Proposed Estimation Algorithm Research Articles

이산 시간 선형 스위치드 시스템의 스위칭 신호 및 상태에 대한 강인한 추정 알고리즘

본 논문에서는 측정 잡음에 의해서 출력 신호가 왜곡된 이산 시간 선형 스위치드 시스템의 스위칭 신호와 상태 변수를 관측하기 위한 강인한 추정 및 검출 기법을 제시한다. 첫째, 최소 거리 기준에 기초한 작동 모드 추정 알고리즘이 제안된다. 이 과정에서 모드의 추정값을 얻게 되고, 이를 이용하여 상태 변수 또한 관측할 수 있게 된다. 둘째, 주어진 스위치드 시스템의 작동 모드 변화 시점, 즉, 스위칭이 언제 발생하는지 검출하는 알고리즘이 고안된다. 시스템에 인가되는 측정 잡음이 유계라는 가정 하에, 제안된 추정 알고리즘은 주어진 스위치드 시스템의 작동 모드를 정확하게, 그리고 상태 변수를 근사적으로 추정하며, 검출 알고리즘은 실제 스위칭이 발생한 후, 미리 정해진 지연 시간이 지나가기 전에 스위칭 시간을 검출할 수 있다.

A novel method for estimating external force: Simulation study with a 4-DOF robot manipulator

This paper proposes an algorithm to estimate external force exerted on the end-effector of a robot manipulator using information from joint torque sensors (JTS). The algorithm is the combination of Time Delay Estimation (TDE) and input estimation technique where the external force is considered as an unknown input to the robot manipulator. Based on TDE’s idea, the estimator which does not require an accurate dynamics model of the robot manipulator is developed. The simultaneous input and state estimation (SISE) is used to reject not only nonlinear uncertainties of the robot dynamics but also the noise of measurements. The performance of the proposed estimation algorithm is evaluated through simulation and experiment of a four degree-of-freedom manipulator and it demonstrates the stability and feasibility in estimating the external force. The estimation results show that this approach allows inexpensive sensors as joint torque sensors to be used instead of expensive ones as force/torque sensors in robot applications.

An Efficient Sensorless Slip Dependent Thermal Motor Protection Schemes Applied to Submersible Pumps

This paper proposes a sensorless procedure to estimate the induction motor speed and the dependable heat contents of the stator and rotor sides. The proposed procedure is based on the electrical models of a three phase Induction Motor (IM). The motor electrical models for normal and abnormal will be discussed and a technique is introduced for accommodating frequency dependent skin effect of the rotor resistance using a simple proposed speed estimation algorithm. The electrical models are customized from the positive and negative sequence networks. The speed detection is based on the rotor parameters slip dependent. The models are then used to analyze different operating conditions of the motor. Two thermal motor protection schemes are suggested. The first scheme is dependent on the stator side while the other scheme is developed for rotor side. The Matlab software is used for this purpose to emulate efficiently the proposed estimation procedures through a complete motor modeling which is fed from the power grid. Finally, the results provide the motor performance characteristics which involve current, torque, speed and stator/rotor temperature versus time for numerous operating conditions. It is concluded that the proposed sensorless procedure is efficient to protect the induction motors against abnormal starting as well as the overheating on either stator or rotor sides. Also, the proposed sensorless estimation for speed and temperature is reliable for submersible motor applications. The proposed schemes can be considered as costless preventive maintenance procedure.Index Terms: induction motor, slip dependent, sensorless, thermal model, abnormal operating conditions.

