Proposed Estimation Algorithm Research Articles

Forward–backward processing for efficient underground channel estimation in 60 GHz MISO FBMC systems

In this Letter, a novel low complexity sparse channel estimation method based on forward–backward processing is proposed for mm-Wave underground channel incorporating a 2 × 1 space-time-block coding filter bank multi-carrier (STBC-FBMC) system, which employs quadrature amplitude modulation. The proposed estimation algorithm relies on the idea of the channel support forward selection and backward removal, which iteratively expands and compresses the support estimate, hence improving the ability to cover up for the errors made in the conventional orthogonal matching pursuit (OMP) algorithm. By contrast to the latter, the most innovative feature of the proposed approach is its capability of effective channel reconstruction without prior information of the sparsity level and its adaptive step size method to approach the true sparsity level. The proposed approach is shown to achieve a higher estimation accuracy than OMP alternative.

Adaptive Cooperative Localization Using Relative Position Estimation for Networked Systems With Minimum Number of Communication Links

In this paper, a novel solution for cooperative localization problem involving a network with multiple mobile agents accessing to only one beacon agent is presented. The solution can be applied to a network with a minimum number of communication links (subjecting the network topology to be an undirected spanning tree). The objective is to provide each agent in the network with information on its absolute position without using any on-board global positioning sensors such as GPS receivers. The proposed solution uses an adaptive relative position estimation algorithm for each pair of neighboring mobile agents. The proposed estimation algorithm requires each agent to measure the relative inter-agent distance. The local velocity vector is also measured and transmitted to the neighboring agents. The estimation mechanism incorporating a signum function outperforms the recently established relative position estimation algorithms, in terms of positioning and tracking errors. An adaptive cooperative localization (ACL) algorithm is formed by augmenting the relative position estimation in a cooperative observer scheme suitably applicable for accomplishing localization task involving a network of mobile agents. The salient feature of the proposed ACL algorithm is that the communication graph among the agents needs only to have one undirected path between two agents in the network. Such convenience promotes easy practical implementation and lite computation for each agent. The proof of the proposed algorithm is provided using the Lyapunov stability theorem. Three simulation case studies are presented to evaluate the performance of the solution in different scenarios, including the stationary and moving beacon agent as well as the non-cooperatively controlled and cooperatively controlled network of mobile agents. The comparative studies reveal that the ACL algorithm is superior to the recently investigated linear-convex algorithm. The number of communication links required for the localization task to be carried out by the proposed algorithm is minimum, thus promoting a more preferable energy-efficient solution.

Modelling and estimation of nonlinear quantile regression with clustered data

In regression applications, the presence of nonlinearity and correlation among observations offer computational challenges not only in traditional settings such as least squares regression, but also (and especially) when the objective function is nonsmooth as in the case of quantile regression. Methods are developed for the modelling and estimation of nonlinear conditional quantile functions when data are clustered within two-level nested designs. The proposed estimation algorithm is a blend of a smoothing algorithm for quantile regression and a second order Laplacian approximation for nonlinear mixed models. This optimization approach has the appealing advantage of reducing the original nonsmooth problem to an approximated L2 problem. While the estimation algorithm is iterative, the objective function to be optimized has a simple analytic form. The proposed methods are assessed through a simulation study and two applications, one in pharmacokinetics and one related to growth curve modelling in agriculture.

A Novel Hybrid RSS and TOA Positioning Algorithm for Multi-Objective Cooperative Wireless Sensor Networks

Due to the significance of positioning in complicated electromagnetic environment of its variety of applications, wireless localization techniques have attracted significant research interests with the trends moving toward boosting the capabilities of multi-objective cooperative networks. This paper addresses the problem of locating multiple targets in 3-D cooperative wireless sensor network. A novel hybrid received signal strength (RSS) and time-of-arrival (TOA) positioning scheme is proposed. Employing RSS and TOA measurements, four valuable expressions are drawn. The TOA and RSS technologies are combined with path loss difference variable being introduced. Based on the weighted least squares criterion, the rough solutions of multiple targets positions are derived. Finally, the locations of obtained targets are optimized by cooperative wireless communication between the target and another target. The location accuracy of the proposed estimation algorithm is analyzed and compared with the conventional RSS and TOA estimation algorithms through simulation experiments. The simulation result demonstrates that the proposed hybrid RSS and TOA localization algorithm for multi-objective cooperative wireless networks outperforms traditional approaches.

