Number Of Estimation Algorithms Research Articles

The research on the signal source number estimation algorithm

The research on the signal source number estimation algorithm

The optimal partition number estimation algorithm based on variational inference

The optimal partition number estimation algorithm based on variational inference

Optimal Value for Number of Clusters in a Dataset for Clustering Algorithm

It is essential to know the parameters required to clustering the dataset. One of the parameters is the number of clusters k and it is very important to select the k value to get deficient results on clustering. There are few algorithms to find the k value for k-means algorithm and it requires specifying a maximum value for k or a range of values for k as an input. This paper proposes a novel method Optimal cluster number estimation algorithm (OCNE) to find the optimal number of clusters without specifying the maximum or range of k values or knee point detection in the graph. In the experiment, this method is compared with the different existing methods with deficient real-world as well as synthetic datasets and provides good performance.

Enhancing web search result clustering model based on multiview multirepresentation consensus cluster ensemble (mmcc) approach.

Existing text clustering methods utilize only one representation at a time (single view), whereas multiple views can represent documents. The multiview multirepresentation method enhances clustering quality. Moreover, existing clustering methods that utilize more than one representation at a time (multiview) use representation with the same nature. Hence, using multiple views that represent data in a different representation with clustering methods is reasonable to create a diverse set of candidate clustering solutions. On this basis, an effective dynamic clustering method must consider combining multiple views of data including semantic view, lexical view (word weighting), and topic view as well as the number of clusters. The main goal of this study is to develop a new method that can improve the performance of web search result clustering (WSRC). An enhanced multiview multirepresentation consensus clustering ensemble (MMCC) method is proposed to create a set of diverse candidate solutions and select a high-quality overlapping cluster. The overlapping clusters are obtained from the candidate solutions created by different clustering methods. The framework to develop the proposed MMCC includes numerous stages: (1) acquiring the standard datasets (MORESQUE and Open Directory Project-239), which are used to validate search result clustering algorithms, (2) preprocessing the dataset, (3) applying multiview multirepresentation clustering models, (4) using the radius-based cluster number estimation algorithm, and (5) employing the consensus clustering ensemble method. Results show an improvement in clustering methods when multiview multirepresentation is used. More importantly, the proposed MMCC model improves the overall performance of WSRC compared with all single-view clustering models.

A Source Number Estimation Algorithm Based on Data Local Density and Fuzzy C‐Means Clustering

An advanced source number estimation (SNE) algorithm based on both fuzzy C‐means clustering (FCM) and data local density (DLD) is proposed in this paper. The DLD of an eigenvalue refers to the number of eigenvalues within a specific neighborhood of this eigenvalue belonging to the data covariance matrix. This local density essentially as the one‐dimensional sample feature of the FCM is extracted into the SNE algorithm based on FCM and can enable to improve the probability of correct detection (PCD) of the SNE algorithm based on the FCM especially for low signal‐to‐noise ratio (SNR) environment. Comparison experiment results demonstrate that compared to the SNE algorithm based on the FCM and other similar algorithms, our proposed algorithm can achieve highest PCD of the incident source number in both cases of spatial white noise and spatial correlation noise.

Multipath number estimation based on QR decomposition

Accurate multipath number estimation is the prerequisite for MUSIC algorithm to achieve excellent superresolution performance. To solve the problem that the existing multipath number estimation algorithms based on eigenvalue distribution usually require a large amount of computation, this paper proposed a fast multipath number estimation algorithm based on QR decomposition under the OFDM signal model. This algorithm only needs few times QR iteration decomposition to obtain the pseudo-eigenvalues of the matrix, and uses them to estimate the multipath number through two-step comparison. Due to the avoidance of accurate eigenvalues solution, compared with the traditional algorithms based on eigenvalue distribution, proposed algorithm’s computation is smaller. Simulation results showed that the proposed algorithm has better detection performance in low SNR and few snapshots than existing algorithms.

