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In this article, we consider the equations of motion for an articulated n$$ n $$‐trailer vehicle with different masses and inertias in the presence of force and torque commands. We design a second‐order optimal filter to estimate the pose of the first vehicle and of the trailers exploiting the Lie group structure and the nonholonomic and hooking constraints. We consider the filtering problem from three different perspectives: in the first masses and inertias are time‐varying and perfectively known, in the second they are known only in the first part of the maneuver, but they are not updated over time. In the last case masses and inertias are considered within the state vector and therefore estimated by the filter. Depending on the needs in real‐life applications (e.g., whether the masses and inertias remain fixed or change during the maneuver and are unknown), the best‐performing filter can be used. The sensing system consists of a GPS‐like configuration (global positioning system) obtained by using an antenna attached to the leading car. [ABSTRACT FROM AUTHOR]

In research with event‐related potentials (ERPs), aggressive filters can substantially improve the signal‐to‐noise ratio and maximize statistical power, but they can also produce significant waveform distortion. Although this tradeoff has been well documented, the field lacks recommendations for filter cutoffs that quantitatively address both of these competing considerations. To fill this gap, we quantified the effects of a broad range of low‐pass filter and high‐pass filter cutoffs for seven common ERP components (P3b, N400, N170, N2pc, mismatch negativity, error‐related negativity, and lateralized readiness potential) recorded from a set of neurotypical young adults. We also examined four common scoring methods (mean amplitude, peak amplitude, peak latency, and 50% area latency). For each combination of component and scoring methods, we quantified the effects of filtering on data quality (noise level and signal‐to‐noise ratio) and waveform distortion. This led to recommendations for optimal low‐pass and high‐pass filter cutoffs. We repeated the analyses after adding artificial noise to provide recommendations for data sets with moderately greater noise levels. For researchers who are analyzing data with similar ERP components, noise levels, and participant populations, using the recommended filter settings should lead to improved data quality and statistical power without creating problematic waveform distortion. Filtering can have a large impact on ERP data, influencing both statistical power and the validity of conclusions. However, the field lacks recommended filter cutoffs for cognitive and affective ERP components that are quantitatively justified and empirically validated. Here, we provide recommended filter cutoffs for a broad range of ERP components and scoring methods. [ABSTRACT FROM AUTHOR]

Summary: Data losses and noises in both forward and feedback channels significantly impact the convergence of networked iterative learning control (ILC) systems. To address this issue, this article considers a class of linear time‐invariant objects controlled by proportional ILC controllers, an optimal state filter is then designed at the ILC controller side that aims to guarantee the convergence of the input transmitted by ILC controllers. First, two data transmission processes are introduced to account for the effects of data losses and noises. Second, a filtering model is established utilizing only the object information and the aforementioned data transmission processes. Third, the optimal state filter is designed on the basis of the orthogonal projection principle. This filtered state facilitates the acquisition of actual output errors, thus improving the convergence of the input transmitted by ILC controllers. Simulation results demonstrate the effectiveness of the proposed state filters. [ABSTRACT FROM AUTHOR]

Subject and Purpose. Computer simulation methods are used for investigating the errors that arise in the course of retrieval, by means of an improved photoclinometry technique, of planetary surface reliefs from sets of their photo images. The work has been aimed at evaluating the level of errors in numerically calculated heights and slopes of the reliefs, as retrieved from images with a variety of signal-to-noise ratios, also including estimates for possibly minimal errors. Methods and Methodology. The improved photoclinometry approach permits calculating the most probable relief realizations for parts of a planetary surface, proceeding from sets of their photographic images. Two optional ways for implementing the method are analyzed, namely application of an optimized Fourier transform-based filtering, or solution of Poisson’s equation within the finite-difference technique. Results.Computer experiments have demonstrated that the reliefs retrievable from photo images with the use of the improved photoclinometry methods are always qualitatively similar to real ones. In the case of calculations within the finite-difference method the level of errors in height determination made 0.21s0 to 0.27s0, where s0 stands for the root-mean-square deviation in the height of the relief being modeled. In the case of application of the Fourier analysis-based method the level of errors in the calculated heights varied between 0.86s0 and 0.33s0, while the signal-to-noise ratio for the initial images changed from 1.0 to 100. Within this version of the method the theoretical prediction for the lowest error in the calculated height varied from 0.83s0 to 0.13s0. The relief belonging to the middle portion of the area under study is always retrievable to a better accuracy, as compared with the sites adjacent to the image borders, no matter which of the two available techniques has been applied. Conclusions.The improved photoclinometry method allows retrieving surface reliefs from sets of their images, with error levels for estimates of height equaling either 0.21s0 to 0.27s0 (in the case of application of the finite difference computational technique), or 0.33s0 (if the Fourier analysis has been applied, with the signal-to-noise ratio SNR=50). It is recommended that relief retrieval were performed over sites of a larger surface area than might be strictly necessary for the purpose, since the error value estimated for the middle part of the site always turns out to be several times smaller than the error calculated over the entire area under study.

