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Multipath mitigation in GPS receiver using Taylor integrated bidirectional least mean square algorithm

TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES 2019年第12期30卷 n/a-n/a页

作者： Siridhara, A. L. Ratnam, D., V Koneru Lakshmaiah Educ Fdn Dept Elect & Commun Engn Guntur Andhra Pradesh India

Research interest in Global Navigation Satellite System (GNSS) is increasing in the past few years due to its wide applicability in many application-oriented environments, such as automobile navigation system and aircraft landing system. Global Positioning System (GPS) is one of the GNSSs having high precision navigation performance. Multipath is a dominant error source that occurs in several GPS applications, limiting the performance. To reduce the multipath effects of satellite signals, an effective multipath mitigation technique is required. This paper introduces the Taylor series-based bidirectional least mean square (Taylor-BLMS) beamforming model for the multipath mitigation problems occurring in the GPS receiver. The performance metrics, such as antenna gain and bit error rate (BER), measure the performance of the proposed Taylor-BLMS model against various comparative models. For the system with six sensors, the proposed Taylor-BLMS model has the best performance with the values of 23.460 dB and 4.78E-05 for the antenna gain and the BER, respectively. For the system with 1 jammer, the proposed Taylor-BLMS model has the antenna gain and the BER values of 22.2828 dB and 3.69E-05, respectively.

关键词： antenna gain performance values Taylor multipath effect Performance metrics Error Sources GPS Receiver Global Positioning Systems Bidirectional Global Navigation Satellite System least mean square algorithm

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Robust zero-point attraction least mean square algorithm on near sparse system identification

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IET SIGNAL PROCESSING 2013年第3期7卷 210-218页

作者： Jin, Jian Qu, Qing Gu, Yuantao Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

The newly proposed l(1) norm constraint zero-point attraction least mean square algorithm (ZA-LMS) demonstrates excellent performance on exact sparse system identification. However, ZA-LMS has less advantage against standard LMS when the system is near sparse. Thus, in this study, firstly the near sparse system (NSS) modelling by generalised Gaussian distribution is recommended, where the sparsity is defined accordingly. Second, two modifications to the ZA-LMS algorithm have been made. The l(1) norm penalty is replaced by a partial l(1) norm in the cost function, enhancing robustness without increasing the computational complexity. Moreover, the ZA item is weighted by the magnitude of estimation error which adjusts the ZA force dynamically. By combining the two improvements, Dynamic Windowing ZA-LMS (DWZA-LMS) algorithm is further proposed, which shows better performance on NSS identification. In addition, the mean-square performance of DWZA-LMS algorithm is analysed. Finally, computer simulations demonstrate the effectiveness of the proposed algorithm and verify the result of theoretical analysis.

关键词： Cost functions System identification NSS Gaussian distribution Attraction least mean square algorithm zero point

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Robust non-negative least mean square algorithm based on step-size scaler against impulsive noise

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ADVANCES IN DIFFERENCE EQUATIONS 2020年第1期2020卷 1-10页

作者： Fan, Kuangang Qiu, Haiyun Pei, Chunyang Chen, Yuhang Jiangxi Univ Sci & Technol Sch Elect Engn & Automat Ganzhou Peoples R China Jiangxi Univ Sci & Technol Inst Permanent Maglev & Railway Technol Ganzhou Peoples R China Jiangxi Univ Sci & Technol Sch Mech & Elect Engn Ganzhou Peoples R China

Conventional non-negative algorithms restrict the weight coefficient vector under non-negativity constraints to satisfy several inherent characteristics of a specific system. However, the presence of impulsive noise causes conventional non-negative algorithms to exhibit inferior performance. Under this background, a robust non-negative least mean square (R-NNLMS) algorithm based on a step-size scaler is proposed. The proposed algorithm uses a step-size scaler to avoid the influence of impulsive noise. For various outliers, the step-size scaler can adjust the step size of the algorithm, thereby eliminating the large error caused by impulsive noise. Furthermore, to improve the performance of the proposed algorithm in sparse system identification, the inversely-proportional R-NNLMS (IP-RNNLMS) algorithm is proposed. The simulation result demonstrates that the R-NNLMS algorithm can eliminate the influence of impulsive noise while showing fast convergence rate and low steady-state error under other noises. In addition, the IP-RNNLMS algorithm has faster convergence rate compared with the R-NNLMS algorithm under sparse system.

关键词： least mean square algorithm Impulsive noise Step-size scaler Non-negativity constraints

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Detection of Inter-Turn Short Circuit Fault of Active Magnetic Bearing Based on least mean square algorithm 24

Detection of Inter-Turn Short Circuit Fault of Active Magnet...

