Showing papers by "Luigi Chisci published in 2007"

Tracking of a Ballistic Missile with A-Priori Information

Abstract: The paper addresses the problem of estimating the launch and impact points of a ballistic target from radar measurements. The problem has been faced under different hypotheses on the available prior knowledge. The proposed approach combines a nonlinear batch estimator with a recursive MM (multiple model) particle filter in order to attain the estimation goal. Extensive simulations assess the achievable estimation performance.

An approach to threat assessment based on evidential networks

Abstract: The paper develops an information fusion system that aims at supporting a commander's decision making by providing an assessment of threat, that is an estimate of the extent to which an enemy platform poses a threat based on evidence about its intent and capability. Threat is modelled in the framework of the valuation-based system (VBS), by a network of entities and relationships between them. The uncertainties in the relationships are represented by belief functions as defined in the theory of evidence. Hence the resulting network for reasoning is referred to as an evidential network. Local computations in the evidential network are carried out by inward propagation on the underlying joint binary tree. This allows the dynamic nature of the external evidence, which drives the evidential network, to be taken into account by recomputing only the affected paths in the joint binary tree.

Estimation of Constrained Parameters With Guaranteed MSE Improvement

Abstract: We address the problem of estimating an unknown parameter vector x in a linear model y=Cx+v subject to the a priori information that the true parameter vector x belongs to a known convex polytope X. The proposed estimator has the parametrized structure of the maximum a posteriori probability (MAP) estimator with prior Gaussian distribution, whose mean and covariance parameters are suitably designed via a linear matrix inequality approach so as to guarantee, for any xisinX, an improvement of the mean-squared error (MSE) matrix over the least-squares (LS) estimator. It is shown that this approach outperforms existing "superefficient" estimators for constrained parameters based on different parametrized structures and/or shapes of the parameter membership region X

Design and development of a signal and data processor test bed for a passive radar in the FM band

Abstract: This paper describes the design and development of the signal and data processing chain required by a Passive Covert Radar (PCR) exploiting a single non co-operative frequency modulated (FM) commercial radio station as its transmitter of opportunity The processing chain is proved to: 1) efficiently remove interference and clutter from the received signal, 2) detect targets and extract their bistatic range, bistatic Doppler and azimuth, 3) track targets in the Cartesian domain via the use of particle filtering The whole processing sequence has been tested using emulated FM radio signals and scenarios as an input to receiving channels Detection and tracking performance of the passive radar have been assessed comparing the performance of the signal and data processor test bed to the truth data feeding the test bed itself (5 pages)

Towards optimal energy-quality tradeoff in tracking via sensor networks

Abstract: The paper addresses tracking of a moving target by means of a wireless sensor network A centralized tracking filter and procedure for selectively activating sensors around the expected target's position are combined Unlike selective activation methods existing in the literature, which are concerned only with the tracking accuracy, the one considered in this work attempts to tradeoff tracking performance optimization versus lifetime maximization A performance evaluation via Monte Carlo simulations shows the effectiveness of the proposed approach

Improved estimation for object localization via sensor networks

Abstract: The paper addresses object localization via a distributed sensor network. A centralized estimation approach is undertaken along with a selective node activation strategy to ensure energy efficiency. The attention is concentrated on compensating the scarcity of information in the data (i.e., few and noisy measurements) due to selective activation by the exploitation of available constraints. To this end, a recently developed soft-constrained estimation algorithm based on linear matrix inequalities is applied to the localization problem to get an improved quality of estimation. The performance of the proposed approach is evaluated on a case-study concerning a network of direction of arrival sensors.