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Traditional recursive least squares (RLS) adaptive filtering is widely used to estimate the impulse responses (IR) of an unknown system. Nevertheless, the RLS estimator shows poor performance when tracking rapidly time-varying systems. In this paper, we propose a multi-layered RLS (m-RLS) estimator to address this concern. The m-RLS estimator is composed of multiple RLS estimators, each of which is employed to estimate and eliminate the misadjustment of the previous layer. It is shown that the mean squared error (MSE) of the m-RLS estimate can be minimized by selecting the optimum number of layers. We provide a method to determine the optimum number of layers. A low-complexity implementation of m-RLS is discussed and it is indicated that the complexity order of the proposed estimator can be reduced to ${\mathcal O}(M)$ , where $M$ is the IR length. Through simulations, we show that m-RLS outperforms the classic RLS and the RLS methods with a variable forgetting factor. [ABSTRACT FROM AUTHOR]

In graph signal processing (GSP), complex datasets arise from several underlying graphs and in the presence of heterogeneity. Graph learning from heterogeneous graph signals often results in challenging high-dimensional multiple graph estimation problems, and prior information regarding which graph the data was observed is typically unknown. To address the above challenges, we develop a novel framework called GRACGE (GRAph signal Clustering and multiple Graph Estimation) to partition the graph signals into clusters and jointly learn the multiple underlying graphs for each of the clusters. GRACGE advocates a regularized EM (rEM) algorithm where a structure fusion penalty with adaptive regularization parameters is imposed on the M-step. Such a penalty can exploit the structural similarities among graphs to overcome the curse of dimensionality. Moreover, we provide a non-asymptotic bound on the estimation error of the GRACGE algorithm, which establishes its computational and statistical guarantees. Furthermore, this theoretical analysis motivates us to adaptively re-weight the regularization parameters. With the adaptive regularization scheme, the final estimates of GRACGE will geometrically converge to the true parameters within statistical precision. Finally, experimental results on both synthetic and real data demonstrate the performance of the proposed GRACGE algorithm. [ABSTRACT FROM AUTHOR]

Coherent forecasting techniques for count processes generate forecasts that consist of count values themselves. In practice, forecasting always relies on a fitted model and so the obtained forecast values are affected by estimation uncertainty. Thus, they may differ from the true forecast values as they would have been obtained from the true data generating process. We propose a computationally efficient resampling scheme that allows to express the uncertainty in common types of coherent forecasts for count processes. The performance of the resampling scheme, which results in ensembles of forecast values, is investigated in a simulation study. A real-data example is used to demonstrate the application of the proposed approach in practice. It is shown that the obtained ensembles of forecast values can be presented in a visual way that allows for an intuitive interpretation. [ABSTRACT FROM AUTHOR]

The problem of developing a controller for spacecraft to accomplish attitude maneuvers within a prescribed time, independently of the initial states, is addressed. A fixed-time disturbance observer-based control is established and proved to drive the error state to zero, in a prescribed time, in the presence of modeling and external uncertainties. The key advantages of the proposed control are that the gains are explicitly determined by the prescribed maneuvering time only and that it is also robust to instantaneous disturbances. The control approach enables an efficient design process for attitude control applications with time constraints. Simulation results are presented for the attitude control of a rigid spacecraft to verify the benefits of this control scheme. [ABSTRACT FROM AUTHOR]

Flux linkage estimation based on the voltage model has been widely employed in surface-mounted permanent magnet synchronous machine (SPMSM) sensorless drives due to its simple implementation. To deal with dc-offset and dc-drift problems, the pure integrator is modified as a programmable low pass filter (LPF)in practice. However, the estimation accuracy of the rotor position is affected by the adopted filter. Although the position deviation is compensated in steady-state based on the frequency characteristics of the filter, the compensation effect deteriorates during a dynamic process which severely degrades the sensorless control performance. In order to effectively tackle this problem, a novel compensation strategy based on the linear extended state observer (LESO) is proposed in this paper. With the constructed LESO in the estimated synchronous rotating reference frame, the position estimation error can be detected and compensated both in the steady and dynamic state. Moreover, the observer is enhanced to improve the estimation accuracy in linear acceleration and deceleration. Finally, the feasibility of the proposed method is verified under various operating conditions with a 5.5 kW SPMSM drive system. [ABSTRACT FROM AUTHOR]

Position estimation error caused by the cross-saturation effect seriously affects the performance of signal injection based sensorless control, especially for the surface permanent-magnet synchronous motor (SPMSM) with load. In this paper, a position estimation error compensation method is proposed to improve the accuracy and stability of sensorless control for a dual three-phaseSPMSM. Based on the space vector signal injection, the initial position is obtained by the demodulation of high-frequency current response. By analyzing the relationship between the stator flux and stator current, the optimal compensation angle can be achieved. The position estimation error can be dynamically compensated under load conditions with the combination of initial position and compensation angle. Due to the convergence of compensation angle, the compensation process has weak dependence on prior knowledge of the motor. Simulation and experimental results are presented to validate the proposed method. [ABSTRACT FROM AUTHOR]

