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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 presents a novel and an improved approach for estimating the position of a vehicle using vehicle-infrastructure cooperative localization. In our previous work we presented a Factor Graph based solution which added the topology (inter-vehicle distance) as a constraint while localizing the vehicle using data from sensors from both inside and outside the vehicle. This paper extends the work...
We consider the problem of choosing the best subset of sensors that results in a prescribed error probability Pe in Bayesian setting. Since minimizing the error probability is often difficult to evaluate and manipulate, conventional methods adopt Bhattacharyya distance instead of it. In fact, Chernoff distance is the best achievable exponent in the Bayesian error probability and it is more accurate...
Multispectral face recognition systems are widely used in various access control applications. The vulnerability of multispectral face recognition sensors towards low-cost Presentation Attack Instrument (PAI) such as printed photos used in attacks has emerged as a serious security threat. In this paper, we present a novel framework to detect presentation attacks against an extended multispectral face...
This paper is concerned with the fusion estimation problem for multi-sensor discrete time-invariant linear systems with multiple time delays and colored measurement noise. A fast sequential covariance intersection (SCI) fusion Kalman filter is given based on the augmented Kalman filter in the linear minimum variance sense, which avoids the calculation of the cross covariance matrices between local...
In order to solve the problem that asynchronous multi-source multi-track cannot be correlated effectively, a trajectory similarity model for asynchronous multi-source multi-track and a track correlation algorithm based on this model are proposed in this paper. Based on the idea of searching potential matched data points under spatial and temporal constraints, the optimal matched point is determined...
Robust belief revision methods are crucial in streaming data situations for updating existing knowledge (or beliefs) with new incoming evidence. Bayes conditioning is the primary mechanism in use for belief revision in data fusion systems that use probabilistic inference. However, traditional conditioning methods face several challenges due to inherent data/source imperfections in big-data environments...
With the increase of the imaging resolution, the resulting enormous amount of sampling raw data aggravates transmission and storage load for multi-channel synthetic aperture radar (SAR) system. Considering the fact that the correlation among the dual-channel SAR images is high, we propose a Bayesian compressive sensing (BCS) based SAR imaging algorithm for ground moving targets indication (GMTI) system,...
In decentralised estimation, locally measured data are processed locally and the local filters are unaware of the other ones. Due to the lack of the global knowledge, the fusion of the local estimates cannot utilise the correlations of the local estimate errors in the computation of the fused mean square error matrix. For this reason, algorithms of fusion under unknown correlations have been designed...
We present a novel joint detection and tracking algorithm using raw measurements, in a compressed sensing framework. The sparse vector representing the state space is directly reconstructed, which transforms the nonlinear estimation problem into a linear one through sparse representation. A number of significant grids are obtained based on the sparse vector, indicating the positions of multiple potential...
This paper presents three iterative methods for orientation estimation. The first two are based on iterated Extended Kalman filter (IEKF) formulations with different state representations. The first is using the well-known unit quaternion as state (q-IEKF) while the other is using orientation deviation which we call IMEKF. The third method is based on nonlinear least squares (NLS) estimation of the...
Detection of surface water in natural environment via multi-spectral imagery has been widely utilized in many fields, such land cover identification. However, due to the similarity of the spectra of water bodies, built-up areas, approaches based on high-resolution satellites sometimes confuse these features. A popular direction to detect water is spectral index, often requiring the ground truth to...
Tracking in high-dimensional state-space is particularly hard due to the curse of dimensionality. A way to mitigate the curse of dimensionality is the use of Sequential Hamiltonian Monte Carlo (SHMC). In this paper, we describe the exercise of tracking a single extended target using Integrated Processing based on SHMC. By way of an example, we show that this filter estimates all state variables of...
In this paper we study the problem of estimating the position of a joint that is connecting two rigid links in a biomechanical model. By equipping the two links with inertial sensors, which measure linear acceleration and angular velocity, it is possible to estimate the joint position. Estimation methods for this problem have been proposed before, but experimental evaluation and comparison between...
Tracking single or multiple maneuvering targets is an urgent need for defense. In order to meet the military requirement, we propose a modified clustering-based Rao-Blackwellized particle filter (CBRBPF) to track single or multiple maneuvering targets with observations received by single or multiple sensors. The modified RBPF is basing on the clustering-based data association method. We partition...
Situational Awareness (SAW) is a widespread concept in areas that require critical decision-making and refers to the level of consciousness that an individual or team has about a situation. A poor SAW can induce humans to failures in the decision-making process, leading to losses of lives and property damage. Data fusion processes present opportunities to enrich the knowledge about situations by integrating...
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