Superconducting transition-edge sensor (TES)-based single-photon detectors with high detection efficiency, low dark count rate and photon-number resolving capability are suitable for many scientific applications. We have developed a high-performance Ti TES-based single-photon detector at a working wavelength of about 1550 nm. The TES with a critical temperature of around 200 mK has an active area of 10 μm × 10 μm, which is manually aligned to an ultra-high numerical aperture fiber with a mode field diameter of 4 μm, leading to a nearly perfect coupling efficiency. The Ti TES integrated into an optical cavity consisting of a dielectric mirror and an anti-reflection coating exhibits an absorption efficiency of 97% at 1550 nm. The fabricated TES shows an energy resolution of 0.21 eV using a 1540 nm pulsed source, and it can resolve up to 20 photons. The measured system detection efficiency is 95% ± 0.4%. [ABSTRACT FROM AUTHOR]

In passive target tracking, target motion parameters (TMP) i.e. range, course, and speed are estimated using bearing measurements. In the simulation mode, the accuracy of the estimated solution can be calculated, as true values are available. In the actual scenario, the true values of TMP are not readily obtainable. In this research, the focus is laid on the evolution of the acceptance criterion of the TMP in the actual scenario. Detailed mathematical modeling is carried out and an unscented Kalman filter (UKF) is used to estimate TMP. The state vector covariance matrix of UKF is utilized to express the errors in estimated TMP and an acceptance criterion is derived in terms of the standard deviation of errors in estimated TMP. Submarine to submarine tracking is carried out and scenarios are chosen accordingly. Several low angle on target bow (ATB) scenarios are used in the Monte-Carlo simulation and the results are presented. A similar procedure can be carried out for medium and high ATB scenarios and a submarine to ship or vice-versa passive target tracking. [ABSTRACT FROM AUTHOR]

Optimal filtering is the crucial technique for the data analysis of transition-edge-sensor (TES) calorimeters to achieve their state-of-the-art energy resolutions. Filtering out the 'bad' data from the dataset is important because it otherwise leads to the degradation of energy resolutions, while it is not a trivial task. We propose a neural network-based technique for the automatic goodness tagging of TES pulses, which is fast and automatic and does not require bad data for training. [ABSTRACT FROM AUTHOR]

We propose two techniques for the fast numerical implementation of a random vector transform within Wiener filtering paradigm. In signal processing terminology, the transform is interpreted as an optimal filter. The proposed transform aims to minimize the sum of distances between a reference random vector x i and its transformed counterpart y i , for all i = 1 , ... , p. In contrast to known techniques where the transform consists of p matrices that minimize a difference between a single reference random vector and p observed random vectors, the proposed transform uses a single optimal matrix to transform p random vectors. It requires less computational work for the numerical implementation. Nevertheless, a direct numerical realization of the transform involves computation of the covariance matrix pseudo-inverse and the matrix square root which might be large, and then are computationally expensive. Proposed fast versions of the transform allow us to avoid that computation and, as a result, accelerate the numerical implementation. While the fast proposed techniques are approximate versions of the original transform, it is shown that their accuracies are very close to those of the original transform. An error analysis is provided. The effectiveness of the proposed transform is illustrated by numerical experiments in four real-world scenarios. [ABSTRACT FROM AUTHOR]