This article proposes a non-smooth funnel control (NSFC) strategy based on the extended state observer (ESO) for motor-drive servo systems with unknown dynamics. These unknown dynamics, including the modeling error, the external disturbances, and the non-linear friction, are considered as a lumped disturbance. For estimating the unknown states and the lumped disturbance, a non-linear bandwidth extended state observer (NLB-ESO) is developed. The NLB designed using the Gaussian function and the position observation error can deal with the “peaking value problem” caused by the high observer gains. Furthermore, a funnel error surface is proposed based on the non-smooth error transformation (NSET) and incorporated into the dynamic surface control design procedure, where the NSFC can prescribe the position tracking error in a funnel region with small steady-state error and overshoot and short adjustment time. The non-smooth funnel error surface also avoids the singular problem in controller design by selecting the non-smooth parameter. Moreover, the stability of the closed-loop control system with the NLB-ESO is guaranteed by the Lyapunov theory. Comparative simulations and experiments show that the proposed NSFC can prescribe the tracking error in a funnel and ensure better tracking performance. [ABSTRACT FROM AUTHOR]

In the Gaussian sequence model $Y= \theta _{0} + \varepsilon $ in $\mathbb {R}^{n}$ , we study the fundamental limit of statistical estimation when the signal $\theta _{0}$ belongs to a class $\Theta _{n}(d,d_{0},k)$ of (generalized) splines with free knots located at equally spaced design points. Here $d$ is the degree of the spline, $d_{0}$ is the order of differentiability at each inner knot, and $k$ is the maximal number of pieces. We show that, given any integer $d\geq 0$ and $d_{0}\in \{-1,0,\ldots,d-1\}$ , the minimax rate of estimation over $\Theta _{n}(d,d_{0},k)$ exhibits the following phase transition: $\begin{aligned} \inf _{ \widetilde {\theta }}\sup _{\theta \in \Theta _{n}(d,d_{0}, k)} \mathbb {E}_\theta \lVert \widetilde {\theta } - \theta \rVert _{}^{2} \asymp _{d} \begin{cases} k\log \log (16n/k), & 2\leq k\leq k_{0},\\ k\log (en/k), & k \geq k_{0}+1. \end{cases} \end{aligned}$ The transition boundary $k_{0}$ , which takes the form $\left \lfloor{ (d+1)/(d-d_{0}) }\right \rfloor + 1$ , demonstrates the critical role of the regularity parameter $d_{0}$ in the separation between a faster $\log \log (16n)$ and a slower $\log (en)$ rate. We further show that, once encouraging an additional ‘ $d$ -monotonicity’ shape constraint (including monotonicity for $d = 0$ and convexity for $d=1$), the above phase transition is removed and the faster $k\log \log (16n/k)$ rate can be achieved for all $k$. These results provide theoretical support for developing $\ell _{0}$ -penalized (shape-constrained) spline regression procedures as useful alternatives to $\ell _{1}$ - and $\ell _{2}$ -penalized ones. [ABSTRACT FROM AUTHOR]

This article studies the reachable set of cyber-physical systems subject to stealthy attacks with the Kullback–Leibler divergence adopted to describe the stealthiness. The reachable set is defined as the set in which both the system state and the estimation error of the Kalman filter reside with a certain probability. The necessary and sufficient conditions of the reachable set being unbounded are given for the finite and infinite time cases, respectively. When the reachable set is bounded, an ellipsoidal outer approximation is obtained by solving a convex optimization problem. An application of this approximation to the safety evaluation is also given. A numerical simulation of an unmanned ground vehicle is presented to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This article is concerned with the problem of recursive state estimation for a class of multirate multisensor systems with distributed time delays under the round-robin (R-R) protocol. The state updating period of the system and the sampling period of the sensors are allowed to be different so as to reflect the engineering practice. An iterative method is presented to transform the multirate system into a single-rate one, thereby facilitating the system analysis. The R-R protocol is introduced to determine the transmission sequence of sensors with the aim to alleviate undesirable data collisions. Under the R-R protocol scheduling, only one sensor can get access to transmit its measurement at each sampling time instant. The main purpose of this article is to develop a recursive state estimation scheme such that an upper bound on the estimation error covariance is guaranteed and then locally minimized through adequately designing the estimator parameter. Finally, simulation examples are provided to show the effectiveness of the proposed estimator design scheme. [ABSTRACT FROM AUTHOR]

Aiming at recovering an unknown tensor (i.e., multi-way array) corrupted by both sparse outliers and dense noises, robust tensor decomposition (RTD) serves as a powerful pre-processing tool for subsequent tasks like classification and target detection in many computer vision and machine learning applications. Recently, tubal nuclear norm (TNN) based optimization is proposed with superior performance as compared with other tensorial nuclear norms for tensor recovery. However, one major limitation is its orientation sensitivity due to low-rankness strictly defined along tubal orientation and it cannot simultaneously model spectral low-rankness in multiple orientations. To this end, we introduce two new tensor norms called OITNN-O and OITNN-L to exploit multi-orientational spectral low-rankness for an arbitrary K-way (K ≥ 3) tensors. We further formulate two RTD models via the proposed norms and develop two algorithms as the solutions. Theoretically, we establish non-asymptotic error bounds which can predict the scaling behavior of the estimation error. Experiments on real-world datasets demonstrate the superiority and effectiveness of the proposed norms. [ABSTRACT FROM AUTHOR]