Joint TOA and DOA Estimation With CFO Compensation Using Large-Scale Array

This paper investigates estimation of direction of arrivals (DOAs) and time of arrivals (TOAs) for a multi-user orthogonal frequency-division multiplexing (OFDM) system that is equipped with large-scale arrays and interfered by carrier frequency offsets (CFOs). We propose a novel scheme for joint TOA and DOA estimation with CFO compensation by taking advantages of large-scale receive antenna arrays. The pilot is designed to balance the pilot length and the estimation performance. The proposed CFO estimation scheme is with lower complexity. After analyzing the resolution of joint DOA and TOA estimation, we find a criterion to choose an appropriate smoothing window for the best resolution and provide insights in sub-carrier selection strategy. We also prove that the large-scale array can asymptotically achieve perfect resolution of both TOA and DOA estimation. We reveal the pseudo peak (PP) elimination property of large-scale arrays and exploit it to develop a novel path number estimation algorithm accordingly. Based on our numerical results, the proposed scheme can efficiently eliminate the CFO effect, the optimized smoothing window attains much better performance, and the path number estimation algorithm performs better than the traditional minimum description length (MDL) approach, especially in the low signal-to-noise ratio (SNR) regime.

Parameter Estimation of Polynomial-Phase Signals

Introduction . Polynomial phase signals frequently appear in radar, sonar, communication and technical applications. Therefore, estimation of polynomial phase coefficients of such signals is an urgent problem in signal theory. Currently, a large number of estimation algorithms have been proposed. The best way is the maximum likelihood (ML) method. However, its implementation is associated with a multidimensional retrieval, which makes the method unsuitable for practical implementation. A number of alternative strategies have been developed to circumvent the ML difficulties. These strategies are very close to optimal. Among them one can single out the HAF-algorithm based on the computation of the High order Ambiguity Function and the CPF algorithm, which uses the computation of the Cubic Phase Function and produces very accurate estimates for signals with the quadratic frequency modulation. However, both algorithms have obvious drawbacks. The HAF algorithm pro-duces a large number of combinatorial noise components. The CPF algorithm is limited in its implementation to the third order polynomial signals and does not use fast algorithms, such as the Fast Fourier Transform. Aim. Synthesis of an estimation algorithm that produces a small number of noise combinatorial components and uses the Fast Fourier Transform computation algorithms to find coefficient estimates of an arbitrary order phase polynomial. Materials and methods. In the paper a concept of a decisive function was introduced. It was calculated so that its phase contained only a first-order monomial with a coefficient equal to the highest coefficient of the signal phase polynomial. Results. A new estimation algorithm was proposed able to use Fast Fourier Transform computation algorithms to find estimates. Each polynomial coefficient was estimated on the basis of a unified procedure, which reduced the number of combinatorial noise components in an estimate search. Conclusions. The synthesized algorithm gives asymptotically efficient estimates for lower signal-to-noise ratios in comparison with the HAF-algorithm.

High-Resolution Angle-of-Arrival and Channel Estimation for mmWave Massive MIMO Systems With Lens Antenna Array

Lens antenna array (LAA), as a promising architecture in millimeter-wave (mmWave) massive MIMO systems, which can considerably reduce the number of required radio-frequency (RF) chains. In this article, we propose a high-resolution channel or angle-of-arrival (AoA) estimation scheme while taking into account the power leakage problem. Specifically, the criterion function is constructed based on DFT beam difference (DBD) to infer the real position of arrived signal. Subsequently, the novel antenna selection and path number estimation algorithms are proposed without prior knowledge of channel. A few groups of antennas are selected to connect to the RF chains, where each group contains only a small number of antennas. Thus, the beamspace multiple signal classification (MUSIC) algorithm can be implemented in low dimensional space to estimate the AoA(s) for each group. Then the channel estimation can be obtained by least square method after the reconstructions of steering vectors using the AoA estimations. Furthermore, the corresponding spatial smoothing algorithm is leveraged to tackle rank deficient problem at the scenario of constant path gains. Simulation results verify the effectiveness of the proposed scheme.

On Number of Tags Estimation in RFID Systems

According to the latest version of the radio-frequency identification (RFID) standard, EPCglobal UHF Gen2, dynamic framed slotted ALOHA has been accepted and employed as the de facto collision resolution algorithm to share the channel usage when multiple tags respond to the reader's signal command simultaneously. However, the frame size in read cycle is usually far from optimal when the reader does not know the number of unidentified tags in its interrogation zone, and this will lead to several performance issues and technical limitations such as power consumption, longer reading time, and degraded system performance. In this paper, we show that the number of unidentified tags can be expressed as a function of the collision rate received by the reader when the reader collects tags' information, and then we use an extended Kalman filter-based tag number estimation algorithm to estimate the number of unidentified tags in an RFID system on run-time measurements. Simulation results show that our scheme provides much better accuracy than existing well-known approaches even in a fast-changing environment.