In this paper, we shall discuss the convergence of the continuous-discrete feedback particle filter (FPF) proposed in Yang et al. (2014). The FPF is an interacting system of N particles where the interaction is designed such that the empirical distribution of the particles approximates the posterior distribution by an innovation error-based feedback control structure. Under some assumptions, it is proved that, for a class of functions ϕ and ∀ p ≥ 2 , the estimate of ϕ (X t n ) by FPF converges to its optimal estimate E [ ϕ (X t n ) | F t n ] in L p sense, as the number of particles goes to infinity and the numerical approximation error of computing the control input U goes to zero. Furthermore, the bound of the estimation error is also delicately analyzed. [ABSTRACT FROM AUTHOR]

In this paper, we investigate a linear-quadratic (LQ) optimal control problem for partially observed forward-backward stochastic systems with random jumps, where the observation’s drift term is linear with respect to the state x and control variable v. In our model, the observation process is no longer a Brownian motion but a controlled stochastic process driven by Brownian motions and Poisson random measures, which also have correlated noises with the state equation. Applying a backward separation approach to decompose the state and observation, we overcome the problem of cyclic dependence of control and observation. Then, the necessary and sufficient conditions for optimal control are derived. We also obtain the feedback representation of optimal control and provide two special cases to illustrate the significance of our results. Moreover, we also provide a financial application to demonstrate the practical significance of our results. [ABSTRACT FROM AUTHOR]

This article is concerned with the convergence performance of wireless networked iterative learning control (ILC) systems with data dropouts and channel noises in both sensor‐to‐controller and controller‐to‐actuator channels. In order to improve the convergence performance of such ILC systems, an optimal input filter is developed at the actuator side to estimate controller updated input with the effect of these network uncertainties. Specifically, a filtering model is constructed only by using the learning process of P‐type controllers and the transmission process of both measured output data and updated input data. On the basis of this model and the orthogonality principle, the filter in the sense of linear minimum variance is designed at the actuator side so that the controller updated input could be estimated in iteration domain. The convergence performance of filtering error covariance matrix is analyzed theoretically. Moreover, because the filter design does not employ plant information, the tracking performance of any plant with P‐type learning controllers can be improved by driving with the filtered input. Simulation results show that the proposed filtering method is effective. [ABSTRACT FROM AUTHOR]

Purpose: The paper discusses the possibility for increasing the planet’s surface relief retrieving accuracy with the improved photoclinometry method through the reference of the desired relief to the altimetry data. The general approach to solving the problem is proposed. The use of altimeters having both wide and narrow beam patterns are discussed, but the narrow beam pattern altimeter data is studied more in detail. The spatial resolution of the retrieved relief calculated with the improved photoclinometry method conforms to the one of the source images. Altimetry allows absolute reference to the surface heights and improves the accuracy of the relief determination. Design/metodology/approach: The work is based on the improved photoclinometry method for the planet’s surface relief retrieving from images. This method is mathematically rigorous and uses the Bayesian statistical approach, that allows calculation of the most probable relief according to available observations. Findings: An approach to determining the optimal statistical estimate of the surface heights from images in the frames of the improved photoclinometry method is proposed and an expression for the optimal filter which converts source images along with the wide beam pattern altimetry data into the most probable relief of the planet surface area is presented. The reference technique for the narrow beam pattern altimeter data is formulated. The efficiency of the method has been verified with the computer simulation. The relief of the surface area in Mare Imbrium on the Moon was retrieved using three images and laser altimeter data taken by the “Lunar Reconnaissance Orbiter” spacecraft. Conclusions: Accounting for the narrow beam pattern altimeter data increases the accuracy of the relief determination. Using the narrow beam pattern altimeter data turns out to be more preferable over the involving wide beam pattern altimeter data. Computer simulation has shown that accounting for the narrow beam pattern altimeter data significantly increases the accuracy of the calculated heights as against using images exclusively and helps to speed up the calculation procedure.