In this article, the problem of a nonlinear system states estimation with multisensor stochastic scheduling is investigated. In order to solve the non-Gaussian property induced by the nonlinear transformation, the unscented transformation (UT) technique is applied. Since the sensor networks channel is limited, the stochastic event-triggered mechanisms (SETMs) are proposed to reduce the network transmission burden. Under the SETMs, the modified unscented Kalman filter is proposed. Additionally, the sufficient conditions are given to guarantee the stabilities of the error covariance and the estimation error. Finally, extensive examples are carried out. Performances evaluation and comparison with existing methods are given to demonstrate the superiority of the proposed methods. [ABSTRACT FROM AUTHOR]

Aims: Reliable estimation of the time spent in different glycaemic ranges (time‐in‐ranges) requires sufficiently long continuous glucose monitoring. In a 2019 paper (Battelino et al., Clinical targets for continuous glucose monitoring data interpretation: recommendations from the international consensus on time in range. Diabetes Care. 2019;42:1593‐1603), an international panel of experts suggested using a correlation‐based approach to obtain the minimum number of days for reliable time‐in‐ranges estimates. More recently (in Camerlingo et al., Design of clinical trials to assess diabetes treatment: minimum duration of continuous glucose monitoring data to estimate time‐in‐ranges with the desired precision. Diabetes Obes Metab. 2021;23:2446‐2454) we presented a mathematical equation linking the number of monitoring days to the uncertainty around time‐in‐ranges estimates. In this work, we compare these two approaches, mainly focusing on time spent in (70‐180) mg/dL range (TIR). Methods: The first 100 and 150 days of data were extracted from study A (148 subjects, ~180 days), and the first 100, 150, 200, 250 and 300 days of data from study B (45 subjects, ~365 days). For each of these data windows, the minimum monitoring duration was computed using correlation‐based and equation‐based approaches. The suggestions were compared for the windows of different durations extracted from the same study, and for the windows of equal duration extracted from different studies. Results: When changing the dataset duration, the correlation‐based approach produces inconsistent results, ranging from 23 to 64 days, for TIR. The equation‐based approach was found to be robust versus this issue, as it is affected only by the characteristics of the population being monitored. Indeed, to grant a confidence interval of 5% around TIR, it suggests 18 days for windows from study A, and 17 days for windows from study B. Similar considerations hold for other time‐in‐ranges. Conclusions: The equation‐based approach offers advantages for the design of clinical trials having time‐in‐ranges as final end points, with focus on trial duration. [ABSTRACT FROM AUTHOR]

Time estimates are an important part of many costing systems. As they may be subject to estimation error and hence result in measurement error in product and service costs, it is important to understand the sources of this estimation error as well as mechanisms to mitigate them. We performed a computer-based lab experiment to test the joint impact of task interruption and interactive time estimation on the accuracy of time estimates. As predicted, we find that the negative impact of task interruption on time estimation accuracy is mitigated when participants are allowed to interactively discuss their time estimates before making a final time estimate alone. We explain our findings through the underlying effects of cognitive load and confidence. Hence, managers may improve the accuracy of their time-based costing systems by enabling an interactive time estimation process to offset the detrimental effect of task interruption. [ABSTRACT FROM AUTHOR]

This brief presents a novel one-step adaptive parameter estimation framework for identification of unknown asymmetric dead-zone characteristic parameters (e.g., width and slopes) in the sandwich systems, which avoids using the intermediate variables or carrying out a two-step recursive identification procedure. By applying a continuous piecewise linear neural network (CPLNN), the asymmetric dead-zone nonlinearities can be represented into a parameterized form, where the dead-zone characteristic parameters can be derived based on the online updated CPLNN weights. Moreover, after representing the sandwich system into an augmented system, an adaptive observer is designed to reconstruct the immeasurable internal variables, and an adaptive law with guaranteed convergence is used to online update the CPLNN weights so as to estimate the dead-zone parameters. In this adaptive law, the parameter estimation error information is extracted using simple filters and introducing several auxiliary variables. It is proven that the observation error and estimation error both converge to a small set around zero. The efficiency of the estimation method is exemplified via practical experiments on an $X$ - $Y$ positioning platform. [ABSTRACT FROM AUTHOR]

This brief is about robust estimation for a sampled output system with continuous dynamics using unknown input observers (UIOs). A novel worst case estimation error bound is developed using a particular form of Gronwall inequality. The motivation is to assess the accuracy of the estimator in real-world conditions, considering slow sensor sampling times and possible corruption of the measurements due to faults. The theoretical results are applied for robust velocity estimation of a braking train when wheel jamming faults affect odometric sensors. Simulations and experimental data analysis are used to evaluate the proposed approach. [ABSTRACT FROM AUTHOR]