Imaging and Parameter Estimating for Fast Moving Targets in Airborne SAR

A novel algorithm is presented to image fast moving targets and estimate moving parameters in single-antenna synthetic aperture radar, in which phase information is extracted by a proposed maximum amplitude extraction algorithm, and the Doppler parameters of moving targets are estimated by combining the fractional Fourier transform and an adaptive and iterative ambiguous number estimation algorithm. The range curve and walk can be corrected simultaneously with the estimated Doppler parameters. After azimuth compression, the shortest distance is estimated in a focused image to obtain the accurate moving parameters. Our simulations demonstrate that high-precision estimation and robust performance can be achieved when a large range curve and walk caused by fast moving targets exist, even in low-signal-to-noise-ratio cases. Comparisons with state-of-the-art algorithms also illustrate the advantages of the proposed algorithm.

Fault-Decoupled Instantaneous Frequency and Phase Angle Estimation for Three-Phase Grid-Connected Inverters

Frequency and phase angle estimation is a key aspect for grid-connected inverters that are required to guarantee low-voltage fault-ride-through capability. Over the past two decades, a number of estimation algorithms have been proposed, mostly based on the well-known phase-locked loop (PLL). It has been demonstrated that standard PLLs do not perform correctly in abnormal grid conditions, due to the oscillations produced in the frequency and phase angle estimates by the voltage harmonics. This paper introduces a new, general approach to harmonic decoupling and presents a highly intuitive and simple scheme, applying it to an αβ-PLL; compensation of any desired number of harmonic components is possible. Two implementations of this decoupling scheme are presented. It is shown that the performances of the resulting fault-decoupled PLLs are comparable with those of other advanced frequency and phase angle estimation structures.

Design of Digital Channelizer Based on Source Number Estimation

Digital channelizer is used to separate the sub-band signals contained within a wideband intermediate frequency (IF) received signal. This paper presents a structure of digital channelizer which is built upon estimation of the sub-band signal number. The proposed structure is designed by employing nonuniform filter banks (NUFBs), which are implemented by merging the adjacent sub-branches of the perfect reconstruction (PR) cosine modulated filter banks (CMFBs). Using source number estimation algorithm, the estimated value of sub-band number can be obtained. By energy detection, the energy characteristic of the analysis filter banks (AFBs) can be acquired and another value of sub-band number is calculated. Comparing the estimated value and the calculated one, we can evaluate whether the design of the AFB is suitable for the received signal. The structure of digital channelizer proposed by this paper provides a potential way to channelize the unknown signal and suggests a solution for the dynamic channelizaiton. A design example is also presented to demonstrate the feasibility and simplicity of this method.

Comparison Study on Two Model-Based Adaptive Algorithms for SOC Estimation of Lithium-Ion Batteries in Electric Vehicles

State of charge (SOC) estimation is essential to battery management systems in electric vehicles (EVs) to ensure the safe operations of batteries and providing drivers with the remaining range of the EVs. A number of estimation algorithms have been developed to get an accurate SOC value because the SOC cannot be directly measured with sensors and is closely related to various factors, such as ambient temperature, current rate and battery aging. In this paper, two model-based adaptive algorithms, including the adaptive unscented Kalman filter (AUKF) and adaptive slide mode observer (ASMO) are applied and compared in terms of convergence behavior, tracking accuracy, computational cost and estimation robustness against parameter uncertainties of the battery model in SOC estimation. Two typical driving cycles, including the Dynamic Stress Test (DST) and New European Driving Cycle (NEDC) are applied to evaluate the performance of the two algorithms. Comparison results show that the AUKF has merits in convergence ability and tracking accuracy with an accurate battery model, while the ASMO has lower computational cost and better estimation robustness against parameter uncertainties of the battery model.