The convergence performance of wireless networked iterative learning control systems is affected by additive noises, random delays, and data dropouts in both sensor‐to‐controller and controller‐to‐actuator channels. In order to guarantee the convergence performance of such systems, an input filter is designed in front of actuators to estimate the controller updated input under the effect of those uncertainties. Specifically, a P‐type learning controller is considered firstly, and then a model is developed to describe the transmission of sensor measured output data and controller updated input data under the mixed effect of those uncertainties. On the basis of state augmentation, the two developed data transmission processes are further combined with the controller learning process to build a unified filtering model. According to this filtering model and the projection theory, an optimal filter in linear minimum variance sense is designed at the actuator side to estimate the controller updated input in iteration domain. The convergence performance of the norm of filtering error covariance matrix is proved theoretically, which means driven by this accurately estimated input, the convergence performance of networked systems adopting the P‐type learning controller is guaranteed. Finally, some numerical results are given to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this article, we consider the issue of energy efficient management for distributed state estimation over unknown packet-dropping communication channels which are exposed to Denial-of-Service (DoS) attacks. After a packet is transmitted, the sensor determines whether to retransmit and the retransmission power levels based on the feedback information from the receiving sensor. We formulate the energy efficient management problem as a modified Markov decision problem (MMDP), and proposed an algorithm to seek a retransmission power schedule that achieves optimal tradeoff between energy consumption and communication rate for given channel noise power. In the case of unknown channel state, we design a statistical learning method to estimate channel noise power. Besides, inspired by the hypothesis testing, we design an anomaly detector to guarantee the instantaneity of channel noise power estimation and efficiency of energy management. Furthermore, we derive the optimal gain for each estimator, and provide a sufficient condition for the stability of estimator under the energy management. Finally, we verify the main results by numerical examples. [ABSTRACT FROM AUTHOR]

We investigate quantum correlations appearing for two-qubit detectors which are initially uncorrelated and locally coupled to a massless scalar field in a vacuum state. Under the perturbation up to the second order in the coupling, the state of the detectors can be entangled through the interaction with the scalar field but satisfies the Bell-CHSH inequality. The violation of the Bell-CHSH inequality for such an entangled state is revealed by local filtering operations. In this paper, we construct the optimal filtering operations for the qubit detectors and derive the success probability of the filtering. The success probability characterizes the reliability of revealing the violation of the Bell-CHSH inequality by the filtering operations. Through these analyses, we demonstrate a trade-off relation between the success probability and the size of parameter region showing the violation of the Bell-CHSH inequality.

Background. The presence of a noise component on 3D images of single-photon emission computed tomo­graphy (SPECT) of a brain significantly distorts the probability distribution function (PD) of the radioactive count rate in the images. The presence of noise and further filtering of the data, based on a subjective assessment of image quality, have a significant impact on the calculation of volumetric cerebral blood flow and the values of the uptake asymmetry of the radiopharmaceutical in a brain. Objective. We are aimed to develop a method for optimal SPECT filtering of brain images with lipophilic radiopharmaceuticals, based on a Gaussian filter (GF), for subsequent image segmentation by the threshold method. Methods. SPECT images of the water phantom and the brain of patients with 99mTc-HMPAO were used. We have developed a technique for artificial addition of speckle noise to conditionally flawless data in order to determine the optimal parameters for smoothing SPECT, based on a GF. The quantitative criterion for optimal smoothing was the standard deviation between the PD of radioactive count rate of the smoothed image and conditionally ideal one. Results. It was shown that the maximum radioactive count rate of the SPECT image has an extremum by changing the standard deviation of the GF in the range of 0.3–0.4 pixels. The greater the noise component in the SPECT image, the more quasi-linearly the corresponding rate changes. This dependence allows determining the optimal smoothing parameters. The application of the developed smoothing technique allows restoring the probability distribution function of the radioactive count rate (distribution histogram) with an accuracy up to 5–10%. This provides the possibility to standardize SPECT images of brain. Conclusions. The research results of work solve a specific applied problem: restoration of the histogram of a radiopharmaceuticals distribution in a brain for correct quantitative assessment of regional cerebral blood flow. In contrast to the well-known publications on the filtration of SPECT data, the work takes into account that the initial tomographic data are 3D, rather than 2D slices, and contain not only uniform random Gaussian noise, but also a pronounced speckle component.

Subject and purpose. The paper discusses the extension of the photometric method for the surface relief retrieval from images in the optical and radar wavelengths ranges through the involving of the direct heights measurements by an altimeter onboard the spacecraft. The photometric method allows retrieval the relief which has accuracy and the spatial resolution inherent in initial images. Altimetry provides direct measurements of the surface heights, but to cover the entire study area it requires too many spacecraft passes. The paper considers an altimeter having a wide radiation pattern. Methods and methodology. The paper presents the development of the photometric method for the surface relief retrieval. The main characteristic of the method is the Bayesian statistical approach which allows calculations of the most probable relief from available observational data with mathematical rigour. Results. An expression for the optimal filter which converts source images along with altimetry data into the most probable relief is derived. The efficiency of the method is verified using computer modeling. Conclusion. The statistical approach to processing of experimental data allows optimal combination of surface images and altimeter data properties to obtain the surface relief with high spatial resolution and reference to real heights.

