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In this paper, we derive the distributed observable state from first principles. In particular, we extend the estimation setup to a distributed framework where in addition to the state and sensing, we also have communication among the sensors. We consider that each sensor estimates the entire state-vector to recover its unobservability. Combining the estimates at all of the sensors we arrive at the...
Currently, in 3D point cloud data field, different methods based on multi-value characteristics, which are utilized to measure the similarity between different point cloud data, are developed. These features are more dependent on low dimensional normal vector and curvature. In this paper, feature histogram of each point of the point cloud is calculated in high dimensional space. Through global feature...
Pedestrian flow estimation is a vital issue in video surveillance. Inspired by fluid mechanics we proposed to model the pedestrian flow as time-dependent fluid, and estimate the pedestrian flow using flux. Firstly, optical flow is used to construct the motion vector field. Then, we compute the inside and outside flux components within fixed areas to estimate the pedestrian flow in different direction...
The desire to improve short-term predictions of wind speed and direction has motivated the development of a spatial covariance-based predictor in a complex valued multichannel structure. Wind speed and direction are modelled as the magnitude and phase of complex time series and measurements from multiple geographic locations are embedded in a complex vector which is then used as input to a multichannel...
In this paper, robustness of star identification and attitude estimation methods that are operating in Lost-in-Space (LIS) mode, under harsh noise conditions of the near space, are analyzed. Despite star extraction, identification and attitude estimation methods that are proposed as solutions for subproblems, there is a lack of study that investigates the effects of errors in the initial stages on...
In many practical periodic parameter estimation problems, the appropriate cost function is periodic with respect to the unknown parameter. In this paper a new class of cyclic Bayesian lower bounds on the mean cyclic error (MCE) is developed. The new class includes the cyclic version of the Bayesian Cramér-Rao bound (BCRB). The cyclic BCRB requires milder regularity conditions compared to the conventional...
This work develops a new DOA tracking technique by proposing a novel semi-parametric method of sequential sparse recovery for a dynamic sparsity model. The proposed method iteratively provides a sequence of spatial spectrum estimates. The final process of estimating direction paths from the spectrum sequence is not considered. However, the simulation results show concentration of the spectrum around...
GROUSE (Grassmannian Rank-One Update Subspace Estimation) [1] is an incremental algorithm for identifying a subspace of ℝn from a sequence of vectors in this subspace, where only a subset of components of each vector is revealed at each iteration. Recent analysis [2] has shown that GROUSE converges locally at an expected linear rate, under certain assumptions. GROUSE has a similar flavor to the incremental...
In the diffusion strategies for distributed estimation over adaptive networks, each node calculates a weighted average of the intermediate parameter estimates of its neighboring nodes. Thus, all the nodes should continuously share their intermediate estimates with their neighbors. In this paper, we consider exchanging a predetermined number of elements of each intermediate estimate vector at each...
This work studies the performance of position estimation for distress beacons using time of arrival and frequency of arrival measurements. The analysis is conducted for emergency signals modeled as pulses with sigmoidal transitions. This model has shown interesting properties for Cospas-Sarsat search and rescue signals. The modified Cramér-Rao bounds of the symbol width, time of arrival, frequency...
Localization is a fundamental challenge for any wireless network of nodes, in particular when the nodes are mobile. For an anchorless network of mobile nodes, we present a relative velocity estimation algorithm based on multidimensional scaling. We propose a generalized two-way ranging model, where the time-varying pairwise distances between the nodes are expressed as a Taylor series for a small observation...
We present a framework for Tensor-based subspace Tracking via Kronecker-structured projections (TeTraKron). TeTraKron allows to extend arbitrary matrix-based subspace tracking schemes to track the tensor-based subspace estimate. The latter can be computed via a structured projection applied to the matrix-based subspace estimate which enforces the multi-dimensional structure in a computationally efficient...
We explore the problem of anomaly detection based on several one-dimensional projections. The main advantage of the proposed approach is that it does not require any covariance matrix estimation, allowing to compute spatial adaptive anomaly detection in small neighborhoods. Although this is contrary to common sense, theoretical results support the consistence of our approach when a large number of...
We introduce a novel approach for pitch tracking of multiple sources in mixture signals. Unlike traditional approaches to pitch tracking, which explicitly attempt to detect periodicities, this approach is using a learning framework by making use of previously pitch-tagged recordings as training data to teach spectrum/pitch associations. We show how the mixture case of this task is a nearest subspace...
Minimum mean square error (MMSE) estimation of block sparse signals from noisy linear measurements is considered. Unlike in the standard compressive sensing setup where the non-zero entries of the signal are independently and uniformly distributed across the vector of interest, the information bearing components appear here in large mutually dependent clusters. Using the replica method from statistical...
In this paper, we introduce a Goodness-of-Fit test for the Multivariate Exponential Power (MEP) distribution, a multivariate extension of the Generalized Gaussian, which has recently gained considerable interest as a model for wavelet coefficients in the context of color image retrieval and spread-spectrum watermarking. We present a size and power study of this test and show Goodness-of-Fit results...
We present a 3D feature descriptor that represents local topologies within a set of folded concentric rings by distances from local points to a projection plane. This feature, called as Concentric Ring Signature (CORS), possesses similar computational advantages to point signatures yet provides more accurate matches. It produces more compact and discriminative descriptors than shape context. It robust...
In this paper we propose a new method for appearance-based pose estimation, called Local Procrustes Regression (LPR). In LPR, rather than learning a map between all available training samples and pose space, as is common for appearance-based pose estimation algorithms, the pose of an unknown sample is recovered locally from a small subset of the training samples, by utilizing their inter-point distances...
This paper introduces a sparsity based optical flow estimation method in digital video sequences. The method stems from the key observation that the gradient field of optical flow, in digital video sequences, is usually structured and sparse in spatial domain, provided there is a small number of multiple motions in the scene. The gradient field of motion vectors is formed by the pixels forming the...
A novel automatic video deshearing is proposed to reduce the skew artifacts (jelly artifacts) in sequences captured by CMOS censor cameras. The paper first considers the principles of skew artifacts in rolling shutter cameras. Then a deshearing algorithm with high accuracy is discussed with automatic skew artifact detection. A video completion step is also implemented to reconstruct unavailable pixels...
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