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In this paper we propose an alternative solution to the Monocular Simultaneous Localization and Mapping (SLAM) problem. This approach uses a Minimum-Energy Observer for Systems with Perspective Outputs and provides an optimal solution. Contrarily to the most famous EKF-SLAM algorithm, this method yields a global solution and no linearization procedures are required. Furthermore, we show that the estimation...
An interesting generalization of dynamic factor analysis models has been proposed recently by Forni, Lippi and collaborators. These models, called generalized dynamic factor analysis models describe observations of infinite cross-sectional dimension. Quite surprisingly the inherent non-uniqueness of factor analysis models does not occur in this generalized context. We attempt an explanation of this...
A theory is developed for quantifying fault detectability and fault isolability properties of static linear stochastic models. Based on the model, a stochastic characterization of system behavior in different fault modes is defined and a general measure, based on the Kullback-Leibler information, is proposed to quantify the difference between the modes. This measure, called distinguishability, of...
The performance of SLAM based on unscented Kalman filter (UKF-SLAM) and thus the quality of the estimation depends on the correct a priori knowledge of process and measurement noise. Imprecise knowledge of these statistics can cause significant degradation in performance. In this paper, the adaptive Neuro-Fuzzy has been implemented to adapt the matrix covariance process of UKF-SLAM in order to improve...
Bearings-only tracking is a challenging estimation problem due to the variable observability of the underlying targets. In the presence of false alarms and missed detections, the difficulty of the estimation problem is further compounded by the presence of ghost targets. This paper presents a solution to the bearings only tracking problem based on the theory of random finite sets or Finite Sets Statistics...
We propose a novel hybrid GNSS-terrestrial localization algorithm based on particle filter that fuses ranging data from both satellites and terrestrial receivers. The proposed positioning approach, named hybrid-cooperative particle filter (HCPF), is fully distributed and allows both increased positioning availability and accuracy compared to GNSS-only localization in challenged scenarios. Moreover,...
This paper presents an algorithm of fuzzy based Kalman filter for trajectory estimation of dynamical objects. The Fuzzy subsystem is designed to tune dynamically the process noise covariance matrix of the discrete time Kalman Filter. The main adaptation strategy is based on the heuristic knowledge/practical expertise of the human observer/control engineer. The Fuzzy Kalman Filter attempts to offset...
Array manifold error is the key factor which influences the performance of DOA algorithm. In ship borne radar, after distance and velocity resolution, the corresponding spatial azimuth of first-order sea echo can be computed. The first-order sea echo on the fixed frequency can be considered as scale source. After array covariance matrix's eigen value decomposition, the vector corresponding to the...
This paper presents a sensor fusion system for autonomous guidance of a robot. The sensor fusion system is physically composed of a laser range finder and two vision sensors. Also, it is systematically designed to fuse the information obtained from sensors and to overcome those sensor's drawbacks. To be specific, it utilizes double fuzzy logics for fusion and extended Kalman filter for estimation...
The channel estimation is an important issue for safety and non safety applications of intelligent transportation systems (ITS). Communication would come to a standstill if there were no channel estimation. It is essential to know the channel characteristics before sending a signal so that we have a fair knowledge of how distorted our received signal could be. Hence proper estimation is a major requirement...
In a recent paper (Weyer and Campi (2011)) a new approach to state estimation based on the Leaveout Sign-dominant Correlation Regions (LSCR) algorithm was introduced. The algorithm has the property that it delivers guaranteed confidence regions for the true state without knowledge of the variance of the process noise and measurement noise. For first order systems there is additional freedom in the...
In this paper, we present a novel portfolio optimization method that aims to generalize the delta changes of future returns, based on historical delta changes of returns learned in a past window of time. Our method addresses two issues in portfolio optimization. First, we observe that daily returns of stock prices are very noisy and often non-stationary and dependent. In addition, they do not follow...
In this paper, based on a cooperative jamming scheme, the design of the optimal covariance matrix of the artificial noise produced by a helper that maximizes the secret rate between a source and legitimate destination pair in the presence of multiple eavesdroppers is addressed. It is shown that the original nonconvex design problem can be transformed into a sequence of convex optimization problems...
In the beamforming algorithm of the matrix decomposition, through QR decomposition of data matrix, the QR decomposition algorithm transforms the problem of solving the weighted vector into the problem of solving a triangular linear equations, which avoids to estimating and inversing of array signal covariance matrix, improves the robustness of the data; But when the smaller sample number, the noise...
In order to improve the accuracy and efficiency of side channel template attack, interesting points should be selected from side channel leakage traces. A new method combined with correlation analysis is proposed to select interesting points. In this method, a correlation coefficients vector is calculated to reflect secret key dependence of side channel leakage, and the index of the bigger elements...
The composite space-time snapshot signal for the airborne passive radar using transmissions of opportunity is presented. Power budget simulations based on a three-dimensional bistatic airborne passive radar geometry with typical parameters illustrate the power spectra and eigenspectra of the interference scenario and highlight the undesirable random range sidelobes coupling of the direct path and...
In this paper we propose a method to estimate the InSAR interferometric phase using the correlation weight subspace projection technique. In the method the correlation weight data vector is constructed, thus the noise subspace dimension of the corresponding covariance matrix will not be affected by the coregistration error, then avoiding the trouble of calculating the noise subspace dimension before...
A robust D-InSAR deformation phase estimation method is proposed. The method can accomplish the images auto-coregistration by using joint processing of multiple pixels and the phase noise suppression by using signal subspace fitting. The performance is investigated using simulated data.
We have been developing the ideal observer formalism for sonography, which is based on the best-possible diagnostic performance. The ideal performance was compared to that of trained human observers to estimate the visual efficiency for discriminating lesion features. We find that humans are generally less than 10% efficient at accessing visual information essential for breast cancer diagnosis. In...
The problem of joint states dimension reduction and quantizer design under communication constraints was discussed for states estimation in quantized linear dynamic systems. In order to meet the requirements of transmit power constraint and the constraints of number and bandwidth of the parallel channels, the structure of DPCM (differential pulse code modulation) was adopted to produce the quantized...
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