Estimating the condition number of f(A)b

New algorithms are developed for estimating the condition number of f(A)b, where A is a matrix and b is a vector. The condition number estimation algorithms for f(A) already available in the literature require the explicit computation of matrix functions and their Fr´echet derivatives and are therefore unsuitable for the large, sparse A typically encountered in f(A)b problems. The algorithms we propose here use only matrix-vector multiplications. They are based on a modified version of the power iteration for estimating the norm of the Fr´echet derivative of a matrix function, and work in conjunction with any existing algorithm for computing f(A)b. The number of matrix-vector multiplications required to estimate the condition number is proportional to the square of the number of matrix-vector multiplications required by the underlying f(A)b algorithm. We develop a specific version of our algorithm for estimating the condition number of e A b, based on the algorithm of Al-Mohy and Higham (SIAM J. Matrix Anal. Appl. 30(4), 1639–1657, 2009). Numerical experiments demonstrate that our condition estimates are reliable and of reasonable cost.

Forward Predicting and Backward Verifying Tags Number Estimation in RFID System

Tag collision happens when multiple tags are energized simultaneously, reflect their respective signals back to the reader at the same time, and the reader is unable to differentiate these signals. This problem causes decrease of the RFID efficiency especially in the tag-intensive RFID system. The dynamic frame slotted aloha (DFSA) algorithm is the most appropriate solution for this problem, which is based on an accurate tags number estimate method. A novel forward predicting and backward verifying adaptive precise tags number estimation algorithm is proposed in this paper. Experiment results show that it is more effective than others.

An approach to the modulation recognition of MIMO radar signals

Multiple-input multiple-output (MIMO) radar is a new radar system and draws more and more attentions in recent years. Along with the development of MIMO radar, the MIMO radar countermeasure is brought into being. Since the modulation recognition is of great significance in the electronic reconnaissance, this article presents a modulation recognition method for the signals of the emerging MIMO radar. Signals of interest are classified into three categories based on instantaneous autocorrelation spectrum analysis first. Then non-coding MIMO radar signals are discriminated by spectrum analysis, and coding MIMO radar signals are recognized by source number estimation algorithm. Meanwhile, the sub-carrier numbers of some kinds of MIMO radar signals are estimated. Simulation results verify the effectiveness of the method and the overall correct recognition rate is over 90% when the value of SNR is above 0 dB.

Source number estimation using dual eigenspace projection

A new tensorial model is herein established for a special multidimensional array of vector sensors composed of a pressure part and orthogonal velocity parts, under which a multi-linear algebra based source number estimation algorithm is proposed. The estimation projects the covariance tensor into the signal subspace. Owing to the orthogonality between the newly defined tensor signal subspace and tensor noise subspace, it is easy to differentiate the contribution of the signal and noise by using the decision value in a compact tensorial fashion, which is the magnitude of projections from twofold mode signal subspaces. To reduce computer burden, meanwhile, the real-valued preprocessing method is applied to the covariance tensor for simplicity. Simulation and experiment results exhibit the superiority of the proposed algorithm over the conventional ones based on the matrix method.

A New Method for Adaptive Blind Source Separation Based on the Estimated Number of Dynamic Fault Sources

In fault diagnosis of large rotating machinery, the number of fault sources may be subject to dynamic changes, which often lead to the failure in accurate estimation of the number of sources and the effective isolation of the fault source. This paper introduced the expansion of the fourth-order cumulant matrices in estimating the dynamic fault source number, plus the relationship between the source signal number and the number of sensors being utilized in the selection of the blind source separation algorithm to achieve adaptive blind source separation. Experiments showed that the source number estimation algorithm could be quite effective in estimating the dynamic number of fault sources, even in the underdetermined condition. This adaptive blind source separation algorithm could then effectively achieve fault diagnosis in respect to the positive-determined, overdetermined and underdetermined blind source separation.

Robust state estimation and its application to spacecraft control

In this paper the estimation and attitude control problem of a rigid body spacecraft system with loss of observation is addressed. While a number of estimation algorithms are widely utilised in real-time applications, most of them are inadequate in the event of loss of observation as they are fundamentally based on the plant dynamics relying heavily on the measured output data. To overcome this shortcoming, a compensated closed-loop estimation algorithm is suggested in this work and is implemented in a spacecraft system with intermittent measured signals. The compensated observation signal, reconstructed using a linear prediction subsystem, is supplied at the measurement update step in the Kalman filtering. To limit the number of observations utilised in the linear prediction filter, a minimum mean square error based scheme is provided to obtain the size of linear prediction filter order. A Lyapunov-stability based output feedback control scheme is employed for the stabilisation problem. The simulation results demonstrate the effectiveness of the compensated algorithm wherein the aim is tackling the estimation problem subject to loss of measurements for the spacecraft application.