Beam tracking can be utilized to compensate for beam misalignment and avoid performance degradation in millimeter wave (mmWave) systems. In order to reduce the training overhead, attitude information obtained from motion sensors has been proposed to assist beam tracking. In this letter, we extend attitude information aided beam tracking to the practical systems with codebook-based beamforming. Moreover, estimation errors of attitude information are inevitable in practical systems, and lead to a trade-off in codebook design: wider beams suffer more from insufficient power; narrower beams are more sensitive to the error. Therefore, it is necessary to design a codebook with appropriate beamwidth. We first derive the optimal beamwidth that maximizes the average transmission rate based on the sectored beam model. Further, we consider the codebook with overlapping beams, which can prevent misalignment. Then, both the optimal beamwidth and overlap ratio are derived. Finally, simulation results validate the performance of the proposed methods. [ABSTRACT FROM AUTHOR]

We address the problem of determining optimal sensor precisions for estimating the states of linear time-varying discrete-time stochastic dynamical systems, with guaranteed bounds on the estimation errors. This is performed in the Kalman filtering framework, where the sensor precisions are treated as variables. They are determined by solving a constrained convex optimization problem, which guarantees the specified upper bound on the posterior error variance. Optimal sensor precisions are determined by minimizing the $l_1$ norm, which promotes sparseness in the solution and indirectly addresses the sensor selection problem. The theory is applied to realistic flight mechanics and astrodynamics problems to highlight its engineering value. These examples demonstrate the application of the presented theory to 1) determine redundant sensing architectures for linear time invariant systems, 2) accurately estimate states with low-cost sensors, and 3) optimally schedule sensors for linear time-varying systems. [ABSTRACT FROM AUTHOR]

The extended state observer (ESO) is used to estimate both the unmeasurable system states and acting lumped disturbance. Generally, high gains need to be selected in ESO to achieve fast convergence, which can make it sensitive to measurement noise. To address this limitation, a governing structure with a special combination of Kalman filter (KF) and ESO is proposed. The former serves as noise filtration while the latter is responsible for on-line reconstruction of states and disturbance. The two are connected as the system model used for the KF design contains a constantly updated estimate of the lumped disturbance obtained with the ESO. Finally, the resultant estimates are used to construct a composite disturbance rejection-based controller. To verify the new method, hardware experiments are performed on an electronic throttle system. Conducted quantitative comparison with conventional solution reveals advantages of the proposed approach in noise attenuation and control performance. [ABSTRACT FROM AUTHOR]

Performance achievable in sensorless control of electrical drives strictly depends on the adopted synchronous machine. The combination of cross-saturation and saliency, both dependent by the current load and the rotor position, makes always the position estimation afflicted by an estimation error. When the machine is highly saturated, the sensorless control can even diverge resulting in a useless drive. Thus, it is of primary importance to know in advance the convergence region of the sensorless drive, i.e., the operating points, where the motor can be successfully controlled without a position sensor. The aim of this article is to show how to compute the estimation error, the saliency, and the convergence region (hereafter called self-sensing capabilities) starting from the flux linkages maps or, more generally, from the incremental inductances. The dependence on the rotor position is also considered. Finite element simulations and experimental measurements validate the proposed model. The code used to compute the self-sensing capabilities is available as an open source package. [ABSTRACT FROM AUTHOR]

In this article, the robust control problem of uncertain nonlinear systems subject to magnitude and rate saturation (MRS) is investigated via active disturbance rejection control. To this purpose, we propose a novel robust controller that involves an improved extended state observer (IESO), a composite loop, and two anti-windup loops. The IESO is designed to estimate the state and the total disturbance, which includes the uncertain dynamics and external disturbance. Compared with the traditional ESO, IESO absorbs an anti-windup loop to ensure accurate estimation under MRS. The convergence analysis of IESO is further carried out to theoretically verify the accurate estimation of IESO. The composite loop is generalized to cancel the total disturbance based on the output of IESO. Moreover, a rate anti-windup loop is designed to attenuate the effect of rate saturation on the estimation/cancellation of total disturbance. An algorithm for anti-windup gains computation is correspondingly established, in order to minimize the effect of external disturbance in terms of $L_{2}$ gain while guaranteeing the local asymptotic stability. Finally, the proposed method is applied to aeroengine, which involves large uncertainty and suffers from disturbance and MRS. The experimental results verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The main objective of this research is to propose a novel machine learning based fault detection algorithm for a nonlinear switched continuous system (NSCS) with time delayed measurements. A simulation model has been established through the analysis of the relevant constraints such as estimation errors derived from self-switched time- delayed (TD) Unscented Kalman Filter (UKF). Machine learning techniques, specifically probabilistic principal components analysis (PPCA) and the support vector machine (SVM) have been successfully adopted on the database of the proposed model to detect and isolate the faults. ‘Three tank system’, the commonly used bench mark problem, has been re-employed here to prove the efficiency of the proposed estimation and fault detection algorithm. In this work, it is conjectured that the available measurements are time delayed. Through this study, the efficacy of the proposed time- delayed Unscented Kalman Filter (TD-UKF) and Machine Learning based fault detection have been demonstrated on the afore mentioned benchmark problem. The results indicates that Machine learning based algorithm can detect the fault with more accuracy and less false alarm than other statistical methods. The finding of this research can be used for any type of industrial system to detect the faults with better potential and higher range coverage. [ABSTRACT FROM AUTHOR]