Parameter Estimation Technique for the SNCK Scheme Based on the Spectral-Correlation Density

The Sine Non-linear Chirp Keying (SNCK) is one kind of the SFM schemes, which play an important role as the commonly useful signals in sonar, radar and wireless communication systems. By adjusting its time-bandwidth product, the SNCK can satisfy different requirements of the quality of service and has better ability in Doppler shift inhibition than the ordinary MSK. However, no mathematical analysis for the time-bandwidth product (modulation factor) estimation of the SNCK scheme has been available. In this paper, a SCD-based estimation algorithm for the modulation factor of the SNCK scheme has been proposed. The mathematical model and diagrams of the three-dimensional SCD and the corresponding bi-frequency plane make it easy to understand how the SCD-based algorithm localizes the center frequency and the keying rate, as well as estimates the modulation factor. The simulation results show that the proposed estimation algorithm is useful under different SNR conditions. The corresponding Cramer---Rao lower bound are derived and used as the reference of the estimation algorithm. Moreover, we improve the estimation algorithm when the modulation factor varies, by using a set of the local ratio data, which compensates the differences between the simulation ratio values and the theoretical ratio results. This algorithm can be effective for the communication system with the SNCK scheme to synchronize the carrier and the symbol, as well as to demodulate the transmitted signals with unknown parameters. It is concluded that the obvious features of the SCD can be extracted for other similar signals with high reliability.

Hybrid Descriptor System State Estimation through an IMM Approach

Stochastic hybrid systems have been largely studied in the literature in the framework of Markov mode transitions and linear Gaussian state space mode models. In order to generalize such results to hybrid systems involving phenomena characterized by difierential- algebraic equations, this paper studies the problem of state estimation for stochastic hybrid systems with modes described by descriptor equations. The proposed state estimation algorithm follows the interacting multiple model (IMM for short) approach, but the classical state space system Kalman filters are replaced by descriptor system Kalman filters. Because of the dificulty for computing the innovation of each descriptor system Kalman filter, a new method for likelihood evaluation is proposed, as one important step of the new IMM algorithm. Numerical examples are presented to illustrate the performance of the proposed algorithm.

Contraction based identification for nonlinear systems

A top-down approach to identification for systems with nonlinear parameterizations has been recently developed in Flores-Perez et al. (2014, 2015) based on contraction analysis where, at the top-level, the contraction analysis is carried out to ensure the proposed estimation algorithm possess the desired contraction properties, then, at the down-level, the estimation algorithm is realized through a proportional plus integral (PI) form. The realization of estimation schemes however relies on the assumption that state time derivatives are available. In this paper, by filtering signals involved in the system, an alternative to the realization of estimation algorithms without requiring the state time derivatives is given.

In-wheel motor electric vehicle state estimation by using unscented particle filter

Vehicle state parameters are essential for active safety stability control and very valuable in chassis design evaluation. In this paper, a method for vehicle state parameters estimation is developed for in–wheel motor (IWM) electric vehicle (EV). The observer is based on information fusion combining standard sensor suite in today's typical vehicle and feedback signals from IWM. This paper utilise unscented particle filter (UPF) for tyre lateral force, longitudinal velocity, lateral velocity and yaw rate estimation, which is based on a numerically efficient nonlinear stochastic estimation technique. Planar vehicle model and dynamic tyre model are developed to describe behaviour of IWM EV. Detailed simulation verifies the validation and robustness of proposed estimation algorithm.

Variable selection for covariate adjusted regression model

This paper employs the SCAD-penalized least squares method to simultaneously select variables and estimate the coefficients for high-dimensional covariate adjusted linear regression models. The distorted variables are assumed to be contaminated with a multiplicative factor that is determined by the value of an unknown function of an observable covariate. The authors show that under some appropriate conditions, the SCAD-penalized least squares estimator has the so called “oracle property”. In addition, the authors also suggest a BIC criterion to select the tuning parameter, and show that BIC criterion is able to identify the true model consistently for the covariate adjusted linear regression models. Simulation studies and a real data are used to illustrate the efficiency of the proposed estimation algorithm.

Low-complexity quantize-and-forward cooperative communication using two-way relaying

Cooperative communication is used as an effective measure against fading in wireless communication systems. In a classical one-way cooperative system, the relay needs as many orthogonal channels as the number of terminal it assists, yielding a poor spectral efficiency. Efficiency is improved in two-way relaying systems, where a relay simultaneously assists two terminals using only one timeslot. In the current contribution, a two-way quantize-and-forward (QF) protocol is presented. Because of the coarse quantization, the proposed protocol has a low complexity at the relay and can be used with half-duplex devices, making it very suitable for low-complexity applications like sensor networks. Additionally, channel parameter estimation is discussed. By estimating all channel parameters at the destination terminals, relay complexity is kept low. Using Monte Carlo simulations, it is shown that the proposed QF protocol achieves a good frame error rate (FER) performance as compared to two-way amplify-and-forward (AF) and one-way relaying systems. It is further shown that, using the proposed estimation algorithm, the FER degradation arising from the channel parameter estimation is negligible when compared to an (unrealistic) system in which all parameters are assumed to be known.