SNR analysis and estimation for efficient phase noise mitigation in millimetre‐wave SC‐FDE systems

This study demonstrates a signal-to-noise ratio (SNR) analysis and estimation algorithm for efficient phase noise mitigation that can be practically applied to single-carrier frequency-domain-equalisation (SC-FDE) systems that operate in millimetre-wave bands. First, the effect of phase noise in SC-FDE systems is investigated on each of the packet reception processes, namely, channel estimation, SNR estimation, and data-field reception. According to the analysis, an SNR estimation algorithm is proposed. The performance of minimum-mean-square-error equalisation and conventional phase noise mitigation algorithm can be enhanced using the proposed SNR estimation. The effectiveness of the proposed analysis and SNR estimation algorithm is verified through the link-level simulation. Compared with the conventional SNR estimation and the iterative phase noise mitigation algorithms, the proposed algorithm provides a lower packet-error rate without any iterative decoding process.

A Novel Robust Model Predictive Controller for Aerospace Three-Phase PWM Rectifiers

This paper presents a novel Model Predictive Direct Power Control (MPDPC) approach for the pulse width modulation (PWM) rectifiers in the Aircraft Alternating Current Variable Frequency (ACVF) power system. The control performance of rectifiers may be largely affected by variations in the AC side impedance, especially for systems with limited power volume system. A novel idea for estimating the impedance variation based on the Bayesian estimation, using an algorithm embedded in MPDPC is presented in this paper. The input filter inductance and its equivalent series resistance (ESR) of PWM rectifiers are estimated in this algorithm by measuring the input current and input voltage in each cycle with the probability Bayesian estimation theory. This novel estimation method can overcome the shortcomings of traditional data based estimation methods such as least square estimation (LSE), which achieves poor estimation results with the small samples data set. In ACVF systems, the effect on the parameters estimation accuracy caused by the number of sampling points in one cycle is also analyzed in detail by simulation. The validity of this method is verified by the digital and Hard-in-loop simulation compared with other estimation methods such as the least square estimation method. The experimental testing results show that the proposed estimation algorithm can improve the robustness and the control performance of the MPDPC under the condition of the uncertainty of the AC side parameters of the three-phase PWM rectifiers in aircraft electrical power system.

Accelerated adaptive super twisting sliding mode observer‐based drive shaft torque estimation for electric vehicle with automated manual transmission

The suddenly released torque that accumulated in the elastic drive shaft will bring torsional vibration and jerking feel at the shifting moment. A novel sliding mode observer is proposed to estimate the torque in drive shaft for a motor-transmission integrated powertrain system. Non-linear external characteristics of a driving motor and non-linear drag torque are considered in the electric powertrain system. In order to attenuate the chatting problem, the second-order super twisting sliding mode algorithm with an adaptive gain is adopted. Furthermore, a term `system damping' is introduced to accelerate the estimation error convergence. The proposed estimation algorithm is tested on test rig for typical operating conditions. The results show that the torque in drive shaft can be estimated satisfactorily and the tracking error converges to 0 in a short time.

Joint state and fault estimation for time-varying nonlinear systems with randomly occurring faults and sensor saturations

This paper is concerned with the joint state and fault estimation problem for a class of uncertain time-varying nonlinear stochastic systems with randomly occurring faults and sensor saturations. A random variable obeying the Bernoulli distribution is used to characterize the phenomenon of the randomly occurring faults and the signum function is employed to describe the sensor saturation due to physical limits on the measurement output. The aim of this paper is to design a locally optimal time-varying estimator to simultaneously estimate both the system states and the fault signals such that, at each sampling instant, the covariance of the estimation error has an upper bound that is minimized by properly designing the estimator gain. The explicit form of the estimator gain is characterized in terms of the solutions to two difference equations. It is shown that the developed estimation algorithm is of a recursive form that is suitable for online computations. In addition, the performance analysis of the proposed estimation algorithm is conducted and a sufficient condition is given to verify the exponential boundedness of the estimation error in the mean square sense. Finally, an illustrative example is provided to show the usefulness of the developed estimation scheme.