In this letter, we present a sharp algorithmic analysis for alternating projected gradient descent which is used to solve the covariate adjusted precision matrix estimation problem in high-dimensional settings. By introducing a new analytical tool (the generic chaining), we remove the impractical resampling assumption used in the literature. The new analysis also demonstrates that this algorithm not only enjoys a linear convergence rate in the absence of convexity, but also attains the minimax rate with optimal order of sample complexity. Our results, meanwhile, reveal a time-data tradeoff in this problem. Numerical experiments are provided to verify our theoretical results. [ABSTRACT FROM AUTHOR]

This article is concerned with the fuzzy functional observer-based finite-time adaptive sliding-mode control for nonlinear systems with the partly unmeasurable states and some matched uncertainties. First, considering that the upper bound of the uncertain function exists but unknown, a fuzzy functional observer (FFO) with an adaptive compensator is constructed. Second, an FFO-based fuzzy integral sliding mode controller (ISMCr) is designed such that the closed-loop fuzzy systems are finite-time bounded with $H_{\infty }$ performance over the reaching phase, the sliding phase, and the whole finite-time interval, respectively. To reduce the conservatism and increase the solution space of linear matrix inequality conditions, the fuzzy Lyapunov functional approach and equivalent fuzzy relaxed matrices technique are developed by introducing some relaxed matrices in the derivative of the fuzzy normalized membership function. Compared with the common Luenberger-type observer-based approach, the gain matrices of ISMCr depend on the FFO designed, which also enhances the flexibility of controller design. Finally, a simulation example with some comparison is given to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Machine learning techniques have been widely used in communication systems because of their superior performance. Among them, the classical machine learning algorithm Principal Component Analysis (PCA) aims to estimate the principal subspace of the received signals, and thus is also known as subspace estimation. It is often used in Direction of Arrival (DoA) tasks. Recently, the widespread deployment of networks has motivated researchers to solve the problem of subspace estimation in decentralized settings. An important concern in the design of decentralized algorithms is the communication complexity, because communication consumes much energy, while the sensors in the network are generally energy-limited. Specifically, communication complexity refers to the minimum amount of transmitted variables to obtain an estimate with an error smaller than $\varepsilon$. For existing algorithms, this complexity is $O(\log ^{2}(1/\varepsilon))$. In this paper, we improve this complexity to be $O(\log (1/\varepsilon))$ by designing a new algorithm named Decentralized Subspace Estimation (DSE). Such improvement is achieved by using the gradient tracking technique. We theoretically analyze the influence of the network connectivity and the eigen-gap of the data on communication complexity. In addition, we use DSE to solve the DoA estimation problem. Experiments verify the effectiveness of the algorithm and the correctness of the theoretical result. [ABSTRACT FROM AUTHOR]

Purpose: Estimated fetal weight (EFW) is crucial for clinical decision-making during pregnancy and labor. Maternal habitus impacts its accuracy. This study compared the accuracy of clinical versus ultrasound EFW in tall pregnant women (height ≥ 172 cm, 90th percentile). Methods: In this prospective study, tall pregnant women at term, who arrived for a prenatal visit and delivered within a week, underwent clinical and ultrasound assessments of estimated fetal weight. Each woman served as her own control. After delivery, birth weight was compared to the clinical and ultrasound EFW. The primary outcome was the accuracy of each method in predicting the actual birth weight. Results: All 100 women included in this trial underwent clinical and ultrasound estimations of fetal weight. Mean maternal height was 175.7 ± 3.3 (172–185) cm. More clinical EFW swere inaccurate compared to ultrasound (25 (25%) vs. 6 (6%), respectively, p < 0.001). Both clinical (3583 g) and ultrasound (3490 g) evaluations underestimated the fetal weights compared to the birth weights (3664 g, p < 0.001). In the macrosomic fetal group, both the clinical (3983 g) and ultrasound (3767 g) estimates were significantly inaccurate compared to the birth weights (4237 g, p < 0.001). Conclusion: Among tall women, ultrasound EFW is more accurate than clinical EFW. Trial registration number and date of registration: IRB-0016-17-MMC, Clinical-Trials.gov identifier NCT03206281. [ABSTRACT FROM AUTHOR]