Obstructive sleep apnea is a common sleep disorder with a high prevalence and often accompanied by significant snoring activity. To diagnose this condition, polysomnography is the standard method, where a neck microphone could be added to record tracheal sounds. These can then be used to study the characteristics of breathing, snoring or apnea. In addition cardiac sounds, also present in the acquired data, could be exploited to extract heart rate. The paper presents new algorithms for estimating heart rate from tracheal sounds, especially in very loud snoring environment. The advantage is that it is possible to reduce the number of diagnostic devices, especially for compact home applications. Three algorithms are proposed, based on optimal filtering and cross-correlation. They are tested firstly on one patient presenting significant pathology of apnea syndrome, with a recording of 509 min. Secondly, an extension to a database of 16 patients is proposed (16 hours of recording). When compared to a reference ECG signal, the final results obtained from tracheal sounds reach an accuracy of 81% to 98% and an RMS error from 1.3 to 4.2 bpm, according to the level of snoring and to the considered algorithm. [ABSTRACT FROM AUTHOR]

This paper is concerned with the optimal H2 filtering problem for continuous‐time systems with multiplicative noise and multiple sampled delay measurements. The reorganized observation technique is firstly applied for treating the sampled delay terms, and then an equivalent delay‐free sampled measurement is received. An optimal H2 filter is constructed by using a dynamic model with finite jumps, which can provide the state estimation of the measurement sampling interval as well as the measurement sampling moments. The filter gain is given in terms of the stabilizing solution to a set of specific Riccati differential equations. It should be pointed out that no state augmentation is required, and thus the Riccati equation developed in this paper remain the same dimension as that of the original system state. This is the clear distinction from the state augmentation method. A practical example is finally supplied to illustrate the efficiency of the proposed results. [ABSTRACT FROM AUTHOR]

The paper considers several scenarios of parametric uncertainty influence on accuracy of system state vector components optimal estimation. The main attention in the simulation is paid to the analysis of the influence of this factor on estimation accuracy and convergence time. A large amount of simulation results on a complex object trajectory is presented. [ABSTRACT FROM AUTHOR]

This paper pays attention to the problem of optimal filtering for linear continuous time-varying It ô stochastic system with multiple delayed measurements and multiplicative noise. The stochastic analysis and calculus of stochastic variables are employed to analyze and design the optimal filtering. For the It ô stochastic continuous-time system with multiple delayed measurements and multiplicative noise, a delay is first transferred from the state to integral term with the Brownian motion in measurements by solving the stochastic equation of multiplicative noise. Then, based on the delay-free state in measurements and the independent increment characteristics of the Brownian motion the optimal filter is derived through the calculation of the conditional expectation. It should be stressed that the optimal filter follows directly from the manipulation of the performance. Finally, a price-volatility feedback rate model in mathematical finance is chosen to demonstrate the design of the optimal filter via the proposed approach in this paper.

Rotating components are commonly used in industry and their failures may cause unexpected accidents and economic losses. To prevent these, prognostics and health management of rotating components attract much attention. The key for prognostics and health management of rotating components is to extract repetitive transients caused by early faults, which is further used to construct health indicators for describing rotating component degradation. In the past years, various theories and algorithms were developed to extract repetitive transients. One of the most famous theories is spectral kurtosis. Moreover, its fast realization is called fast Kurtogram. Even though several improvements have been made on spectral kurtosis and its fast realization, the fast Kurtogram is not an optimal filtering. In this paper, flexible Kurtogram is proposed. First, a flexible filtering framework based on multiple filter banks is constructed. Second, the objective functions of the flexible Kurtogram based on generalized spectral kurtosis are proposed. Third, optimization algorithms are introduced to maximize objective functions so as to make the flexible Kurtogram adaptive for the squared envelope with demodulation analysis. The results showed that the proposed flexible Kurtogram can be used to automatically find an optimal frequency band for demodulation analysis and it is better than the fast Kurtogram.

