Improve Estimation Performance Research Articles

Improvement in Channel Estimation and Error Rate Performance in Mobile Cooperative OFDM Systems with Intercarrier Interference

In mobile cooperative orthogonal frequency division multiplexing (OFDM) systems with amplify-and-forward (AF) relays, in which the source and relay terminals are moving, Doppler frequency might become very large due to the formation of double-hop channels, possibly causing very large intercarrier interference (ICI). Therefore, ICI mitigation technique capable of reducing the impact of high Doppler frequency is required. This paper reports the study of ICI mitigation technique that uses frequency domain equalizer (FDE) with improvements in three aspects, namely, better channel estimation using cubic-spline approximation, use of selection combining to utilize cooperative diversity in order to suppress ICI, and simple error correcting code with double interleaving to suppress errors due to residual ICI. By using the proposed ICI mitigation technique, error rate performance of mobile cooperative OFDM systems can be improved significantly. Even at high Doppler frequencies, error floors due to residual ICI can be decreased by up to two decades.

ML-Based Blind Frequency Offset Estimation Schemes for OFDM Systems in Non-Gaussian Noise Environments

This paper proposes frequency offset (FO) estimation schemes robust to the non-Gaussian noise for orthogonal frequency division multiplexing (OFDM) systems. A maximum-likelihood (ML) scheme and a low-complexity estimation scheme are proposed by applying the probability density function of the cyclic prefix of OFDM symbols to the ML criterion. From simulation results, it is confirmed that the proposed schemes offer a significant FO estimation performance improvement over the conventional estimation scheme in non-Gaussian noise environments. Keywords—Frequency offset, cyclic prefix, maximum-likelihood, non-Gaussian noise, OFDM.

Sensor placement for the analysis of seismic surface waves: sources of error, design criterion and array design algorithms

Seismic surface waves can be measured by deploying an array of seismometers on the surface of the earth. The goal of such measurement surveys is, usually, to estimate the velocity of propagation and the direction of arrival of the seismic waves. In this paper, we address the issue of sensor placement for the analysis of seismic surface waves from ambient vibration wavefields. First, we explain in detail how the array geometry affects the mean-squared estimation error of parameters of interest, such as the velocity and direction of propagation, both at low and high signal-to-noise ratios (SNRs). Secondly, we propose a cost function suitable for the design of the array geometry with particular focus on the estimation of the wavenumber of both Love and Rayleigh waves. Thirdly, we present and compare several computational approaches to minimize the proposed cost function. Numerical experiments verify the effectiveness of our cost function and resulting array geometry designs, leading to greatly improved estimation performance in comparison to arbitrary array geometries, both at low and high SNR levels.

An event-triggered approach to state estimation with multiple point- and set-valued measurements

In this work, we consider state estimation based on the information from multiple sensors that provide their measurement updates according to separate event-triggering conditions. An optimal sensor fusion problem based on the hybrid measurement information (namely, point- and set-valued measurements) is formulated and explored. We show that under a commonly-accepted Gaussian assumption, the optimal estimator depends on the conditional mean and covariance of the measurement innovations, which applies to general event-triggering schemes. For the case that each channel of the sensors has its own event-triggering condition, closed-form representations are derived for the optimal estimate and the corresponding error covariance matrix, and it is proved that the exploration of the set-valued information provided by the event-triggering sets guarantees the improvement of estimation performance. The effectiveness of the proposed event-based estimator is demonstrated by extensive Monte Carlo simulation experiments for different categories of systems and comparative simulation with the classical Kalman filter.

DOA Estimation for Multi-Band Signal Sources Using Compressed Sensing Techniques with Khatri-Rao Processing

SUMMARY Much attention has recently been paid to direction of arrival (DOA) estimation using compressed sensing (CS) techniques, which are sparse signal reconstruction methods. In our previous study, we developed a method for estimating the DOAs of multi-band signals that uses CS processing and that is based on the assumption that incident signals have the same complex amplitudes in all the bands. That method has a higher probability of correct estimation than a single-band DOA estimation method using CS. In this paper, we propose novel DOA estimation methods for multi-band signals with frequency characteristics using the Khatri-Rao product. First, we formulate a method that can estimate DOAs of multiband signals whose phases alone have frequency dependence. Second, we extend the scheme in such a way that we can estimate DOAs of multi-band signals whose amplitudes and phases both depend on frequency. Finally, we evaluate the performance of the proposed methods through computer simulations and reveal the improvement in estimation performance.

