Optimal Policies Search for Sensor Management : Application to the AESA Radar

This report introduces a new approach to solve sensor management problems. Classically sensor management problems are formalized as Partially-Observed Markov Decision Process (POMPD). Our original approach consists in deriving the optimal parameterized policy based on stochastic gradient estimation. Two differents techniques nammed Infinitesimal Approximation (IPA) and Likelihood Ratio (LR) can be used to adress such a problem. This report discusses how these methods can be used for gradient estimation in the context of sensor management . The effectiveness of this general framework is illustrated by the managing of an Active Electronically Scanned Array Radar (AESA Radar)

Optimal Policies Search for Sensor Management

International audienceThis paper introduces a new approach to solve sensor management problems. Classically sensor management problems can be well formalized as Partially-Observed Markov Decision Processes (POMPD). The original approach developped here consists in deriving the optimal parameterized policy based on a stochastic gradient estimation. We assume in this work that it is possible to learn the optimal policy off-line (in simulation ) using models of the environement and of the sensor(s). The learned policy can then be used to manage the sensor(s). In order to approximate the gradient in a stochastic context, we introduce a new method to approximate the gradient, based on Infinitesimal Perturbation Approximation (IPA). The effectiveness of this general framework is illustrated by the managing of an Electronically Scanned Array Radar. First simulations results are finally proposed

Statistically robust representation and comparison of mortality profiles in archaeozoology

Archaeozoological mortality profiles have been used to infer site-specific subsistence strategies. There is however no common agreement on the best way to present these profiles and confidence intervals around age class proportions. In order to deal with these issues, we propose the use of the Dirichlet distribution and present a new approach to perform age-at-death multivariate graphical comparisons. We demonstrate the efficiency of this approach using domestic sheep/goat dental remains from 10 Cardial sites (Early Neolithic) located in South France and the Iberian Peninsula. We show that the Dirichlet distribution in age-at-death analysis can be used: (i) to generate Bayesian credible intervals around each age class of a mortality profile, even when not all age classes are observed; and (ii) to create 95% kernel density contours around each age-at-death frequency distribution when multiple sites are compared using correspondence analysis. The statistical procedure we present is applicable to the analysis of any categorical count data and particularly well-suited to archaeological data (e.g. potsherds, arrow heads) where sample sizes are typically small

Catch Per Unit Research Effort: Sampling Intensity, Chronological Uncertainty, and the Onset of Marine Fish Consumption in Historic London

As the cumulative volume of ecofactual data from archaeological sites mounts, the analytical tools required for its synthesis have not always kept pace. While recent attention has been devoted to spatial aspects of meta-analysis, the methodological challenges of chronological synthesis have been somewhat neglected. Nowhere is this issue more acute than for urban sites, where complex, well-dated stratigraphy; rich organic remains; and multiple small- to medium-scale excavations often lead to an abundance of small datasets with cross-cutting phasing and varied chronological resolution. Individually these may be of limited value, but together they can represent the environmental and socioeconomic history of a city. The challenge lies in developing tools for effective synthesis. This paper demonstrates a new approach to chronological meta-analysis of ecofactual data, based upon (a) use of simulation to deal with dating uncertainty, and (b) calibration of results for variable research intensity. We apply this approach to a large body of historic-period fish bone data from London, revealing otherwise undetectable detail regarding one of the most profound shifts in medieval English economic and environmental history: the sudden onset of marine fishing commonly known as the Fish Event Horizon. Most importantly, we show that this phenomenon predates any visible decline in deposition of freshwater fish, and hence cannot have been driven by depletion of inland fisheries as has sometimes been suggested. The R package developed for this research, archSeries, is freely available

Statistically robust representation and comparison of mortality profiles in archaeozoology

Archaeozoological mortality profiles have been used to infer site-specific subsistence strategies. There is however no common agreement on the best way to present these profiles and confidence intervals around age class proportions. In order to deal with these issues, we propose the use of the Dirichlet distribution and present a new approach to perform age-at-death multivariate graphical comparisons. We demonstrate the efficiency of this approach using domestic sheep/goat dental remains from 10 Cardial sites (Early Neolithic) located in South France and the Iberian Peninsula. We show that the Dirichlet distribution in age-at-death analysis can be used: (i) to generate Bayesian credible intervals around each age class of a mortality profile, even when not all age classes are observed; and (ii) to create 95% kernel density contours around each age-at-death frequency distribution when multiple sites are compared using correspondence analysis. The statistical procedure we present is applicable to the analysis of any categorical count data and particularly well-suited to archaeological data (e.g. potsherds, arrow heads) where sample sizes are typically small

NOTATION

Abstract — Nonlinear distributed target tracking for a single target is addressed in this paper. The problem consists in deriving fusion rules for local full/partial target state estimates processed by a number of sensors. We investigate the general ways for the nonlinear fusion rules with/without feedback implementation via particle filtering algorithms. In particular, we focuse on practical application of these ideas for specific multi-sensor architectures including low/high bandwidth. Then, these new approaches are applied to the distributed bearings-only tracking problem

Initialization of Particle Filter and Posterior Cramér-Rao Bound for Bearings-Only Tracking in Modified Polar Coordinate System

We here address the classical bearings-only tracking problem (BOT) for a single object, which belongs to the general class of non linear filtering problems. Recently, algorithms based on sequential Monte Carlo methods (particle filtering) have been proposed. However, initializing particle filtering is often the main difficulty, especially if the state is only partially observed (BOT). To remedy for this problem, the problem is immersed in a modified polar coordinate (MP) framework. This approach leads us to consider an original formulation of the BOT problem within the MP system. In particular, it is shown that this problem is relevant to a more general class of problems: nonlinear filtering with unknown state covariance. Inside this particular framework, particle filters can be quite convinently initialized by using only observed bearings (optimization problem). The whole algorithm performs quite satisfactorily, avoiding the need of a strong prior about target location and velocity. Simulation results illustrate the benefits of this approach. The Posterior Cramer-Rao Bound (PCRB) provides a lower bound on the mean square error. Original PCRB approximations for the "partial" state target (the observable components) are derived. It is well-known that the "usual" PCRB is (very) over-optimistic. Relaxing the asymptotic unbiasness hypothesis, a new bound is derived, both for partial or complete state vectors, which presents a good agreement with estimated MSE from simulated data