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24th International Conference on Electrical Machines and Systems (ICEMS)

作者： Li, Henghui Wu, Lijian Su, Zhenzhong Chen, Junquan Lyu, Zekai Niu, Feng Zhejiang Univ Coll Elect Engn Hangzhou Peoples R China Naval Univ Engn Natl Key Lab Sci & Technol Vessel Integrated Powe Wuhan Peoples R China Hebei Univ Technol State Key Lab Reliabil & Intelligence Elect Equip Tianjin Peoples R China

ISBN: (纸本)9788986510218

The inter-turn short circuit (ITSC) fault of the winding in the active magnetic bearing (AMB) may cause severe damage. Hence, it is vital to diagnose the ITSC fault in real-time. In this paper, the ITSC fault model of the AMB is presented and a method is proposed for the detection of the ITSC fault of AMB utilizing the least mean square (LMS) algorithm. Furthermore, the forgetting factor recursive least square algorithm is adopted for the online implementation of the LMS algorithm. A compensation method based on look-up table is introduced to improve the robustness of the proposed method to the eccentricity of the rotor. The effectiveness of proposed method is verified by experiments.

关键词： Active magnetic bearing Fault diagnosis Inter-turn short circuit least mean square algorithm

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A New Approach for Efficient SISO Equalizer with Modified least mean square algorithm

A New Approach for Efficient SISO Equalizer with Modified Le...

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International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS)

作者： Sharma, Neha Mishra, Samir Rama Univ Elect & Commun Engn Dept Kanpur Uttar Pradesh India Indian Inst Technol Elect & Elect Commun Dept Kharagpur W Bengal India

ISBN: (纸本)9781509032945

In the presented paper, VLSI Architecture for linear turbo equalization are studied for constituent soft-input soft output equalizer blocks. Linear Turbo Equalizer is the joint form of equalization and decoding in which soft information is iteratively exchanged between the soft-input soft-output equalizer and soft-input soft-output decoder. Linear turbo equalizer exhibits better performance in comparison to Decision Feedback Equalizer in terms of Bit Error Rate (BER) for Proakis channels. In this paper, modified least mean square adaptive algorithm is selected for implementation of soft-input soft-output equalizer as hardware intricacy required to design equalizer is small in comparison to other equalizer. The modified least mean square adaptive algorithm separate reckons of error-computation block and weight-update block. It also allows performance of optimal pipelining by using feed forward filter retiming of both these sections distinctly to minimize the number of pipeline stage and adaptation delay.

关键词： least mean square algorithm Linear Turbo Equalizer Soft information Symbol to Bit mapper SISO equalizer VLSI Architecture

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Diffusion least mean square algorithm for Identification of IIR System Present in Each Node of a Wireless Sensor Networks 4th

Diffusion Least Mean Square Algorithm for Identification of ...

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4th International Conference on Computational Intelligence in Data Mining (ICCIDM)

作者： Dimple, Km Kotary, Dinesh Kumar Nanda, Satyasai Jagannath Malaviya Natl Inst Technol Dept Elect & Commun Engn Jaipur 302017 Rajasthan India

ISBN: (纸本)9789811080555;9789811080548

Most of the real-world practical systems are inherently dynamic and their characteristics are represented by transfer functions which are IIR in nature. In literature-distributed estimation, algorithms have been developed for stable FIR system. In this paper, a distributed estimation technique is developed for identification of IIR system present at each node of a wireless sensor network. The distributed parameter estimation generally based on two modes of cooperation strategies: Incremental and Diffusion. In case of change in network topology, the diffusion mode of cooperation works well and shows robustness to link and node failure. Thus, an infinite impulse response diffusion least mean square (IIR DLMS) algorithm is introduced. In simulation, its performance is compared with the incremental version (infinite impulse response incremental least mean square algorithm (IIR ILMS)). Superior performance by the proposed approach is reported for parameter estimation of two IIR systems under various noisy environments.