Channel Estimation for Two-Way Relay Networks in the Presence of Synchronization Errors

This paper investigates pilot-aided channel estimation for two-way relay networks (TWRNs) in the presence of synchronization errors between the two sources. The unpredictable synchronization error leads to time domain offset and signal arriving order (SAO) ambiguity when two signals sent from two sources are superimposed at the relay. A two-step channel estimation algorithm is first proposed, in which the linear minimum mean-square-error (LMMSE) estimator is used to obtain initial channel estimates based on pilot symbols and a linear minimum error probability (LMEP) estimator is then developed to update these estimates. Optimal training sequences and power allocation at the relay are designed to further improve the performance for LMMSE based initial channel estimation. To tackle the SAO ambiguity problem, the generalized likelihood ratio testing (GLRT) method is applied and an upper bound on the SAO detection error probability is derived. By using the SAO information, a scaled LMEP estimation algorithm is proposed to compensate the performance degradation caused by SAO detection error. Simulation results show that the proposed estimation algorithms can effectively mitigate the negative effects caused by asynchronous transmissions in TWRNs, thus significantly outperforming the existing channel estimation algorithms.

Cascade Extended Kalman Filter 기반의 차량동특성 및 도로종단경사 추정

Vehicle dynamic states used in various advanced driving safety systems are influenced by road geometry. Among the road geometry parameters, the vehicle pitch angle influenced by road slope and acceleration-deceleration is essential parameters used in pose estimation, advanced adaptive cruise control and others on sag road. Although the road slope data is essential data, the method measuring the data is not commercialized. The digital map including the road geometry data and high-precision DGPS system such as DGPS(Differential Global Positioning System) based RTK(Real-Time Kinematics) are used unusually. In this paper, low-cost cascade extended Kalman filter(CEKF) based vehicle pitch angle estimation method is proposed. It use two cascade EKFs. The EKFs use several measured vehicle states, for example yaw rate, longitudinal acceleration, lateral acceleration and wheel speed of the rear tiers and 3 D.O.F(Degree Of Freedom) vehicle dynamics model. The performance of proposed estimation algorithm is evaluated by simulation based on Carsim dynamics tool and T-car based experiment.

Practical signal-dependent noise parameter estimation from a single noisy image.

The additive white Gaussian noise is widely assumed in many image processing algorithms. However, in the real world, the noise from actual cameras is better modeled as signal-dependent noise (SDN). In this paper, we focus on the SDN model and propose an algorithm to automatically estimate its parameters from a single noisy image. The proposed algorithm identifies the noise level function of signal-dependent noise assuming the generalized signal-dependent noise model and is also applicable to the Poisson-Gaussian noise model. The accuracy is achieved by improved estimation of local mean and local noise variance from the selected low-rank patches. We evaluate the proposed algorithm with both synthetic and real noisy images. Experiments demonstrate that the proposed estimation algorithm outperforms the state-of-the-art methods.

