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Finite-sample system identification algorithms can be used to build guaranteed confidence regions for unknown model parameters under mild statistical assumptions. It has been shown that in many circumstances these rigorously built regions are comparable in size and shape to those that could be built by resorting to the asymptotic theory. The latter sets are, however, not guaranteed for finite samples...
This letter proposes an estimation algorithm for the characterization of multiple point inputs for linear fractional order systems. First, using polynomial modulating functions method and a suitable change of variables the problem of estimating the locations and the amplitudes of a multi-pointwise input is decoupled into two algebraic systems of equations. The first system is nonlinear and solves...
Gyro-less attitude and angular rate estimations are of great importance in small, low-cost spacecraft, where high performance gyroscopes are not available due to multiple limitations. Recent development of accurate, high-bandwidth attitude sensors such as high data-rate star trackers, makes this approach implementable. The gyro-less estimator propagates the estimated states by nonlinear attitude dynamics...
The paper utilizes a novel battery model based on the electrical features of LiFePO4 battery, because Kalman filter algorithm(KF) is largely dependent on system model. Measurements of battery state are easily disturbed by colored noise which is high relevance in working condition, and the paper studies that the system noise satisfy one-order AR model. The paper proposes an adaptive extended Kalman...
This paper presents the design of Unscented Kalman Filter (UKF) for estimation of state space variables of permanent magnet synchronous machine (PMSM). The UKF is shown together with the field oriented speed control. At first, the position and the speed of PMSM are measured, and UKF is used only for a load torque estimation. It is indicated how differences in sampling time of the speed and the current...
In this paper, we present a novel image reconstruction algorithm for positron emission tomography(PET). Almost all of existing reconstruction approaches assume that the measurement model for PET is linear equation with Gaussian white noise or energy-bounded noise, which only approximates the emission and detection of PET very roughly. In fact, the real situation is much more complicated than the one...
As of recently, there are more than half a billion cars on the road throughout the world and hence arises the necessity for making safety a higher priority in vehicle technologies. Modern automobiles contain various functions that assist the driver and enhance safety. Anti-lock breaking systems and vehicle stability control systems are few of the technologies that are used to implement vehicular safety...
Noise pollution has a large negative influence on the health of humans, especially in case of long-term exposure. Various passive hearing protection approaches are available. However, they often lack good protection against low frequency noise. For these applications, the principle of Active Noise Cancellation (ANC) offers a promising supplement. It relies on anti-phase compensation of the noise signal...
The novel adaptive multiple-model target tracking algorithm presented here employs a non-asymptotic state and parameter estimator whose design hinges on a non-standard integral system representation. The same estimator can be used for target maneuver detection and isolation and hence constitutes the principal ingredient of the tracking algorithm. The algorithm does not maintain a model bank, but creates...
Both state propagation and sensor measurements are often corrupted by unmodeled non-Gaussian or heavy-tailed noise. Without dealing with such outliers, the accuracy of a estimator significantly degrades, and control systems that rely on high-quality estimation lose stability. To estimate the states of dynamic systems in which both types of outliers occur, we propose a novel approach that combines...
In this work we describe a method of obtaining guaranteed a posteriori estimates of unknown right-hand sides of the Helmholtz transmission problems from indirect measurements of a solution to this problem. The obtained results can be applied in various models of electromagnetics and acoustics that describe excitation of transparent bodies by sources of different kinds.
The problem of estimating parameters of switched affine systems with noisy input-output observations is considered. A subspace technique is proposed to exploit the observations' permutation structure, which transforms the problem of associating observations with subsystems into one of de-permutating a block diagonal matrix. Then a spectral clustering algorithm is presented to recover the block structure...
This paper describes the implementation of an intelligent navigation system, based on the integrated use of the Doppler velocity log (DVL) and strapdown inertial navigation system (SINS), for autonomous underwater vehicle (AUV) applications. A variable structure multiple model (VSMM) filtering method is presented to be used to fuse the data from the SINS sensors and to integrated them with the DVL...
For multi-model multisensor system with uncertain variance linearly correlated white noises, the problems of designing robust weighted fusion Kalman estimators (predictor, filter, smoother) are addressed. According to the minimax robust estimation principle, applying Lyapunov equation approach, a unified design approach to obtain the local and three weighted fusion robust Kalman estimators of the...
For nonlinear estimation, the Gaussian sum filter (GSF) provides a flexible and effective framework. It approximates the posterior probability density function (pdf) by a Gaussian mixture in which each Gaussian component is obtained using a linear minimum mean squared error (LMMSE) estimator. However, for a highly nonlinear problem with large measurement noise, the estimation performance of the LMMSE...
We develop an Expectation-Maximization (EM) algorithm for the simultaneous tracking and shape estimation of a star-convex object based on multiple spatially distributed measurements. In order to formulate the problem within the EM framework, the unknown measurement sources on the object are modeled as hidden variables. As the measurement sources are continuous quantities, we develop a suitable discretization...
Within the complex driving environment, progress in autonomous vehicles is supported by advances in sensing and data fusion. Safe and robust autonomous driving can only be guaranteed provided that vehicles and infrastructure are fully aware of the driving scenario. This paper proposes a methodology for feature uncertainty prediction for sensor fusion by generating neural network surrogate models directly...
In this paper, a novel image moment-based model for extended object shape estimation and tracking is presented. A method to represent and estimate an elliptical shape using its image moments is first developed. The model of representing the shape of an object falls under the category of random hypersurface model (RHM) for extended object tracking. The moments are estimated using an unscented Kalman...
The identification of a 1D heterogenous network with unmeasurable interconnections between neighboring systems is studied in this paper. For a large-scale networked system, it is usually computationally prohibitive to identify the global system in a centralized manner. To cope with this problem, the local identification of a network using local input-output data is considered in this paper, and a...
A lot of performance evaluation metrics exist for nonlinear filters. At present, the most commonly used one is a single and incomprehensive metric of performance. This metric can continuously and quantitatively describe the performance of the nonlinear filters. But in many cases, we need to rank the performance of the filters. It is in general very hard to rank the filters just using a single metric...
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