Joint Direction-of-Departure and Direction-of-Arrival Estimation in a UWB MIMO Radar Detecting Targets with Fluctuating Radar Cross Sections

This paper presents a joint direction-of-departure (DOD) and direction-of-arrival (DOA) estimation in a multiple-input multiple-output (MIMO) radar utilizing ultra wideband (UWB) signals in detecting targets with fluctuating radar cross sections (RCS). The UWB MIMO radar utilized a combination of two-way MUSIC and majority decision based on angle histograms of estimated DODs and DOAs at each frequency of the UWB signal. The proposed angle estimation scheme was demonstrated to be effective in detecting targets with fluctuating RCS, compared to conventional spectra averaging method used in subband angle estimations. It was found that a wider bandwidth resulted in improved estimation performance. Numerical simulations along with experimental evaluations in a radio anechoic chamber are presented.

MAP Based Iterative Channel Estimation for OFDM Systems: Approach, Convergence, and Performance Bound

Iterative channel estimation (ICE) usually exploits soft information of unknown data symbols as references to improve estimation performance. This paper investigates ICE for orthogonal frequency division multiplexing (OFDM) over wireless channels. The optimum ICE is derived in terms of maximum a posteriori (MAP) criterion, which can be solved using fixed-point iteration (FPI). Furthermore, the derived MAP ICE is closely related to the well-known expectation-maximization (EM) estimation. We also demonstrate that the MAP ICE converges within only one step when the signal-to-noise ratio (SNR) is large through analysis and simulation results.

개선된 전차원 자속 관측기를 사용한 영구자석 동기전동기의 센서리스 속도 제어

The sensorless speed control using the improved full-order flux observer for PMSM is proposed in this paper. A conventional full-order flux observer has a drawback that the estimated flux of this observer contains the ripple component at the low speed range due to the increased gains of the convectional full-order flux observer. The improved full-order flux observer with the modified gains guarantee the improved estimation performance without ripple component at the from zero to high speed range. To identify the performance of proposed observer, the simulation and experiment are conducted and this performance is compared with the conventional full-order observer.

Improved Source Number Detection and Direction Estimation With Nested Arrays and ULAs Using Jackknifing

We consider the problem of source number detection and direction-of-arrival (DOA) estimation, based on uniform linear arrays (ULAs) and the newly proposed nested arrays. A ULA with N sensors can detect at most N-1 sources, whereas a nested array provides O(N2) degrees of freedom with O(N) sensors, enabling us to detect K sources with sensors. In order to make full use of the available limited valuable data, we propose a novel strategy, which is inspired by the jackknifing resampling method. Exploiting numerous iterations of subsets of the whole data set, this strategy greatly improves the results of the existing source number detection and DOA estimation methods. With the assumption that the subsets of the data set contain enough information, we theoretically prove that the improvement of detection or estimation performance, compared with the original performance without jackknifing, is guaranteed when the detection or estimation accuracy is greater than or equal to 50%. Numerical simulations demonstrate the superiority of our strategy when applied to source number detection and DOA estimation, both for ULAs and nested arrays.

Regularized Covariance Matrix Estimation in Complex Elliptically Symmetric Distributions Using the Expected Likelihood Approach— Part 1: The Over-Sampled Case

In Abramovich [“Bounds on Maximum Likelihood Ratio-Part I: Application to Antenna Array Detection-Estimation With Perfect Wavefront Coherence,” IEEE Trans. Signal Process., vol. 52, pp. 1524-1536, June 2004], it was demonstrated, for multivariate complex Gaussian distribution, that the probability density function (p.d.f.) of the likelihood ratio (LR) for the (unknown) actual covariance matrix R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> does not depend on this matrix and is fully specified by the matrix dimension M and the number of independent training samples T. This invariance property hence enables one to compare the LR of any derived covariance matrix estimate against this p.d.f., and eventually get an estimate that is statistically “as likely” as R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> . This “expected likelihood” quality assessment allowed significant improvement of MUSIC DOA estimation performance in the so-called “threshold area,” and for diagonal loading and TVAR model order selection in adaptive detectors. Recently, the so-called complex elliptically symmetric (CES) distributions have been introduced for description of highly in-homogeneous clutter returns. The aim of this series of two papers is to extend the EL approach to this class of CES distributions as well as to a particularly important derivative, namely the complex angular central distribution (ACG). For both cases, we demonstrate a similar invariance property for the LR associated with the true scatter matrix Σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> . Furthermore, we derive fixed point regularized covariance matrix estimates using the generalized expected likelihood methodology. This first part is devoted to the conventional scenario (T ≥ M) while Part II deals with the undersampled scenario (T ≤ M).