Subpixel-based image down-sampling has been widely used to improve the apparent resolution of down-sampled images on display. However, previous subpixel rendering methods often introduce distortions, such as aliasing and color-fringing. This study proposes a novel subpixel rendering method that uses selective sampling and optimal filtering. We first generalize the previous frequency domain analysis results indicating the relationships between various down-sampling patterns and the aliasing artifact. Based on this generalized analysis, a subpixel-based down-sampling pattern for each image is selectively determined by utilizing the edge distribution of the image. Moreover, we investigate the origin of the color-fringing artifact in the frequency domain. Optimal spatial filters that can effectively remove distortions caused by the selected down-sampling pattern are designed via frequency domain analyses of aliasing and color-fringing. The experimental results show that the proposed method is not only robust to the aliasing and color-fringing artifacts but also outperforms the existing ones in terms of information preservation.

Copyright of Electronics & Control Systems is the property of National Aviation University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the maritime environment, object motion analysis using bearings-only measurements in a two-dimensional plane is carried out. The observer usually performs S-maneuver on the line of sight for range observability. This traditional S-maneuver is not preferable always. So, in this research work, a procedure is tried out to understand the scenario online and recommend the maneuver accordingly. Object motion parameters are estimated based on one selected acceptance criteria. Online observer maneuver recommendations are carried out to obtain the solution fast. The algorithm is realized using Monte-Carlo simulation and evaluated against number of scenarios. [ABSTRACT FROM AUTHOR]

Copyright of Automatisierungstechnik is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The way of small aircraft strapdown inertial navigation system transfer alignment based on information from the carrier navigation system is considered. Optimal Kalman filter was selected as the main mathematical technique for the proposed method implementation, which showed acceptable accuracy and convergence time in the set task conditions. The main features of SINS transfer alignment algorithm and the results of its operation at complex trajectory simulation are presented. [ABSTRACT FROM AUTHOR]

Summary: This paper is concerned with an asymmetric information linear‐quadratic (AILQ) stochastic Stackelberg differential game with one leader and two followers, where the game system is governed by mean‐field type stochastic differential equation (MF‐SDE). With the help of two systems of Riccati equations, the followers solve a MF‐type stochastic LQ game problem with partial information first, and then, the leader turns to address an optimal control problem driven by linear MF‐type forward‐backward stochastic differential filtering equation (MF‐FBSDFE). By maximum principle, direct construction method and optimal filtering, the open‐loop Stackelberg solution is expressed as a feedback form of state, state estimation, and state mean. [ABSTRACT FROM AUTHOR]

This paper provides conditions on the observation probability distribution in Bayesian localization and optimal filtering so that the conditional mean estimate satisfies convex stochastic dominance. Convex dominance allows us to compare the unconditional mean square error between two optimal Bayesian state estimators over arbitrary time horizons instead of using brute force Monte-Carlo computations. The proof uses two key ideas from microeconomics, namely, integral precision dominance and aggregation of single crossing. The convex dominance result is then used to give sufficient conditions so that the optimal policy of a controlled sensing two-state partially observed Markov decision process (POMDP) is lower bounded by a myopic policy. Numerical examples are presented where the Shannon capacity of the observation distribution using one sensor dominates that of another, and convex dominance holds but Blackwell dominance does not hold. These illustrate the usefulness of the main result in localization, filtering and controlled sensing applications. [ABSTRACT FROM AUTHOR]

Purpose: Efficiency analysis of an optimal algorithm of chirp signal processing based on the chirplet transform as applied to detection of radar targets in uniformly accelerated motion. Design/methodology/approach: Standard methods of the optimal filtration theory are used to investigate the ambiguity function of chirp signals. Findings: An analytical expression has been derived for the ambiguity function of chirp signals that is analyzed with respect to detection of radar targets moving at a constant acceleration. Sidelobe level and characteristic width of the ambiguity function with respect to the coordinates frequency and rate of its change have been estimated. The gain in the signal-to-noise ratio has been assessed that is provided by the algorithm under consideration as compared with application of the standard Fourier transform to detection of chirp signals against a “white” noise background. It is shown that already with a comparatively small (

Abstract In this paper, the problem of filtering for discrete-time networked control systems with time-correlated fading channels is considered where each time-correlated fading channel is described by a one-dimensional discrete-time uncertain linear system model with random disturbance. Using the innovation analysis approach and optimal estimates for the products of channel gain and system state obtained, an optimal linear recursive filter is proposed, that has time-independent complexity, and does not increase computation and storage load with time. An example and simulation are provided to show the effectiveness and potential of the proposed new design technique. [ABSTRACT FROM AUTHOR]