We propose a fast Newton algorithm for ℓ 0 regularized high-dimensional generalized linear models based on support detection and root finding. We refer to the proposed method as GSDAR. GSDAR is developed based on the KKT conditions for ℓ 0 -penalized maximum likelihood estimators and generates a sequence of solutions of the KKT system iteratively. We show that GSDAR can be equivalently formulated as a generalized Newton algorithm. Under a restricted invertibility condition on the likelihood function and a sparsity condition on the regression coefficient, we establish an explicit upper bound on the estimation errors of the solution sequence generated by GSDAR in supremum norm and show that it achieves the optimal order in finite iterations with high probability. Moreover, we show that the oracle estimator can be recovered with high probability if the target signal is above the detectable level. These results directly concern the solution sequence generated from the GSDAR algorithm, instead of a theoretically defined global solution. We conduct simulations and real data analysis to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a convex optimization problem, based on which the optimal strategy of false data injection (FDI) attacks is obtained to intrude machine-type-communications (MTC) networks from the perspective of an attacker, aiming to seek effective defensive measures based on a good understanding of attackers’ behaviour. We consider a target tracking example, which is a typical application of MTC networks. Specifically, as a type of MTC devices, smart sensors each have the ability of perception, calculation and communication and all of them can form a sensor network. In this network, its sensors and transmission channels are vulnerable to FDI attacks, resulting in the degradation of system estimation performance. In order to maximize the estimation error covariance of MTC network, the attacker needs to decide which sensors and channels to intrude due to limited energy budget. The estimation error covariance of the MTC network is calculated, based on which a convex optimization problem to obtain the optimal attack strategy is proposed. Simulation results demonstrate that the optimal attack strategy maximizes the transient mean-square deviation and estimation error covariance of the MTC network. [ABSTRACT FROM AUTHOR]

A fixed-time control approach is investigated for the leader–follower formation tracking issue of wheeled mobile robots (WMRs) with prescribed performance in this brief. The nonlinear and underactuated model of the WMR is first transformed into a linear one with uncertainties and external disturbances. Then, a time-varying fixed-time extended state observer is developed to observe the total disturbance. Furthermore, a time-varying fixed-time distributed observer is constructed to estimate the state of the leader. Finally, a novel fixed-time prescribed performance strategy is presented to achieve the formation tracking. Experimental results demonstrate the validation of the proposed method. [ABSTRACT FROM AUTHOR]

Although optimal control with full state-feedback has been well studied, online solving output-feedback optimal control problem is difficult, in particular for learning online Nash equilibrium solution of the continuous-time (CT) two-player zero-sum differential games. For this purpose, we propose an adaptive learning algorithm to address this trick problem. A modified game algebraic Riccati equation (MGARE) is derived by tailoring its state-feedback control counterpart. An adaptive online learning method is proposed to approximate the solution to the MGARE through online data, where two operations (i.e., vectorization and Kronecker’s product) can be adopted to reconstruct the MGARE. Only system output information is needed to implement developed learning algorithm. Simulation results are carried out to exemplify the proposed control and learning method. [ABSTRACT FROM AUTHOR]

In this brief, the distributed state and parameter estimation problem is addressed for a class of discrete-time nonlinear systems subject to sensor saturations over wireless sensor networks. In this regard, a novel estimation design is proposed that involves identification of an unknown parameter under event-triggering mechanism. A sufficient condition for exponential uniform ultimate boundedness of estimation error is established using Lyapunov stability theory. The estimator gains to ensure boundedness of error are linked to the solution of inequality constraints. Finally, the effectiveness of proposed estimator is demonstrated using a simulation example. [ABSTRACT FROM AUTHOR]

This brief studies the simultaneous estimation of states and uncertainties in general continuous-time systems. In particular, we present a novel time-varying learning intensity (TLI) learning observer (LO). It has the advantage of inheriting the valuable properties of conventional LOs with a simple structure, i.e., the uncertainty estimation is achieved using simply one algebraic equation with low computational costs. The foremost difference in comparison with conventional LOs is the utilization of the TLI approach, which attenuates the overshooting response in the case of large estimation errors and obtains decent performance improvement. Simulations for constant and time-varying signals demonstrate a notable performance boost of TLI-LO. [ABSTRACT FROM AUTHOR]

The finite-time output regulation (FTOR) problem for linear heterogeneous multi-agent systems (MAS) is investigated in this brief. The objective of this brief is to design controllers such that each agent can accurately track the reference signal and reject the disturbance. The disturbance and reference signal are generated by an exosystem. It is assumed that only a part of agents can obtain the state and system matrix of the exosystem. Then, distributed fixed-time observers are firstly proposed to estimate them, which can ensure the fixed-time convergence of the estimation errors. Next, we design both state feedback controller and output feedback controller to solve the FTOR problem using feedforward method. By virtue of Lyapunov theory and output regulation technique, it is shown that the proposed control strategy can solve the FTOR problem. Finally, a numerical example is included for illustration. [ABSTRACT FROM AUTHOR]

In this letter, we investigate the security issue of a discrete linear time-varying system following a fixed switching rule. We first give a sufficient condition for the stability of this two-state switched system. In our scenario, the communication channel between the sensor and the remote estimator is interfered by a jammer with limited energy. Then we prove that the continuous jamming strategy is optimal compared with other strategies, which maximizes the estimation error in the viewpoint of the attacker. Finally, numerical examples are presented to certificate the effectiveness of the proposed optimal jamming strategy. [ABSTRACT FROM AUTHOR]