A Semi-Analytical Model for Delay/Doppler Altimetry and Its Estimation Algorithm

The concept of delay/Doppler (DD) altimetry (DDA) has been under study since the mid-1990s, aiming at reducing the measurement noise and increasing the along-track resolution in comparison with the conventional pulse-limited altimetry. This paper introduces a new model for the mean backscattered power waveform acquired by a radar altimeter operating in synthetic aperture radar mode, as well as an associated least squares (LS) estimation algorithm. As in conventional altimetry (CA), the mean power can be expressed as the convolution of three terms: the flat surface impulse response (FSIR), the probability density function of the heights of the specular scatterers, and the time/frequency point target response of the radar. An important contribution of this paper is to derive an analytical formula for the FSIR associated with DDA. This analytical formula is obtained for a circular antenna pattern, no mispointing, no vertical speed effect, and a uniform scattering. The double convolution defining the mean echo power can then be computed numerically, resulting in a 2-D semi-analytical model called the DD map (DDM). This DDM depends on three altimetric parameters: the epoch, the sea surface wave height, and the amplitude. A multi-look model is obtained by summing all the reflected echoes from the same along-track surface location of interest after applying appropriate delay compensation (range migration) to align the DDM on the same reference. The second contribution of this paper concerns the estimation of the parameters associated with the multi-look semi-analytical model. An LS approach is investigated by means of the Levenberg-Marquardt algorithm. Simulations conducted on simulated altimetric waveforms allow the performance of the proposed estimation algorithm to be appreciated. The analysis of Cryosat-2 waveforms shows an improvement in parameter estimation when compared to the CA.

Fractional Order Estimation Schemes for Fractional and Integer Order Systems with Constant and Variable Fractional Order Colored Noise

The paper presents estimation schemes for discrete fractional and integer order state-space systems with fractional order colored noise. The fractional order colored noise is a generalization of the traditional colored noise (noise with dynamic dependency) for the case when the dynamics of noise is of fractional order. Proposed estimation algorithm additionally uses information about noise dynamics, which allows for obtaining better estimates of state vector. The numerical experiments of estimation integer order system with fractional noise are presented as well.

Soft Decoding Assisted SNR Estimation Under Block Fading Channels for Orthogonal Modulations

The performance of the coded orthogonal modulation (OM) system under slow fading channels heavily depends on the estimation of the signal-to-noise ratio (SNR), including the fading amplitude and the noise spectral density. However, a relatively long packet of pilot symbols is often required to guarantee the accuracy of the SNR estimation, which makes it impractical in some situations. To address this problem, this paper proposes an iterative SNR estimation algorithm using the soft decoding information based on the expectation-maximization algorithm. In the proposed method, a joint iterative loop between the SNR estimator and decoder is performed, where the extrinsic information generated by the soft decoder is employed to enhance the estimation accuracy and the SNR estimated by the estimator is used to generate the soft information to the decoder. Also, no pilot symbols are needed to estimate the SNR in the proposed estimator. The Cramer---Rao lower bound (CRLB) of fully data-aided (FDA) estimation is derived to works as the final benchmark. The performance of the proposed algorithm is evaluated in terms of the normalized mean square errors (NMSEs) and the bit error rates (BERs) under block fading channels. Simulation results indicate that the NMSE of the proposed estimator reaches the CRLB of the FDA estimator and outperforms that of the approximate ML (ML-A) estimator proposed by Hassan et al. by 4.1 dB. The BER performance of coded OM system with the proposed estimation algorithm is close to the ideal case where the channel fading and the noise spectral density are known at the receiver.

Effective assessment of tyre–road friction coefficient using a hybrid estimator

Vehicle stability and active safety control depend heavily on tyre forces available on each wheel of a vehicle. Since tyre forces are strongly affected by the tyre–road friction coefficient, it is crucial to optimise the use of the adhesion limits of the tyres. This study presents a hybrid method to identify the road friction limitation; it contributes significantly to active vehicle safety. A hybrid estimator is developed based on the three degrees-of-freedom vehicle model, which considers longitudinal, lateral and yaw motions. The proposed hybrid estimator includes two sub-estimators: one is the vehicle state information estimator using the unscented Kalman filter and another is the integrated road friction estimator. By connecting two sub-estimators simultaneously, the proposed algorithm can effectively estimate the road friction coefficient. The performance of the proposed estimation algorithm is validated in CarSim/Matlab co-simulation environment under three different road conditions (high-μ, low-μ and mixed-μ). Simulation results show that the proposed estimator can assess vehicle states and road friction coefficient with good accuracy.