In this work, we introduce a stochastic maximum principle (SMP) approach for solving the reinforcement learning problem with the assumption that the unknowns in the environment can be parameterized based on physics knowledge. For the development of numerical algorithms, we apply an effective online parameter estimation method as our exploration technique to estimate the environment parameter during the training procedure, and the exploitation for the optimal policy is achieved by an efficient backward action learning method for policy improvement under the SMP framework. Numerical experiments are presented to demonstrate that the SMP approach for reinforcement learning can produce reliable control policy, and the gradient descent type optimization in the SMP solver requires less training episodes compared with the standard dynamic programming principle based methods. • SMP has multiple advantages over DPP as an optimal control solver. • The BAL under the SMP framework is reliable and efficient. • Environment parameterization is necessary when solving scientific problems. [ABSTRACT FROM AUTHOR]

We study in this work the mixed -filtering problem for Markov jump linear systems in a partial observation context. Rather than the Markov chain , we consider that only an estimation coming from a detector is available to the filter. We present a sufficient condition for the synthesis of a filter depending only on such that the estimation errors for the and filtering problems are bounded by given scalars. The robust mixed filtering considering that the transition and detection probabilities are uncertain, as well as the complete observation, cluster and mode-independent cases, are also studied. The results are given in terms of linear matrix inequalities and are illustrated by a numerical example. [ABSTRACT FROM AUTHOR]

In this work, we seek exact formulations of the optimal estimator and filter for a non-linear framework, as the Kalman filter is for a linear framework. The solution is well established with the Mortensen filter in a continuous-time setting, but we seek here its counterpart in a discrete-time context. We demonstrate that it is possible to pursue at the discrete-time level an exact dynamic programming strategy and we find an optimal estimator combining a prediction step using the model and a correction step using the data. This optimal estimator reduces to the discrete-time Kalman estimator when the operators are in fact linear. Furthermore, the strategy that consists of discretizing the least square criterion and then finding the exact estimator at the discrete level allows to determine a new time-scheme for the Mortensen filter which is proven to be consistent and unconditionally stable, with also a consistent and stable discretization of the underlying Hamilton-Jacobi-Bellman equation. [ABSTRACT FROM AUTHOR]

The Wiener–Kolmogorov theory of filtering has been with us since the first half of the twentieth century. A later matrix-based approach which was more general was derived with the steady-state Kalman filter. This approach uses a novel method of representing causal and uncausal systems in the form of convolution matrices and leads to a Wiener solution which is much easier to calculate than either the Kalman or Wiener approaches. For coloured additive noise, it avoids the use of Diophantine equations. The key idea missing in previous work is the close link between polynomials and Toeplitz matrices which are lower triangular in form. There is already a reasonably sized literature in the mathematics field on such matrices and so the area is ripe for exploration. Although the method does not offer a different or better solution, it shows a completely new way of defining linear time-invariant (LTI) systems which is neither transfer-function nor state-space-based. This is achieved by exploiting the connection between polynomials and Toeplitz matrices. The application here is the Wiener filter but there could well be many more as this is a generic approach. [ABSTRACT FROM PUBLISHER]

Quantisation of signals is a ubiquitous property of digital processing. In many cases, it introduces significant difficulties in state estimation and in consequence control. Popular approaches either do not address properly the problem of system disturbances or lead to biased estimates. Our intention was to find a method for state estimation for stochastic systems with quantised and discrete observation, that is free of the mentioned drawbacks. We have formulated a general form of the optimal filter derived by a solution of Fokker–Planck equation. We then propose the approximation method based on Galerkin projections. We illustrate the approach for the Ornstein–Uhlenbeck process, and derive analytic formulae for the approximated optimal filter, also extending the results for the variant with control. Operation is illustrated with numerical experiments and compared with classical discrete-continuous Kalman filter. Results of comparison are substantially in favour of our approach, with over 20 times lower mean squared error. The proposed filter is especially effective for signal amplitudes comparable to the quantisation thresholds. Additionally, it was observed that for high order of approximation, state estimate is very close to the true process value. The results open the possibilities of further analysis, especially for more complex processes. [ABSTRACT FROM PUBLISHER]