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The Simultaneous Localization And Mapping (SLAM) estimation problem is a nonlinear problem, due to the nature of the range and bearing measurements. In latter years it has been demonstrated that if the nonlinearities from the attitude are handled by a separate nonlinear observer, the SLAM dynamics can be represented as a linear time varying (LTV) system, by introducing these nonlinearities and nonlinear...
Modeling and filtering of a mobile object stochastic trajectory on the basis of fractal Wiener process taking into account the Hurst indicator are offered. For numerical realization of this processes the wavelet based decomposition is used. The peculiarities of trajectory parameters estimation by using Kalman filter and the wavelet algorithm are investigated. The illustrating examples are given.
In this paper we present a method for the tracking of interacting targets disregarding whether or not the targets are close to each other. The method relies on parametric modeling of assumptions about targets interactive motion. Our filtering solution incorporates the parameters of the model in the state vector to perform on-line parameter estimation and exploitation. The proposed method is applied...
In this paper, we propose the bias-compensated distributed diffusion LMS algorithm to estimate the parameter in the presence of multiplicative regressor noise to mitigate the estimation bias caused by multiplicative regressor noise. In the case where the multiplicative regressor noise exists, the estimate produced by standard distributed diffusion LMS algorithm will be biased. To validate the algorithm,...
For distributed generation (DG) network, it is important to estimate the real-time states. The information-centric networking (ICN) is established to take charge of the communication of DG network. However, the assumption of ideal communication between sensors and the estimation center cannot be guaranteed due to the communication constraints of ICN with the increasing DG network. A conventional algorithm,...
The square root unscented Kalman filter was introduced to provide a more numerically robust formulation of the unscented Kalman filter and to guarantee positive semi-definiteness. The filter maintains the Cholesky factor of the covariance matrix instead of the covariance itself. Efficient linear algebra techniques, including Cholesky update and downdate, are used to predict and update the Cholesky...
We study the problem of distributed state estimation over adaptive networks, where agents collaborate to estimate a common state parameter vector. If the sensing target area is too large or we want to improve the convergence speed of a large adaptive network, single-level diffusion algorithms do not have better performance, so we study the multi-level diffusion Kalman filter algorithm where a network...
In order to solve the coherent problem of Orthogonally Matched(OMP) Pursuit algorithm in sparse reconstruction, the feature vector corresponding to large eigenvalue of SVD is constructed by using received data, and two improved methods are proposed. Both methods reconstruct the angle through the feature vector, and can reconstruct the angle information accurately without knowing the number of the...
An efficient subspace-based two-step direction finding method is proposed for uniform linear arrays. It improves the estimation accuracy for small sample size and coherent sources by diminishing the undesirable terms and utilizing the Toeplitz structure of the sample covariance matrix. Furthermore, it works well even using single snapshot, therefore, it is a good candidate to track the direction-of-arrival...
This paper proposes a method for estimating the process noise covariance matrix, using multiple Kalman filters. The basic idea is to employ the difference between the expected prediction error covariance, calculated in the Kalman filters, and the measured prediction error covariance. The required estimate of the process noise covariance is obtained by solving a least squares problem. One simulated...
A coprime array enables an increased number of degrees-of-freedom by deriving a non-uniform virtual array. However, existing work such as spatial smoothing fails to utilize all of the information provided by the coprime array, which results in performance loss. In this paper, we propose a novel coprime virtual array interpolation-based direction- of-arrival (DOA) estimation algorithm by Toeplitz matrix...
This paper proposes new variable regularized (VR) partial update (PU) affine projection algorithms (APAs) for distributed estimation over adaptive networks. They extend the conventional diffuse PU-APAs (Diff-PU-APAs) by imposing a regularization parameter to mitigate possible impairments, such as modeling uncertainties and lacking of excitation, and to deal with sparse channel estimation problems...
We consider the problem of estimating the parameters of a vector autoregressive (VAR) process from low-dimensional random projections of the observations. This setting covers the cases where we take compressive measurements of the observations or have limits in the data acquisition process associated with the measurement system and are only able to subsample. We first present fundamental bounds on...
In this study, we add on to our previous researches for non-traditional filtering the investigation of measurement and process noise covariance adaptation and propose an Adaptive Unscented Kalman Filter (AUKF) for nanosatellite attitude estimation. Singular Value Decomposition (SVD) method runs using the magnetometer and sun sensor measurements as the first stage of the algorithm and estimates the...
Hypothesis testing of covariance matrices is an important problem in multivariate analysis. Given n data samples and a covariance matrix Σο, the goal is to determine whether or not the data is consistent with this matrix. In this paper we introduce a framework that we call sketched covariance testing, where the data is provided after being compressed by multiplying by a “sketching” matrix A chosen...
In this paper we analyze the shape reproduction errors of continuous spectrum of random Gaussian process (particularly, echo signals from meteorological formations in pulse Doppler weather radar) using various spectral estimation (SE) methods in “adaptive” situation when a priori unknown covariance matrix (CM) is replaced with its estimates of different kind. We also show the significant robustness...
Va\ et al. proposed a method to extend a coprime array to a larger virtual uniform linear array (ULA), thus subspace-based direction of arrival (DOA) estimation algorithms can be used to detect more sources than the number of array elements. However, since the subspace-based DOA estimation methods are applied, the DOA estimation accuracy depends on the number of sources estimation performance. By...
This paper presents two approaches considering a distributed framework for joint optimization of sensor coverage for target detection and target tracking for maximizing estimation performance for multi-agent systems. The first algorithm is based on the Lloyd algorithm, which uses a centroid of Voronoi partitions, one of the workarounds of sensor coverage problems. The other algorithm is based on the...
Estimation of distribution algorithms (EDAs) are a special class of model-based evolutionary algorithms (EAs). To improve the performance of traditional EDAs, many remedies were suggested, which mainly focused on estimating a suitable probability distribution model with superior solutions. Different from existing research ideas, this paper tries to enhance EDA by exploiting the potential value of...
Data fusion for parameter estimation with multi-structure and unequal-precision is considered in this paper. Matrix tools e.g., congruent transformation, trace function and matrix differential, are used to analyze the estimation performance. Theoretical results reveal that: the single equipment estimate, the optimal fusion estimate, and the joint estimate are some special cases of the fusion estimate...
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