Connected cruise control among human-driven vehicles: Experiment-based parameter estimation and optimal control design

In this paper, we consider connected cruise control design in mixed traffic flow where most vehicles are human-driven. We first propose a sweeping least square method to estimate in real time feedback gains and driver reaction time of human-driven vehicles around the connected automated vehicle. Then we propose an optimal connected cruise controller based on the mean dynamics of human driving behavior. We test the performance of both the estimation algorithm and the connected cruise control algorithm using experimental data. We demonstrate that by combining the proposed estimation algorithm and the optimal controller, the connected automated vehicle has significantly improved performance compared to a human-driven vehicle.

DC Coefficient Estimation of Intra-Predicted Residuals in HEVC

This paper presents a DC coefficient estimation algorithm for intra-predicted residual blocks in the High-Efficiency Video Coding (HEVC) standard. Discarding the DC coefficient directly in each transform block leads to substantial bit-saving, but at the same time produces strong discontinuities between neighboring blocks. To overcome this problem, we propose an estimation algorithm for the DC coefficient, which solves an optimal offset in a closed-form to recover the corresponding block edges. Then, we embed this algorithm into HEVC in its rate-distortion optimized quantization and sign bit hiding steps. Furthermore, a flag is signaled to decide whether the DC estimation strategy is used for each transform block. Test results under the common test condition show that our algorithm achieves 1.5% and 1.6% BD-rate reduction on average for luma and chroma, respectively, under all intra configuration. In the meantime, our simulation results show that both encoding time and decoding time increase only slightly (about 10%, without any special optimization on programming our proposed algorithm). When testing the proposed DC estimation algorithm on inter coding configurations, including low delay with P pictures, low delay with B pictures, and random access, we can also achieve 0.5%–1.1% bit-rate savings on average, while nearly no extra encoding and decoding time is needed.

Computationally Efficient Self-Tuning Controller for DC–DC Switch Mode Power Converters Based on Partial Update Kalman Filter

In this paper, a partial update Kalman Filter (PUKF) is presented for the real-time parameter estimation of a dc–dc switch mode power converter. The proposed estimation algorithm is based on a novel combination between the classical Kalman filter (KF) and an M-Max partial adaptive filtering technique. The proposed PUKF offers a significant reduction in computational effort compared to the conventional implementation of the KF, with 50% less arithmetic operations. Furthermore, the PUKF retains comparable overall performance to the classical KF. To demonstrate an efficient and cost effective explicit self-tuning controller, the proposed estimation algorithm (PUKF) is embedded with a Banyasz/Keviczky proportional, integral, derivative (PID) controller to generate a new computationally light self-tuning controller. Experimental and simulation results clearly show the superior dynamic performance of the explicit self-tuning control system compared to a conventional pole placement design based on a precalculated average model.

Accurate and Efficient Image Forgery Detection Using Lateral Chromatic Aberration

In copy-and-paste image forgeries, where image content is copied from one image and pasted into another, inconsistencies in an imaging feature called lateral chromatic aberration (LCA) are intrinsically introduced. In this paper, we propose a new methodology to detect forged image regions that is based on detecting localized LCA inconsistencies. To do this, we propose a statistical model that captures the inconsistency between global and local estimates of LCA. We then use this model to pose forgery detection as a hypothesis testing problem and derive a detection statistic, which we show is optimal when certain conditions are met. To test its detection efficacy, we conduct a series of experiments that demonstrate our proposed methodology significantly outperforms prior art and addresses deficiencies of previous research. Additionally, we propose a new and efficient LCA estimation algorithm. To accomplish this we adapt a block matching algorithm, called diamond search, which efficiently measures the LCA in a localized region. We experimentally show that our proposed estimation algorithm reduces estimation time by two orders of magnitude without introducing additional estimation error.

Reduced order Kalman filter for a continuous-time fractional-order system using fractional-order average derivative

This paper investigates two kinds of reduced order Kalman filters for a continuous-time fractional-order system with uncorrelated and correlated process and measurement noises. The fractional-order average derivative is adopted to enhance the discretization accuracy for the investigated continuous-time fractional-order system. The uncorrelated and correlated cases for the process and measurement noises are treated by the reduced order Kalman filters to achieve the robust estimation for a part of states of a fractional-order system. The truncation issue is considered to implement the practical application of the proposed state estimation algorithm. Finally, two examples for uncorrelated and correlated noises are offered to verify the effectiveness of the proposed reduced order Kalman filters.