Novel Data Detection and Channel Estimation Algorithms for BICM-OFDMA Uplink Asynchronous Systems in the Presence of IQ Imbalance

The problem of channel estimation and in-phase/quadrature-phase (IQ) imbalance is one of the main challenges that has to be faced by uplink transmission in orthogonal frequency division multiple access (OFDMA) systems. The previously reported investigations for such systems are limited to synchronous and uncoded transmission. Furthermore, in these previous works, equalization of the IQ imbalance should be performed after the estimation process in order to properly detect data symbols. In this contribution, the transmit and receive IQ imbalance for uplink OFDMA systems is investigated in the context of asynchronous and bit-interleaved coded modulation (BICM) transmission. We propose a novel data detector exploiting the unwanted IQ as a beneficial resource to achieve a diversity gain, without the necessity of an additional equalizer. In addition, we develop a novel code-aided algorithm to estimate the propagation delays and the overall channel impulse responses (CIRs), which include the physical CIRs and IQ imbalance occurring at both the transmitters and base station. Since the exact maximum-likelihood (ML) solution to this problem turns out to be too complex for practical purposes, we resort to the space alternating generalized expectation-maximization (SAGE) algorithm as a low complexity method to perform parameter estimation. The proposed estimation algorithm operates in an iterative way, in which the soft information provided by the channel decoder is utilized as a priori information to refine the estimates. The computational complexity analysis and simulation results confirm the effectiveness of the proposed estimation algorithm and the proposed detector for practical application.

Estimation algorithm of minimum dwell time in precision cylindrical plunge grinding using acoustic emission signal

This work presents an investigation of the minimum dwell time during the precision cylindrical-plunge grinding process. According to the model of acoustic emissions proposed in earlier work, two estimation algorithms of the dwell time, focused on the size error and the surface roundness of the machined workpiece, are deduced from the models of the material removal and the surface roundness, respectively. Based on the proposed estimation algorithms, an on-line estimation method of the minimum dwell time is developed using the measurement and analysis of the acoustic emission signal. A series of experiments of grinding C45 steel are conducted to confirm the validity of the proposed estimation algorithms and determine the model coefficient. The ability of the estimation algorithms to predict the minimum dwell time is deemed necessary for optimal control of the grinding process. In addition, the proposed model provides a new approach to estimate the machined qualities of a workpiece such as the surface roughness and size error using the dwell time of the grinding process.

Accuracy of Phase-Based Algorithms for the Estimation of the Specific Differential Phase Shift Using Simulated Polarimetric Weather Radar Data

The specific differential phase shift on propagation K <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dp</sub> is widely employed in the study of precipitation, although little is known about the effective accuracy of its estimates. The aim of this letter is to analyze the quality of K <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dp</sub> estimates, using realistic simulated fields of drop size distributions. Two classical and one recently proposed estimation algorithms are tested, which are chosen among the algorithms that use the measured and noisy total differential phase shift Ψ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dp</sub> as their main input. A data set of six simulated rain events, from which polarimetric radar variables can be derived, is employed in this letter. The mean normalized absolute error in the estimation of K <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dp</sub> at the radar resolution volume scale ranges from 27% to 30% for all the algorithms proposed, and significant negative biases up to -50% emerge at the highest values of K <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dp</sub> for the most biased algorithm. The new algorithm, which is based on Kalman filtering, is able to keep these localized bias values around -25% and outperforms the classical algorithms in terms of efficiency, correlation, and root-mean-square error.

Carrier frequency offset estimation for OFDMA uplink using null sub-carriers

Carrier synchronization in OFDMA uplink systems comprises estimation and compensation of carrier frequency offsets (CFOs) corresponding to different users. We propose a null sub-carrier-based CFO estimation algorithm for OFDMA uplink systems, employing generalized or sub-band sub-carrier allocation scheme. We analyze the effect of location and number of null sub-carriers on the performance of the algorithm, in the presence of multi-user interference (MUI) and channel frequency selectivity. We calculate the acquisition range of the proposed estimation algorithm and prove that the identifiability of CFO estimation is guaranteed in the presence of MUI and channel nulls. We also evaluate the computational complexity and the performance of the proposed estimation algorithm and, compare it with some of the existing approaches in the literature.