关键词： least mean square algorithm Infinite impulse response system mean square error Parameter estimation

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A q-Noise Constrained least mean square algorithm 54

A q-Noise Constrained Least Mean Square Algorithm

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54th Asilomar Conference on Signals, Systems, and Computers

作者： Bin Saeed, Muhammad O. Zerguine, Azzedine Air Univ Dept Elect & Comp Engn Islamabad Pakistan King Fahd Univ Petr & Minerals Elect Engn Dept Dhahran 31261 Saudi Arabia

ISBN: (纸本)9780738131269

The least mean square (LMS) algorithm has an inherent trade-off issue between convergence speed and steady-state error performance. One of the algorithms proposed to tackle this issue is called the Noise Constrained LMS algorithm, which uses the noise variance to iteratively vary the stepsize. This work uses the q-derivative to propose an improved Noise Constrained LMS algorithm. Simulation results show that the proposed algorithm shows better performance than the conventional algorithm at the cost of only a minimal increase in complexity. Steady-state analysis for the proposed algorithm has also been carried out.

关键词： qq-Derivative Noise constrained algorithm least mean square algorithm

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Comparative Study of Active Power Filters with least mean square algorithm and conventional strategy in discrete-time domain

Comparative Study of Active Power Filters with Least Mean Sq...

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4th International Conference on Aerospace and Signal Processing

作者： Delgado, I Ponce, G. R., V Machaca, Susan Mendoza, Ester Calcina, Eyner Natl Univ Altiplano Profess Sch Elect Engn Puno Peru

ISBN: (纸本)9798331534240;9798331534233

This paper compares the performance of an active power filter implemented using the least mean square (LMS) algorithm, with that of the traditional filter. An active power filter is designed to improve power quality in an electrical system. This entails reducing harmonics and reactive power, as well as eliminating unbalance. Since the LMS algorithm is among the fastest and simplest filters, which tends to make it easy to implement, the performance is evaluated using the Fast Fourier Transfer. Performance indicators for this application considered are harmonic removal and the stability period. The simulations are implemented in Matlab software, and the results allow a comparison of the LMS-based APF and reflect when the method is better and when it is necessary to have to stick to the traditional one. The LMS algorithm demonstrates results due to its ability to adapt to changes in real-time operation even under varying load operation frequency. This paper presents the mathematical model of two strategies of power compensation in continuous and discrete-time domain.

关键词： Active Power Filter least mean square algorithm Power quality improvement Harmonic reduction Reactive power compensation

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Multiple parallel branch with folding architecture for multichannel filtered-x least mean square algorithm

Multiple parallel branch with folding architecture for multi...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Dongyuan Shi Jianjun He Chuang Shi Tatsuya Murao Woon-Seng Gan Digital Signal Processing Lab School of Electrical & Electronic Engineering Nanyang Technological University Singapore

ISBN: (纸本)9781509041183

Multichannel active noise control (MCANC) systems are commonly used in acoustic noise or vibration control, such as large-dimension ventilation ducts, open windows and mechanical structures. However, its computational load far exceeds the capabilities of digital signal processors (DSPs) and microcontrollers. Even the field programmable gate array (FPGA) cannot straightforwardly cope with the exponential increase in the computation load of MCANC systems. A novel architecture, called the multiple parallel branch with folding, is proposed for the J × J × M (J reference microphones, J secondary sources and Merror microphones) MCANC implementation with the floating-point arithmetic. This architecture addresses the tradeoff between throughput and hardware resource consumption by using parallel execution and folding. The proposed architecture is validated in an experimental setup that carries out a 4 × 4 × 4 multichannel filtered-x least mean square (FxLMS) algorithm achieving the sampling rate and throughput of 24 KHz and 18.4 Mbps, respectively.

关键词： Filtered-x least mean square algorithm multichannel active noise control field programmable gate array Field programmable gate arrays Active noise control least mean square algorithm FOLDING Throughput Building construction ARCHITECTURE iodine MULTICHANNEL parallel execution Acoustic noise Microphone Device Component sampling rate

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A new proportionate normalized least mean square algorithm for high measurement noise

A new proportionate normalized least mean square algorithm f...

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IEEE International Conference on Signal Processing, Communications and Computing

作者： Yinxia Dong Haiquan Zhao School of Electrical Engineering Southwest Jiaotong University

ISBN: (纸本)9781479989218

In this paper, we derive a new improved proportionate normalized least mean square (IPNLMS) algorithm with unconventional minimization criterion that minimizes the summation of each squared Euclidean norm of difference between the currently updated coefficient vector and past coefficient vectors, which is called the improved IPNLMS (I-IPNLMS) algorithm. Simulation results demonstrate that the proposed I-IPNLMS algorithm has the superiority of the lower misalignment than the conventional IPNLMS algorithm in the context of sparse system identification with a low signal-noise-ratio (SNR).

关键词： Adaptive filtering Improved proportionate normalized least mean square algorithm Sparse system High measurement noise least mean square algorithm Noise measurement adaptive filters System identification Euclidean norm Summation algorithm simulation

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