Inertial Parameter Estimation of an Excavator with Adaptive Updating Rule Using Performance Analysis of Kalman Filter

This paper presents a rotational inertia estimation algorithm for excavators based on recursive least-squares with forgetting and an adaptive updating rule that uses the performance analysis of the Kalman filter. Generally, excavators execute a swing motion with various materials, and the rotational inertia of the excavator is changed greatly due to the excavator’s working posture. The large variation in the rotational inertia of the excavator has an influence on the dynamic behaviors of the excavator, and an estimation of the excavator’s rotational inertia is essential to developing a safety system based on prediction of dynamic behavior. Therefore, a real-time rotational inertia estimation algorithm has been proposed in this study using a swing dynamic model. The proposed estimation algorithm has been designed using only swing velocity, utilizing the recursive least squares method with multiple forgetting for practical application to actual excavators. Two updating rules have been applied to the estimation algorithm in order to enhance the estimation performance. The first proposed rule is the damping coefficient updating rule. The second rule is the forgetting factor updating rule based on real-time analysis of linear Kalman filter estimation performance. The performance evaluation of the estimation algorithm proposed in this paper has been conducted based on the excavator’s typical dumping scenario. The performance evaluation results show that the developed inertia estimation algorithm can estimate actual rotational inertia with the two designed updating rules using only excavator swing velocity.

Maximum‐likelihood synchronization and channel estimation with multiuser detection in GFDMA

AbstractAccurate estimation and correction of channel distortions and carrier frequency offset (CFO) are of a great importance in any multicarrier communication system. Hence, in this paper, we propose data‐aided CFO and channel estimation techniques for both multiuser uplink and downlink of the generalized frequency division multiple access (GFDMA). Our proposed solutions jointly estimate the CFO and channel responses based on the maximum‐likelihood criterion. To simplify the implementation of the proposed estimation algorithms, we suggest a preamble composed of two similar Zadoff‐Chu training sequences in a generalized frequency division multiplexing block. It is worth mentioning that our proposed technique can estimate both integer and fractional CFO values without any limitation on the acquisition range of CFO. In the uplink phase, each user aligns its carrier frequency with the base station using the estimated CFO in the downlink. However, the CFO estimates may get outdated for the uplink transmission. Thus, residual CFOs may still remain in the received signal at the base station. While being trivial in the downlink, CFO correction is a challenging task in the uplink. Thus, we also propose a joint CFO correction and channel equalization technique for the uplink of GFDMA systems. Finally, we evaluate our proposed estimation and correction algorithms in terms of estimation mean square error and bit error rate performance through simulations.

Estimation of Hidden Chemoattractant Field from Observed Cell Migration Patterns

Neutrophilic chemotaxis is essential to immune system response to external threats. During this process cells alternate between directed motion towards the higher concentration of external stimuli and correlated random walk. An individual neutrophil migration can thus be characterised as a stochastic dynamical process driven by an external chemotactic environment that is typically not measured. This introduces the problem of estimating spatially-varying chemoattractant concentration field from the observed migration patterns of cell populations. We propose a solution to this estimation problem in a statistical inference framework. The framework has measured cell positions in the field as inputs and employs the expectation-maximisation algorithm for joint estimation of full cell states and parameters of the chemoattractant field decomposed with cubic B-splines. The performance of the developed algorithm is accessed via process in vivo measurements of cell positions in the injured tail fin of zebrafish. Estimation results for different injury types evidence that the proposed estimation algorithm provides a rigorous connection between mathematical modelling and experimental data.