State Estimation in Unknown Non-Gaussian Measurement Noise using Variational Bayesian Technique

The problem of state space estimation of linear systems in an unknown non-Gaussian noise field is considered. A finite Gaussian mixture model (GMM) is used to model the non-Gaussian measurement noise with unknown statistics. A variational Bayesian expectation maximization (VBEM) algorithm is proposed to estimate the system states as well as the unknown parameters. In the variational Bayesian expectation (VBE) step, approximate inference is established to estimate the system state. The Gaussian mixture parameters are then updated in the variational Bayesian maximization (VBM) step. We also derive the true marginal posteriors to verify the performance of the proposed VBEM method. Computer simulations show that the proposed method has an improved estimation performance compared with other conventional approaches.

Unitary dual-resolution ESPRIT for joint DOD and DOA estimation in bistatic MIMO radar

This paper investigates the problem of joint direction of departure (DOD) and direction of arrival (DOA) estimation in bistatic MIMO radar. A new bistatic MIMO array, both the transmit array and the receive array consist of two separated subarrays with dual baselines is proposed, in contrast to the modeling of both the transmit array and the receive array to be uniform linear array in the existing research literature. And a new joint DOD and DOA estimation algorithm, called unitary dual-resolution ESPRIT, is proposed. First, the short baseline rotational invariance within subarray yields non-ambiguous coarse DOD and DOA estimation. Then the long baseline rotational invariance between subarray yields high accuracy but cyclically ambiguous DOD and DOA estimation. Last, the combination of both estimates yield high accuracy and non-ambiguous DOD and DOA estimation. Moreover, we propose to utilize the properties of centro-Hermitian matrices to transform the complex-valued computations into real-valued computations throughout the whole process. The proposed algorithm gives significant improvement in DOD and DOA estimation performance, with automatic pairing and without extra antennas and computational complexity compared with the conventional algorithms. Simulation results verify the effectiveness of the proposed algorithm.

Two‐step interference cancellation scheme in doubly selective channel estimation using superimposed training

Channel estimation for single-input–multiple-output systems over doubly selective channel using superimposed training and discrete prolate spheroidal basis expansion model is considered. To remove the interference from unknown data, a two-step interference cancellation scheme is proposed, where in the first step, a first-order statistics-based estimator with cyclic mean removal before transmission is proposed. In this step, only interference components corresponding to cyclic mean of data sequence are removed, which avoids the conflict between total interference elimination and symbol recovery. In the second step, a low complexity iterative interference cancellation scheme is proposed to further improve estimation performance with detected symbols at the receiver's end. Simulation results show that the proposed iterative scheme has the symbol error rate (SER) performance slightly inferior to that of the partially-data-dependent approach but with less computational complexity and outperforms the data-dependent scheme in terms of SER as well as mean-square error over doubly selective channels.

Comparison of Kasai Autocorrelation and Maximum Likelihood Estimators for Doppler Optical Coherence Tomography

In optical coherence tomography (OCT) and ultrasound, unbiased Doppler frequency estimators with low variance are desirable for blood velocity estimation. Hardware improvements in OCT mean that ever higher acquisition rates are possible, which should also, in principle, improve estimation performance. Paradoxically, however, the widely used Kasai autocorrelation estimator's performance worsens with increasing acquisition rate. We propose that parametric estimators based on accurate models of noise statistics can offer better performance. We derive a maximum likelihood estimator (MLE) based on a simple additive white Gaussian noise model, and show that it can outperform the Kasai autocorrelation estimator. In addition, we also derive the Cramer Rao lower bound (CRLB), and show that the variance of the MLE approaches the CRLB for moderate data lengths and noise levels. We note that the MLE performance improves with longer acquisition time, and remains constant or improves with higher acquisition rates. These qualities may make it a preferred technique as OCT imaging speed continues to improve. Finally, our work motivates the development of more general parametric estimators based on statistical models of decorrelation noise.

Traffic Queue Estimation for Metered Motorway On-Ramps through use of Loop Detector Time Occupancies

The primary objective of this study is to develop a robust queue estimation algorithm for motorway on-ramps. Real-time queue information is a vital input for dynamic queue management on metered on-ramps. Accurate and reliable queue information enables the management of on-ramp queues in a manner that adapts to the actual traffic queue size and thus minimizes the adverse impacts of queue flush while increasing the benefit of ramp metering. The proposed algorithm is based on the Kalman filter framework. The fundamental conservation model is used to estimate the system state (queue size) with the flow-in and flow-out measurements. These projection results are updated with the measurement equation by using the time occupancies from midlink and link entrance loop detectors. This study also proposes a novel singular-point correction method. This method resets the estimated system state to eliminate the counting errors that accumulate over time. In the performance evaluation, the proposed algorithm demonstrated accurate and reliable performance and consistently outperformed the benchmarked single-occupancy Kalman filter (SOKF) method. The improvements over the SOKF method were 62% and 63% on average for the estimation accuracy and reliability, respectively. The benefit of the innovative concepts of the algorithm is well justified by the improved estimation performance in conditions of congested ramp traffic, in which long queues may significantly compromise the benchmark algorithm's performance.