This article proposes a compensation method of position estimation error for high-speed surface-mounted permanent magnet synchronous motors based on robust inductance estimation. The proposed method relies on the variation of the estimated $\delta$ -axis back back-electromotive force when a small current is injected into the $\gamma$ axis. The inductance estimation error is limited within ${\bf \pm \!5\%}$ when the nominal resistance and inductance vary $\bf \pm 30\%$ of their real values. With the estimated inductance, the position estimation error can be well compensated. Compared with the conventional current-injection method, the proposed method has enhanced robustness against the system noises. Benefiting from this, it is effective to estimate the inductance with a small injected current ($ \bf 0.5\%$ of the rated current), where the conventional methods fail. Finally, the effectiveness of the proposed method is validated by simulation and experiment results on a 100 kr/min (1.67 kHz) high-speed permanent magnet synchronous machines accurately with 10-kHz sampling frequency. [ABSTRACT FROM AUTHOR]

The measurement of liquidity is the basis of research in market microstructure studies. Based on the intraday tick trading data on Chinese stock markets from 2009 to 2016, we run horseraces of monthly estimates of newly and widely employed low-frequency liquidity proxies in the literature against three types of bid-ask spread high-frequency benchmarks. The empirical results reveal that the closing percent quoted spread estimator has the smallest estimation error, and the FHT estimator has the highest correlation. Moreover, these two estimators win the majority of horseraces in terms of estimation error and correlation comparison with the high-frequency benchmarks. Meanwhile, we find that most liquidity estimators based on Roll's model do not perform well. Because the performance metrics of estimation precision or correlation performance on related liquidity issues differ depending on the type of research, our study offers appropriate liquidity measures for different research purposes. [ABSTRACT FROM AUTHOR]

A speed observer for field-oriented controlled induction drives is presented. The proposed solution exploits the concept of the classic full-order flux observer which is adaptive to the rotor resistance variations. Due to the structural similarity of the rotor speed and active rotor resistance in motor model, the speed observer can be designed using similar structure. The observer guarantees local asymptotic estimation of the rotor fluxes, stator currents and rotor speed in all operating conditions, excluding DC stator excitation. The stability properties of the observer are studied using Lyapunov’s second method. It is experimentally shown that the observer provides an acceptable dynamics of the speed estimation if speed is slowly varying. The proposed observer is suitable for the applications with medium requirements for speed regulation performance. References 13, figures 5, table 1. [ABSTRACT FROM AUTHOR]

In this article, a new scheme for range-based localization is proposed. The main goal is to estimate the position of a mobile point based on distance measurements from fixed devices, called anchors, and on inertial measurements. Due to the nonlinear nature of the problem, an analytic relation to compute the position starting from these measurements does not exist, and often trilateration methods are used, generally based on least-square algorithms. The proposed scheme is based on the modeling of the localization process as a parallel robot, thereby methodologies and control algorithms used in the robotic area can be exploited. In particular, a closed-loop control system is designed for tracking the position of a mobile point based on range measurements from fixed anchors, and it is shown a peculiar structure of the tracking error dynamics, whose allows an intuitive gain tuning and ensures global exponential stability. Moreover, it is also shown a nice connection between tuning parameters and rate of convergence of the estimation error. Experimental results confirm the validity of the proposed localization method. [ABSTRACT FROM AUTHOR]

In this letter we derive an optimal time-varying smoothing factor for smoothing non-stationary power spectra of a target signal when a noisy observation of this signal is given. The proposed approach is based on a complex Gaussian signal model in the short-time discrete Fourier domain and on mean squared error optimization. We find that the optimal smoothing factor depends on the signal-to-noise ratio as well as on the deviation between the smoothed estimate and the target signal power spectra. We investigate the properties of the proposed adaptive smoothing system and demonstrate its utility in a basic validation experiment. [ABSTRACT FROM AUTHOR]

We consider the scenario where two pursuers are required to intercept an enemy evader that launches a decoy. Under the assumption that all four aircrafts have first-order linear dynamic characteristics, a cooperative guidance law is designed in two stages: the unidentified decoy stage and the discriminated decoy stage. In the unidentified decoy stage, the predictive guidance law is designed based on the method of the highest probability interval (HPI). This guidance law can maximize the probability of intercepting the real target despite the decoy's presence, thus providing an interception maneuver advantage for later cooperative guidance to the real target. In the discriminated decoy stage, based on the optimal control theory, an optimal guidance law considering the detection configuration is designed, which reduces the estimation error in the detection process and improves the interception performance. Simulation results verify the feasibility of the phased cooperative guidance law.

