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With the availability of sensor technology across the broad electromagnetic spectrum, multi-spectral imaging is increasingly used in biometric systems. Especially for face recognition, multi-spectral imaging has gained a lot of attention due to it's invariant property against variation caused by unknown illumination. However, obtaining best performance using multi-spectral imaging is still a challenge...
Autonomous vehicles operating in dynamic environments rely on precise localization. In this paper we present a novel approach for cooperative localization of vehicular systems and an infrastructure RADAR which is resilient against outliers generated from the RADAR. The problem of cooperative localization is represented as a factor graph, where interrelated topologies (including that of outliers) are...
A new change detection method for heterogeneous remote sensing images (i.e. SAR & optics) has been proposed via pixel transformation. It is difficult to directly compare the pixels from heterogeneous images for detecting changes. We propose to transfer the pixels in different images to a common feature space for convenience of comparison. For each pixel in the 1st image, it will be transferred...
The paper addresses the problem of distributed sensor fusion in the framework of random finite set. The Generalized Covariance Intersection (GCI) rule of multi-target densities is extensively used in multi-target Bayesian filtering scheme. But there are two problems in GCI which are unreasonable design of fusion weight and unable to tackle informative differentiation. In order to get rid of the bad...
This paper addresses the problem of joint detection and estimation fusion when sensor quantized data are correlated in the distributed system. The traditional methods to handle this joint problem tend to treat the detection and estimation tasks separately, which put more emphasis on the detection part but treat the estimation part sub-optimally. In this work, the joint detection and estimation fusion...
This paper presents an approach named symmetric measurement equation (SME) to track known number of multiple extended targets. The SME approach removes the target-measurement association uncertainty through converting the original observation into pseudo-measurement vector. The work is focused on tracking moving extended target using SMEs which define new measurements through the sums of products...
This paper explores a novel model to describe linear dynamic system with random delays. Compared with the existing research, the probabilities of random delays in the novel model are calculated by conditional probabilities. Therefore, the process noises and measurements noises in the new model for random delay problems are infinitely correlated. By treating the model as random parameter matrices Kalman...
In this paper, we propose an adaptive node selection strategy for target tracking in passive multiple radar systems, with the objective of minimizing the number of nodes in the tracking task. Since the signal parameters are random in passive systems, we first take the expectation over the random parameters, and derive a new Bayesian Cramer-Rao lower bound (BCRLB) as the criterion. Then, we formulate...
This paper compares the tracking performance that can be achieved when using a nonlinear drag model for a helicopter, a constant drag motion model, and a baseline constant acceleration model. A particle filter is used for state estimation to address problems associated with nonlinear drag and nonlinear measurements of helicopter pose. We demonstrate that the inclusion of this nonlinear kinematic effect...
An autonomous navigation scheme for unmanned aerial vehicles is presented based on visual and inertial measurement information fusion without the known ground cooperative target. The UAV relative translation and rotation motion parameters are estimated by inter-frame image feature detection and tracking. Then the relative motion parameters are considered to be the relative pose measurements of two...
Strapdown inertial navigation system (SINS) is afflicted with the accumulated navigation errors. For implementation of underwater autonomous navigation, it is a challenging task for long-term SINS operations without external information to fix errors. In this paper, a dynamics-aided method is proposed which is suitable for low speed underwater carrier. A generalized velocity integration formula which...
The random-matrix approach to extended object tracking (EOT) supposes that the measurement model is linear and its covariance is a random matrix to stand for the object extension. In practice, however, most measurement algorithms are nonlinear and multiple extensions cannot be simplified by an ellipsoid. This paper proposes a new method for nonlinear maneuvering non-ellipsoidal extended object tracking...
This article presents an information theory based sensor management method to be used for aerospace multi-target collaborative detection and tracking. The proposed sensor management method follows an information theoretic approach, in which PCRLB is used to calculate the tracking accuracy of multi-target. The detection particles are employed to determine the detection probability of incoming targets...
Support vector machine (SVM) is a popular machine learning method and has been widely applied in many real-world applications. Since SVM is sensitive to noises, fuzzy SVM (FSVM) has been proposed to relieve the over-fitting problem caused by noises through assigning a fuzzy membership to each sample. Then, different samples make different contributions to the learning of classification hyperplane...
The conventional multi-target tracking (MTT) algorithms usually suffer from computational intractability problem. The appearance of Iterative Joint Integrated Probabilistic Data Association (iJIPDA) filter solves this problem by providing a tradeoff between the tracking performance and computational cost for computational resource management of sensor systems. However, the iJIPDA filter essentially...
In the multiple target tracking scenarios, the correct matching between targets and measurements is critical. There have been many approaches to resolve this problem called data association. In this paper, a regression method is proposed to resolve the data association problem. In the logistic regression model, nine potential predictor variables are designed which are related to the geometric information...
Bayesian filters are often used in statistical inference and consist of recursively alternating between two steps: prediction and correction. Most commonly the Gaussian distribution is used within the Bayes filtering framework, but other distributions, which could model better the nature of the estimated phenomenon like the von Mises-Fisher distribution on the unit sphere, have also been subject of...
Big classes of directional distribution laws generalizing the von Mises distribution are provided in [4] following a general geometric offset approach in [20]. Once a distribution law is estimated for modeling a given data set, one of the next steps of statistical analysis is simulating from such distribution. The von Mises distribution was simulated in [1] using an acceptance-rejection simulation...
Registration of images from different modalities in the presence of intra-image fluctuation and noise contamination is a challenging task. The accuracy and robustness of the deformable registration largely depend on the definition of appropriate objective function, measuring the similarity between the images. Among them the multi-dimensional modality independent neighbourhood descriptor (MIND) is...
This paper presents a compressed sensing based sensor selection algorithm for direction-of-arrival estimation, in a large scale randomly distributed sensor array. First, a target tracker is employed for the prior information of target position. Second, according to the prior information, we reduce rank of sensing matrix by narrowing the interesting area. Third, a linear independence combination of...
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