Yingtong Dou Hao Yang Xiaolong Deng International School Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781509047963

This paper introduces the status of social recommender system research in general and collaborative filtering in particular. For the collaborative filtering, the paper shows the basic principles and formulas of two basic approaches, the user-based collaborative filtering and the item-based collaborative filtering. For the user or item similarity calculation, the paper compares the differences between the cosine-based similarity, the revised cosine-based similarity and the Pearson-based similarity. The paper also analyzes the three main challenges of the collaborative filtering algorithm and shows the related works facing the challenges. To solve the Cold Start problem and reduce the cost of best neighborhood calculation, the paper provides several solutions. At last it discusses the future of the collaborative filtering algorithm in social recommender system.

关键词： Collaboration Recommender systems Prediction algorithms Social network services filtering algorithms Mathematical model

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Efficient filtering algorithms for Location-Aware Publish/Subscribe

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2015年第4期27卷 950-963页

作者： Yu, Minghe Li, Guoliang Wang, Ting Feng, Jianhua Gong, Zhiguo Tsinghua Univ Tsinghua Natl Lab Informat Sci & Technol TNList Dept Comp Sci Beijing 100084 Peoples R China Univ Macau Dept Comp & Informat Sci Macau 3974465 Peoples R China

Location-based services have been widely adopted in many systems. Existing works employ a pull model or user-initiated model, where a user issues a query to a server which replies with location-aware answers. To provide users with instant replies, a push model or server-initiated model is becoming an inevitable computing model in the next-generation location-based services. In the push model, subscribers register spatio-textual subscriptions to capture their interests, and publishers post spatio-textual messages. This calls for a high-performance location-aware publish/subscribe system to deliver publishers' messages to relevant subscribers. In this paper, we address the research challenges that arise in designing a location-aware publish/subscribe system. We propose an R-tree based index by integrating textual descriptions into R-tree nodes. We devise efficient filtering algorithms and effective pruning techniques to achieve high performance. Our method can support both conjunctive queries and ranking queries. We discuss how to support dynamic updates efficiently. Experimental results show our method achieves high performance which can filter 500 messages in a second for 10 million subscriptions on a commodity computer

关键词： Location based services Messages Location awareness HIGH-PERFORMANCE filtering algorithms instant Servers tree nodes Publishers Subscriptions

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The block-sparse proportionate second-order volterra filtering algorithms for nonlinear echo cancellation

The block-sparse proportionate second-order volterra filteri...

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International Workshop on Acoustic Signal Enhancement (IWAENC)

作者： Jianming Liu Quintin Liu Steven L. Grant Yahong Rosa Zheng ForteMedia Inc. CA USA Missouri University of Science and Technology MO USA

Recently, a family of block-sparse proportionate adaptive filtering has been introduced for the block-sparse system identification. Here, we focus on the family of block-sparse proportionate second-order Volterra filtering algorithm for nonlinear echo cancellation. It is demonstrated that a multiple block-sparse system is naturally involved in the sparse adaptive Volterra filter, which could be accelerated by applying the block-sparse proportionate idea. Simulation results confirmed the performance improvement compared to both the previous proportionate second-order Volterra and the classical second-order Volterra filters.

关键词： Kernel Adaptive filters filtering algorithms Echo cancellers System identification Convergence Finite impulse response filters

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Weighted Average Consensus filtering for Continuous-Time Linear Systems With Asynchronous Sensor Measurements

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IEEE SENSORS JOURNAL 2025年第4期25卷 6881-6893页

作者： Hu, Yanyan Lin, Xufeng Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Univ Sci & Technol Beijing Key Lab Intelligent Bion Unmanned Syst Minist Educ Beijing 100083 Peoples R China Univ Sci & Technol Beijing Sch Intelligence Sci & Technol Beijing 100083 Peoples R China

In practical sensor networks, especially heterogeneous sensor networks, sensor nodes may have distinct sampling periods and initial sampling time instants, probably their observations are also nonuniform. However, the distributed consensus state estimation problem for continuous-time linear systems in such asynchronous sensor networks has not been addressed in the literature. To solve this problem, this article proposes a consensus filtering algorithm for distributed state estimation over asynchronous sensor networks based on the weighted average consensus strategy. First, asynchronous measurements at each sensor node within a given filtering interval are transformed to the consensus filtering time instant according to the continuous-time system dynamics. Statistical characteristics of converted measurement noises are carefully exploited as well as their cross-correlations induced by the synchronization procedure. It is also discovered that the converted measurement noises are one-step correlated with the discretized process noise. Second, measurements after synchronization are used to update local estimates at sensor nodes with the above correlations taken into account and weighted average consensus iterations are performed based on information interactions of sensor nodes with their neighbors. Finally, the estimation error of the proposed asynchronous consensus filtering algorithm is proved to be exponential mean-square bounded, and its effectiveness is evaluated by a simulation example.

关键词： Sensors Synchronization filtering State estimation Sensor systems Noise measurement Noise Time measurement filtering algorithms Sensor phenomena and characterization Asynchronous sensor networks consensus filtering measurement synchronization weighted average consensus

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