Frédéric Dambreville

A hierarchical approach for planning a multisensor multizone search for a moving target

This paper deals with a well-known problem in the general area of search theory: optimize the search resources sharing so as to maximize the probability of detection of a (moving) target. However, the problem we consider here considerably differs from the classical one. First, there is a bilevel search planning and we have to consider jointly discrete and continuous optimization problems. To this perspective original methods are proposed within a common framework. Furthermore, this framework is sufficiently general and versatile so as to be easily and successfully extended to the difficult problem of the multizone multisensor search planning for a Markovian target.

Optimal path planning using Cross-Entropy method

This paper addresses the problem of optimizing the navigation of an intelligent mobile in a real world environment, described by a map. The map is composed of features representing natural landmarks in the environment. The vehicle is equipped with a sensor which allows it to obtain range and bearing measurements from observed landmarks during the execution. These measurements are correlated with the map to estimate its position. The optimal trajectory must be designed in order to control a measure of the performance for the filtering algorithm used for the localization task. As the mobile state and the measurements are random, a well-suited measure can be a functional of the approximate posterior Cramer-Rao bound. A natural way for optimal path planning is to use this measure of performance within a (constrained) Markovian decision process framework. However, due to the functional characteristics, dynamic programming method is generally irrelevant. To face that, we investigate a learning approach based on the cross-entropy method

The cross-entropy method for solving a variety of hierarchical search problems

This paper introduces a common method, based on the cross-entropy method, in order to solve a variety of search problems when search resources are scarce compared to the size of the space of search. In particular, we solve: detection and information search problems, a detection search game, and a two-targets detection search problem. Our approach is built of two steps: first, decompose a problem in a hierarchical manner (two optimization levels) and then, solve the global level using the cross-entropy method. At local level, different solutions are conceivable, depending of the kind of the problem. Problems of interest are in the field of combinatorial optimization and are considered to be hard to solve: we find optimal solution in most cases with a reasonable computation time.

Application of probabilistic PCR5 fusion rule for multisensor target tracking

This paper defines and implements a non-Bayesian fusion rule for combining densities of probabilities estimated by local (non-linear) filters for tracking a moving target by passive sensors. This rule is the restriction to a strict probabilistic paradigm of the recent and efficient proportional conflict redistribution rule no 5 (PCR5) developed in the DSmT framework for fusing basic belief assignments. A sampling method for probabilistic PCR5 (p-PCR5) is defined. It is shown that p- PCR5 is more robust to an erroneous modeling and allows to keep the modes of local densities and preserve as much as possible the whole information inherent to each densities to combine. In particular, p-PCR5 is able of maintaining multiple hypotheses/modes after fusion, when the hypotheses are too distant in regards to their deviations. This new p-PCR5 rule has been tested on a simple example of distributed non-linear filtering application to show the interest of such approach for future developments. The non-linear distributed filter is implemented through a basic particles filtering technique. The results obtained in our simulations show the ability of this p-PCR5-based filter to track the target even when the models are not well consistent in regards to the initialization and real cinematic.

Map-reduce Implementation of Belief Combination Rules

This paper presents a generic and versatile approach for implementing combining rules on preprocessed belief n nfunctions, issuing from a large population of information sources. In this paper, we address two issues, which n nare the intrinsic complexity of the rules processing, and the possible large amount of requested combinations. n nWe present a fully distributed approach, based on a map-reduce (Spark) implementation.

Optimized trajectories for mobile robot with map uncertainty

Abstract In this paper we propose a methodology to generate trajectories for a mobile robot which uses an uncertain geometric map to estimate its position and orientation during its displacement. This methodology is based on the definition of particular paths and on defining a cost function to weigh the trade-off between expected localization gain and the risk due to the map uncertainty.

Combining evidences by means of the entropy maximization principle

Works have investigated the problem of the conflict redistribution in the fusion rules of evidence theories. As a consequence of these works, many new rules have been proposed. Now, there is not a clear theoretical criterion for a choice of a rule instead another. The present paper proposes a new theoretically grounded rule, based on a new concept of sensor independence. This new rule avoids the conflict redistribution, by an adaptive combination of the beliefs. Both the logical grounds and the algorithmic implementation are considered.

Detection with spatial and temporal optimization of search efforts involving multiple modes and multiple resources management

The paper deals with optimisation of splittable resources aimed at the detection of a moving target, following a Markovian movement or a conditionally deterministic motion. Our work extends S.S. Browns (1980) spatial optimisation method. By use of a generalised linear formalism, we developed a method for optimizing both spatially and temporally (modeling resource renew), with management of multiple resource types or multi-mode resources. Such optimisation also involves the fusion of several detection tools, in order to make them work together efficiently.

Generic and Concurrent Computation of Belief Combination Rules

As a form of random set, belief functions come with specific semantic and combination rule able to perform the representation and the fusion of uncertain and imprecise informations. The development of new combination rules able to manage conflict between data now offers a variety of tools for robust combination of piece of data from a database. The computation of multiple combinations from many querying cases in a database make necessary the development of efficient approach for concurrent belief computation. The approach should be generic in order to handle a variety of fusion rules. We present a generic implementation based on a map-reduce paradigm. An enhancement of this implementation is then proposed by means of a Markovian decomposition of the rule definition. At last, comparative results are presented for these implementations within the frameworks Apache Spark and Apache Flink.

Generic and massively concurrent computation of belief combination rules: a MapReduce approach

This paper presents a generic and versatile approach for implementing combining rules on preprocessed belief functions, issuing from a large population of information sources. In this paper, we address two issues, which are the intrinsic complexity of the rules processing, and the possible large amount of requested combinations. We present a fully distributed approach, based on a MapReduce scheme. This approach is generic. In particular, we compare two implementations of three sources Dubois & Prade rule within framework Apache Spark and Apache Flink.