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This paper describes a new method for segmenting hyperspectral imagery (HSI) using dynamic curves. We are concerned about challenging HSI target segmentation/detection use cases where the scene includes confusers exhibiting a spectral return similar to the desired signature and in close proximity of the object of interest. Our method is based on a level sets approach. It fuses all available spectral...
We propose an asymptotically optimum test for the problem of decentralized sequential hypothesis testing in continuous time, in the case where the sensors have full local memory and no feedback from the fusion center. According to our scheme, the sensors perform locally repeated SPRTs and communicate, asynchronously, their one-bit decisions to the fusion center. The fusion center in turn uses the...
Most of the existing distributed estimation fusion algorithms rely on the existence of the inverses of the corresponding error covariance matrices, e.g., distributed estimation fusion algorithms based on the information form of the Kalman filter and the optimal weighted least-square (WLS) estimator. Theoretically speaking, the error covariance matrices are only at least positive semi-definite and...
Beamforming of near-field sonar signals from sources with appreciable spatial extent is more difficult to perform than the case of far-field point sources. Earlier, a model was developed in which the beam response of an extended source is formulated as a convolution of the beampattern of a point source and a probability density function representing the spatial distribution of the real source about...
In multisensor systems, the measurements reported by local sensors are usually not time aligned or synchronous due to different data rates. A novel algorithm, based on Kalman filter combined with pseudomeasurement and equivalent bias, is proposed to solve a general bias estimate problem in asynchronous sensors systems. The pseudomeasurement equation of sensor biases is obtained by linearizing the...
Time series of optical satellite images acquired at high spatial resolution constitute an important source of information for crop monitoring, in particular for keeping track of crop harvest. However, the quantity of information extracted from this source is often restricted by acquisition gaps and uncertainty of radiometric values. This paper presents a novel approach that addresses this issue by...
New methods for team optimal decision, based on a stochastic agent coordination, are presented, implemented and tested. The methods are extensions of the cross-entropy algorithm (CE), initially dedicated to rare-event simulation. In particular, this paper investigates how the contributions of the agents could be involved in the simulation process. The approaches are tested on a SD-assignment problem,...
This paper considers the problem of the classification of objects observed by vehicle embedded sensors. We propose a general architecture and an algorithm to perform multisensor fusion for the classification purpose. The proposed solution has to be robust and flexible. The robustness is essential because this system is for safety applications. The flexibility is ensured by a modular architecture alongside...
In this paper, we present various approaches for combining classifiers to improve classification of textured images, which are not generally used in this application framework. This is what we call post-classification step of textured images. Three approaches to combine classifiers are presented: the majority voting approach, belief approach, and classification-based approach. Belief, majority voting...
The problem of optimal detection and localization of contamination sources in a distributed parameter system is formulated as that of maximizing the power of a parametric hypothesis test which checks whether or not system parameters have nominal values. We consider a setting where mobile nodes with sensing capabilities form a network aimed at collecting the most valuable measurements for parameter...
Change detection is an important task for remote monitoring, fault diagnostics and system prognostics. When a fault occurs, it will often times cause changes in measurable quantities of the system. Early detection of changes in system measurements that indicate abnormal conditions helps the diagnostics of the fault so that appropriate maintenance action can be taken before the fault progresses, causing...
In recent years, the particle filter has become commonly accepted as the preferred tool for single target tracking in highly non-linear and non-Gaussian environments. This paper investigates the issues that arise when particle filters are integrated into a hierarchical data fusion system, in which the sensor-level tracking is performed using particle filters, but central-level track fusion is performed...
We introduce a new confidence scoring method based on an extension of STANAG2022. Our method uses the two parameters included in the STANAG, that is integrates source-trustworthiness to the computation of information-credibility, with two additional parameters: source-proficiency and information-likelihood. These parameters will be formally defined, as will our understanging of the existing criteria...
In many tracking scenarios, the amplitude of target returns are stronger than those coming from false alarms. This information can be used to improve the multi-target state estimation by obtaining more accurate target and false alarm likelihoods. Target amplitude feature is well know to improve data association in conventional tracking filters (such as the PDA, MHT), and results in better tracking...
Blind localization and tracking of mobile terminals in urban scenarios is an important requirement for offering new location based services, handling emergency cases of non-subscribed users, public safety etc. In this context, we propose a track-before-detect scheme, taking explicit advantage of multipath propagation in an urban terrain by using a priori information about the known locations of the...
The multistatic tracking working group (MSTWG) was formed in 2005 by an international group of researchers interested in developing and improving tracking capabilities when applied to multistatic sonar and radar problems. The MSTWG developed several simulated multistatic sonar scenario data sets for use in tracker evaluation by the grouppsilas participants. A common set of performance metrics was...
A fully neurally integrated high degree of freedom prosthetic limb system is the goal of the Revolutionizing Prosthetics program, a large-scale Defense Advanced Research Projects Agency (DARPA) project. Multi-Sensor Data Fusion (MSDF) for prosthetic control is one of the research areas. This paper proposes a novel approach of applying MSDF technology to prosthetics. The paper proposes a prosthetic...
In this contribution the problem of tracking convoys moving on the ground by means of airborne radar is discussed. A coherent radar with multi-channel array antenna is considered which makes clutter suppression by space-time adaptive processing (STAP) techniques possible. In addition, a technique to estimate the lateral length component of a convoy is used in addition to the conventional range measurement...
In this paper the multitarget tracking (MTT) under a cluttered environment is considered. The proposed approach contains two steps: The first step is based on clustering algorithm of finite mixture models (FMM). The second step first obtain equivalent measurement (EQM) and then the EQM is used to estimate state of target. In fact, The first step is the parametric estimation of the FMM and the second...
For the detection of targets moving on ground, airborne ground moving target indicator (GMTI) radar is well-suited. In the tracking process, complex target dynamics, particularly stop and go maneuvers, and target masking due to Doppler blindness, often lead to track losses. By means of a refined sensor model it is possible to detect and handle such diverse target states. In addition, the generation...
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