At present, many enterprises provide users with better services by collecting their sensitive information. However, these enterprises will inevitably cause the leakage of users' information, thereby infringing on users' privacy. Local differential privacy resolves this problem by only aggregating randomized values from each user, providing plausible deniability. However, different users might have diverse privacy requirements for different attributes. Moreover, the dimensions of these attributes may be unbalanced. Traditional local differential privacy algorithms usually assign the same privacy budget to all attributes, resulting in undesired frequency estimation. To obtain highly accurate of the results while satisfying local differential privacy, the aggregator needs to implement a reasonable privacy budget allocation scheme. Motivated by this, this paper proposed a novel local differential privacy scheme. The proposed method combines the advantages of BRR and MRR to address the problem of high and low privacy requirements. It employs the Lagrange multiplier algorithm to transform the privacy budget allocation problem between unbalanced attributes into a problem of calculating minima from unconditionally constrained convex functions. The solution to the resulting nonlinear equation is used as the final privacy budget allocation scheme. Simulation experiments show that the novel local differential privacy scheme proposed by this paper can significantly reduce the estimation error under the premise of satisfying the local differential privacy.

Integrated sensing and communications (ISAC) is emerging as a key technology of 6G. Owing to the low peak-to-average power ratio (PAPR) property, discrete Fourier transform spread orthogonal frequency-division multiplexing (DFT-s-OFDM) is helpful to improve the sensing range and suitable for high-frequency transmission. However, compared to orthogonal frequency-division multiplexing (OFDM), the sensing accuracy of DFT-s-OFDM is relatively poor. In this paper, frequency-domain spectral shaping (FDSS) is adopted to enhance the performances of DFT-s-OFDM including sensing accuracy and PAPR by adjusting the correlation of signals. Specifically, we first establish a signal model for the ISAC system, followed by the description of performance indicators. Then, we analyze the influence of amplitude fluctuation of frequency domain signals on sensing performance, which shows the design idea of FDSS-enhanced DFT-s-OFDM. Further, a FDSS-enhanced DFT-s-OFDM framework is introduced for ISAC, where two types of FDSS filters including a pre-equalization filter and an isotropic orthogonal transform algorithm (IOTA) filter are designed. The simulation results show that the proposed scheme can obtain about 4 dB performance gain in terms of sensing accuracy over DFT-s-OFDM. In addition, FDSS-enhanced DFT-s-OFDM can significantly reduce PAPR and improve the power amplifier efficiency.

Current responses based self-sensing control estimates rotor position by detecting magnetic saliency of a machine. However, the estimation error can be resulted from several nonideal factors, especially magnetic cross-saturation, as effects of these factors show up in current responses. This article applies image registration technique to improve the accuracy of a self-sensing estimation for model predictive control (MPC) interior permanent magnet synchronous machines (IPMSM) drives by minimizing impacts of nonideal factors on measured current responses. In commissioning process, two images are generated. The inverter voltage vectors in MPC excite current responses, represented by a set of vectors that generate a response image. Nonideal factors that result in estimation errors also deform the response image. A primitive image based on resolver output can be considered as a template. An image registration technique is investigated to register deformed images with corresponding primitive images, and transformations contain operations of rotation, scale, translation, and shear can be derived. During the drive, the deformed pixel points are online mapped to new pixel points by applying these operations. The current responses after transformation are less subject to nonideal factors. As a result, impacts of these deformation factors that contribute to estimation errors can be reduced. Finally, the application of image registration technique to the self-sensing estimation is verified on a 3.7-kW IPMSM drive. [ABSTRACT FROM AUTHOR]

A Partial Discharge (PD) monitoring system for Air Insulated Substations (AIS) has been developed for monitoring of partial discharge activities of various insulators and insulating media that are distributed over an electrical substation. The proposed system utilizes four UHF (ultra-high frequency) sensors, which are to be placed around the area of interest so that any impulsive UHF signal originated from PD activity can be captured and subsequently processed for detection and localization of multiple PD sources. To improve the localization accuracy and to improve the detection specificity, i.e., to eliminate false impulsive UHF signals originated from other sources, two novel approaches, namely, Nearest-Neighbour and Probabilistic Grid-Search are proposed, which utilize the physical layout of the AIS to identify most probable PD source locations. The proposed algorithm can accurately find the PD source locations even in the presence of high time-delay-of-arrival (TDOA) estimation error by making necessary corrections in the PD source location estimation. The proposed algorithm has been field trialed in a medium-size distribution substation.  A detailed comparative study with the conventionally used PD source localization approach in different extreme situations has been performed with the help of simulation, laboratory as well as field-based testing. The obtained results are worthwhile for supporting the widespread implementation of the proposed method. [ABSTRACT FROM AUTHOR]

This article investigates the problem of state estimation for discrete-time systems with a Markov driven transmission strategy. A buffer with limited capacity is used to store the latest measurements, and they are transmitted simultaneously once the system accesses to the shared channel. A buffer-dependent smart estimator is then proposed to process the received measurements. A convex sufficient condition concerning the exponential mean-square stability and the $l_{2}-l_{\infty }$ performance is established for the estimation error system to design the estimator gains. Finally, two examples are presented to illustrate the effectiveness of the derived result under different conditions. [ABSTRACT FROM AUTHOR]