Improving visual estimation through active feedback

Summary In field surveys, ecological researchers and practitioners routinely make quantitative judgements that are known to vary in quality. Feedback about judgement accuracy is crucial for improving estimation performance yet is not usually afforded to fieldworkers. One reason it is rare lies in the difficulty of obtaining ‘true values’ (e.g. percentage cover) to learn from. Often, the only information we can access is other people's estimates of the same thing. Group average estimates tend to be remarkably accurate. By extension, receiving feedback about group averages may improve the estimation performance of individuals, dispensing with the need for ‘true values’ to learn from. In experiment 1, we tested whether feedback using group averages might improve estimates of species abundance as much as feedback using true values. However, not all feedback approaches are effective. In experiment 2, we compared two feedback formats for presenting information about group estimates of percentage cover. In both experiments, we used a novel 4‐point interval estimation approach to quantify uncertainty that is known to reduce overconfidence but is yet to be applied in ecology. Results from experiment 1 show that feedback about group averages improved performance (calibration and accuracy) almost as much as feedback about the truth, despite the fact that group averages were generally not close to true values. In contrast, group averages in experiment 2 were remarkably close to true values, but the only participants who improved their estimates were those who evaluated their own performance during the feedback session, using active feedback with a calibration component. Feedback reminds surveyors not to give over‐precise estimates and to appropriately reflect uncertainty. It improves calibration and accuracy of abundance estimates and could reasonably improve estimates of other quantities. Drawing on the wisdom of crowds, group averages could be used as a proxy for true values in feedback procedures. However, the format for delivering feedback matters. Actively engaging participants by having them evaluate their own estimation performance appears critical to improving their subsequent judgements, compared with passive feedback. We advocate the introduction of feedback into the training of ecologists.

Code-aided turbo synchronisation using irregular low-density parity check codes

An efficient scheme for iterative carrier recovery by using irregular low-density parity check (LDPC) codes is proposed at low signal-to-noise ratio (SNR) conditions. Owing to the significant effect of decoding extrinsic information on the carrier estimation algorithms, deficiency of extrinsic information from decoding caused by large residual frequency or phase offsets in initial iterations will result in incorrect parameter estimation and compensation, which would fail to converge toward low BER in decoding process. Irregular LDPC codes have inherence unequal error protection (UEP) capabilities wherein the high-degree nodes tend to correct their value more quickly than low-degree nodes. The more reliable information, which is provided by the high-degree nodes of irregular LDPC code in initial iterations, can be used to offer more reliable soft information for carrier synchronisation even in poor channel conditions, which leads to improvement of the carrier parameter estimation performance. The simulation results indicate that the more reliable information from irregular LDPC codes can cope with large frequency, phase uncertainty region and speed up the convergence properties for iterative synchronisers when comparing with regular LDPC codes with same parameters.

An Enhanced Channel Estimation with Partial Interference Cancelation for MU-MIMO System

In this letter, we propose an enhanced channel estimation method for multi-user multiple-input multiple output (MU-MIMO) transmission in long term evolution (LTE)-Advanced system. The proposed method partially estimates and eliminates the interference using simple matrix operations. After that, we apply an iterative channel estimation scheme. Due to the improvement of initial channel estimation performance, the proposed channel estimation method has significantly large performance gain compared to conventional method.

Multi-rate distributed fusion estimation for sensor networks with packet losses

This paper presents a distributed fusion estimation method for estimating states of a dynamical process observed by wireless sensor networks (WSNs) with random packet losses. It is assumed that the dynamical process is not changing too rapidly, and a multi-rate scheme by which the sensors estimate states at a faster time scale and exchange information with neighbors at a slower time scale is proposed to reduce communication costs. The estimation is performed by taking into account the random packet losses in two stages. At the first stage, every sensor in the WSN collects measurements from its neighbors to generate a local estimate, then local estimates in the neighbors are further collected at the second stage to form a fused estimate to improve estimation performance and reduce disagreements among local estimates at different sensors. Local optimal linear estimators are designed by using the orthogonal projection principle, and the fusion estimators are designed by using a fusion rule weighted by matrices in the linear minimum variance sense. Simulations of a target tracking system are given to show that the time scale of information exchange among sensors can be slower while still maintaining satisfactory estimation performance by using the developed estimation method.

Block-wise frequency offset estimation scheme in MIMO-OFDM systems

This paper proposes a robust residual carrier frequency offset (CFO) tracking scheme with an extended estimation range, as well as an improved estimation performance in a multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) system. An analytical expression for the mean square error (MSE) of the frequency synchronization scheme is reported, and simulation results are used to verify the theoretical analysis developed in this paper.