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Firstly this article presents a multi-level architecture permitting the localization of a mobile platform and secondly an incremental construction of the environment's map. The environment will be modeled by an occupancy grid built with information provided by the stereovision system situated on the platform. The reliability of these data is introduced to the grid by the propagation of uncertainties...
We consider the problem of state estimation for a dynamic system driven by unobserved, correlated inputs. We model these inputs via an uncertain set of temporally correlated dynamic models, where this uncertainty includes the number of modes, their associated statistics, and the rate of mode transitions. The dynamic system is formulated via two interacting graphs: a hidden Markov model (HMM) and a...
This paper presents an innovative approach to the field of information fusion. Fuzzy mediation differentiates itself from other algorithms, as this approach is dynamic in nature. The experiments reported in this work analyze the interaction of two distinct controllers as they try to maneuver an artificial agent through a path. Fuzzy mediation functions as fusion engine to integrate the two inputs...
This paper presents a methodology for the evaluation of a path planning algorithm based on a learning approach. Here this evaluation procedure is applied for the problem of optimizing the navigation of a mobile robot in a known environment. A metric map composed of landmarks representing natural elements is given to define the best trajectory which permits to guarantee a localization performance during...
This paper proposes an automated learning method (ALM) based on real-coded genetic algorithm (RCGA) to infer the multi-criteria classifiers (MCC) parameters. The multi-criteria classifiers (or multi-criteria classification methods) considered are based on concordance and discordance concepts. A military database of 2545 forward looking infra-red (FLIR) images representing eight different classes of...
Naive-Bayes and k-NN classifiers are two machine learning approaches for text classification. Rocchio is the classic method for text classification in information retrieval. Based on these three approaches and using classifier fusion methods, we propose a novel approach in text classification. Our approach is a supervised method, meaning that the list of categories should be defined and a set of training...
Contemporary situational awareness problems such as automated normalcy learning for anomaly detection and motion behavior prediction are addressed with biologically-inspired processing, representation, and learning approaches. Issues and challenges are discussed and our responses to them described. Relatively simple neural principles provide considerable power in providing capabilities required to...
In the area of process refinement under level 4 data fusion of the JDL model [1,2], high-level sensor management is often performed by human operators manning sensor systems who constantly have to monitor the situational and sensor picture for critical events and dynamically employ myriad sensors' functions to carry out mission-specific tasks. To assist the human operators in dealing better with the...
In this paper we present a comparison of three AFE tools used in the context of ship and vehicle detection based on high resolution data. The three tools (Genie Pro - Los Alamos National Laboratory, Feature Analyst - Visual Learning Systems and eCognition - Definiens AG) were chosen because they were defined as promising and were to be analyzed by NGA in the framework of the STAR program. The comparison...
An improved neurobiologically inspired algorithm for situation awareness in the maritime domain is presented, which takes real-time tracking information and learns motion pattern models on-the- fly, enabling the models to adapt well to evolving situations while maintaining high levels of performance. The constantly refined models, resulting from concurrent incremental learning, are used to evaluate...
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