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As it is not suffered by the well-known noise uncertainty problem of energy detection (ED), generalized likelihood ratio test (GLRT) based spectrum sensing schemes for a single secondary user node have recently been extensively addressed. In this paper, we extend the existing research to an optimal GLRT based cooperative spectrum sensing scheme in a heterogeneous network scenario, where each node...
This paper investigates the use of depth images as localisation sensors for 3D map building. The localisation information is derived from the 3D data thanks to the ICP (Iterative Closest Point) algorithm. The covariance of the ICP, and thus of the localization error, is analysed, and described by a Fisher Information Matrix. It is advocated this error can be much reduced if the data is fused with...
In this paper we present two real-time methods for estimating surface normals from organized point cloud data. The proposed algorithms use integral images to perform highly efficient border- and depth-dependent smoothing and covariance estimation. We show that this approach makes it possible to obtain robust surface normals from large point clouds at high frame rates and therefore, can be used in...
Mostly, shape from focus (SFF) methods utilize initial depth estimate to obtain 3D shape of an object. However, accuracy of these methods is limited due to erroneous initial focus and depth measurements. In this paper, we introduce a Gaussian process regression based approach, which estimates 3D shape of the object from the noisy initial depth values and focus measurements. Initial depth is estimated...
The work demonstrates how the methods of Active Sensing (AS), based on the theory of optimal experimental design, can be applied for a location estimation scenario. The simulated problem consists of several mobile and fixed nodes where each mobile unit is equipped with a gyroscope and an incremental path encoder and is capable to make a selective range measurement to one of several fixed anchors as...
In this paper, we consider the problem of fusing measurements which contain correlated noises within posegraph-based formulations of filtering and estimation problems. We develop a formulation of the Weighted Geometric Density (WGD) fusion algorithm, a generalisation of Covariance Intersection (CI), for posegraphs. We show that this form can generate covariance consistent estimates. We propose two...
In this paper we explore the idea of dimensionality reduction and approximation of OD demand based on principal component analysis (PCA). First, we show how we can apply PCA to linearly transform the high dimensional OD matrices into the lower dimensional space without significant loss of accuracy. Next, we define a new transformed set of variables (demand principal components) that is used to represent...
The performance of any transmission scheme is coupled with the receive strategy. Herein the behavior of transmissions based on interference alignment scheme is investigated under different receive strategies. Moreover, interference alignment is compared with different state-of-art transmission schemes under the assumption of intrabase station and inter-base station coordination. The performance of...
This paper presents an in-depth evaluation of filter algorithms utilized in the estimation of 3D position and attitude for UAV using stereo vision based Visual SLAM integrated with feature detection and matching techniques i.e., SIFT and SURF. The evaluation's aim was to investigate the accuracy and robustness of the filters' estimation for vision based navigation problems. The investigation covered...
A filtering technique of neutron flux during the nuclear reactor restarting is presented in this paper. The nuclear reactor model is described by equation of inverse kinetics. The ionization chambers are used for neutron flux measure. To minimize the undesired noise of measurement the extended Kalman filter (EKF) method is used. The filter EKF is designed using the nuclear reactor model.
This paper presents a novel single target particle filter with spawn model and particle labeling approach, abbreviated SL-PF. The purpose of this filter is to detect instantaneously occurring target maneuvers, e.g. course changes of maritime targets, and to provide accurate tracking performance before and after the maneuvers. The key idea is to borrow the spawn model from the probability hypothesis...
This paper deals with distributed information processing in sensor networks. We propose the Hypothesizing Distributed Kalman Filter that incorporates an assumption of the global measurement model into the distributed estimation process. The procedure is based on the Distributed Kalman Filter and inherits its optimality when the assumption about the global measurement uncertainty is met. Recursive...
the problem of adaptive detection of spatially distributed targets or targets embedded in no homogeneous clutter with unknown covariance matrix is studied. At first, assume the clutter is complex circular zero-mean Gaussian clutter with an unknown positive definite covariance matrix, and it is independent of the covariance matrix vector under test, the secondary data are assumed to be random, then...
Cross-correlating ocean noise is a potential alternative to using active sources to monitor and study ocean environments. However, directional sources in the medium (usually ships) often introduce a bias in the cross-correlations, making the travel time estimates unreliable. Here, we use recent results in random matrix theory for the eigenvalue density of isotropic noise sample covariance matrices...
We cast the problem of reverse-engineering the connectivity matrix of genetic regulatory networks from a limited number of measurements as a regularized multivariate regression problem. The regularization term incorporates the prior knowledge of sparsity of genetic regulatory networks. Moreover, the genetic profiles within a measurement are assumed to be correlated with a full covariance structure...
Charrelation matrices are similar in structure (and in additional properties) to correlation matrices, and are closely related to Hessians of the log-characteristic function at selected “processing-points” away from the origin. Charrelation-based estimation methods were shown to offer significant improvement over second-order (correlation-based) methods when the latter are suboptimal. However, judicious...
Several versions of the Daum-Huang (DH) filter have been introduced recently to address the task of discrete-time nonlinear filtering. The filters propagate a particle set over time to track the system state, but, in contrast to conventional particle filters, there is no proposal density or importance sampling involved. Particles are smoothly migrated using a particle flow derived from a log-homotopy...
Adaptive beamformers attempt to eliminate loud interferers in order to facilitate the detection of quiet sources. The Dominant Mode Rejection (DMR) beamformer does this by placing notches in its beampattern corresponding to signals contained in the interference subspace. This subspace is defined by the eigenvectors associated with the largest eigenvalues of the sample covariance matrix. A companion...
1 We consider a Gaussian MISO wiretap channel, where a multi-antenna source communicates with a single-antenna destination in the presence of a single-antenna eavesdropper. The communication is assisted by multi-antenna helpers that act as jammers to the eavesdropper. Each helper independently transmits noise, which lies in the null space of the helper-destination channel, thus creating no interference...
Path delays in IP networks are important metrics, required by network operators for assessment, planning, and fault diagnosis. Monitoring delays of all source-destination pairs in a large network is however challenging and wasteful of resources. The present paper develops a spatio-temporal prediction approach to track and predict network-wide path delays using measurements on only a few paths. The...
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