Generalized Byzantine Attack and Defense in Cooperative Spectrum Sensing for Cognitive Radio Networks

Cognitive radio (CR) is a revolutionary paradigm to solve the spectrum scarcity problem in wireless networks. In cognitive radio networks, cooperative spectrum sensing is regarded as a promising approach method to significantly improve the performance of spectrum sensing, but it can be threatened by Byzantine attack. The existing defense references have focused on how to mitigate the negative effect of Byzantine attack, but with some strong assumptions, such as the attackers are in minority and/or a trusted node exists for data fusion. This observation motivates us to comprehensively analyze the strategies of Byzantine attack and the fusion center (FC) in the absence of these restrictions. To be specific, we consider a generic Byzantine attack model by analyzing sophisticated malicious behaviors, which goes beyond the existing models for its generalization. Under this generalized attack model, we derive the condition which makes the FC blind from malicious perspective. On this basis, the optimal attack strategy to maximize Bayes risk is analyzed, respectively, in the case of the unknown and known fusion rule. Furthermore, we extend our analysis to the scenario where the FC has the knowledge of the attack strategy by an estimation algorithm and adopts the optimal fusion rule. Thus, we also give the closed form expression, in terms of the optimal attack strategy under different scenarios, sequentially. At last, the extensive numerical results are provided to verify our theoretical analyses and proposed estimation algorithm.

Research on Multiple States Joint Estimation Algorithm for Electric Vehicles Under Charge Mode

Power battery is the core component of electric vehicles, and its characteristics determine the performance of electric vehicles. Lithium-ion power battery is a kind of time-varying nonlinear system, which has different external features under different application conditions and aging state. In order to implement the optimized charging method under changeable application conditions, the estimation of the internal states is extremely necessary. For this purpose, this paper studies the multiple states joint estimation based on the accurate and reliable battery modeling method. First, the modeling of lithium-ion battery and the parameter identification of the models are studied. A parameter identification method is proposed based on forgetting factor recursive extended least square. The battery model’s order is based on Bayesian information criterions. Second, multiple states joint estimation algorithm for power battery under charging mode is studied. For the estimated accuracy of the battery’s state of charge is associated with the battery usable capacity, state of charge and usable capacity joint estimation algorithm, based on several different order battery models is proposed. State of power estimation algorithm of the battery under multiple factors constraints is proposed based on the solution discrete analysis of the continuous time traction battery differential state equation. Finally, multiple states joint estimation algorithm is achieved. Validation results show that the proposed estimation algorithm can achieve high accuracy.

State Estimation for Dynamic Systems With Unknown Process Inputs and Applications

The problem of state estimation for discrete-time stochastic time-varying systems in the presence of unknown process inputs or disturbances is addressed in this paper. A Kalman-type filter is proposed, and the optimal oracle filter gain in the sense of minimizing the mean squared error of the state estimate is obtained. To tackle the unknown quantities in the gain matrix, a nonlinear equation is introduced and its solution is taken as the estimate of unknown inputs, and then, a novel nonlinear equation-based unknown input filtering (NEUIF) is proposed. A scalar-based iterative algorithm for related fixed point problem is developed so that the dichotomy method is employed to solve the above nonlinear equation very efficiently. Adopting the same strategy for the dynamic systems with unknown inputs or disturbances, we provide two applications of the proposed state estimation algorithm. One is for a class of nonlinear dynamic systems with linear observations by taking the residual term in linearizing the transition function as an unknown input in the derived linear system. The other is for tracking maneuvering targets in which the bias between the real motion and modeled motions is regarded as an unknown input in the state transition equation. Some numerical simulations demonstrate the effectiveness of the proposed NEUIF method for tackling various uncertainties in complicated dynamic systems.

Performance Comparison of Mode Choice Optimization Algorithm with Simulated Discrete Choice Modeling

Until recently, a majority of modeling tasks of transportation planning, especially in discrete choice modeling, is conducted with the help of commercial software and only concerned about the result of parameter estimates to get a policy-sensitive interpretation. This common practice prevents researchers from gaining a systematic knowledge involved in estimation mechanism. In this research, to shed a light on these limited modeling practices, a standard discrete choice model’s parameter is estimated using Quasi-Newton method, DFP, and BFGS. Two extended algorithms, called DFP-GSM and BFGS-GSM, are proposed for the first time to overcome the weakness of the Quasi-Newton method. The golden section method (GSM) incorporates a nonlinear programming technique to choose an optimal step size automatically. Partial derivatives of log-likelihood function are derived and coded using Visual Basic Application (VBA). Through extensive numerical evaluation, estimation capability of each proposed estimation algorithms is compared in terms of performance measures. The proposed algorithms show a stable estimation performance and the reasons were studied and discussed. Furthermore, useful insights educated in custom-built modeling are present.