Showing papers by "Luigi Chisci published in 2018"

Robust Fusion for Multisensor Multiobject Tracking

Abstract: This letter proposes analytical expressions for the fusion of certain classes of labeled multiobject densities via Kullback–Leibler averaging. Specifically, we provide analytical fusion rules for the labeled multi-Bernoulli and marginalized $\delta$ -generalized labeled multi-Bernoulli families of labeled multiobject densities. Information fusion via Kullback–Leibler averaging ensures immunity to double counting of information and is essential to the development of effective multiagent multiobject estimation.

Distributed Joint Attack Detection and Secure State Estimation

Abstract: The joint task of detecting attacks and securely monitoring the state of a cyber-physical system is addressed over a cluster-based network wherein multiple fusion nodes collect data from sensors and cooperate in a neighborwise fashion in order to accomplish the task. The attack detection–state estimation problem is formulated in the context of random set theory by representing joint information on the attack presence/absence, on the system state, and on the attack signal in terms of a hybrid Bernoulli random set (HBRS) density. Then, combining previous results on HBRS recursive Bayesian filtering with novel results on Kullback–Leibler averaging of HBRSs, a novel distributed HBRS filter is developed and its effectiveness is tested on a case study concerning wide-area monitoring of a power network.

Multi-Sensor Multi-Object Tracking with Different Fields-of-View Using the LMB Filter

Abstract: A key issue in multi-sensor surveillance is the capability to surveil a much larger region than the field-of-view (FoV) of any individual sensor by exploiting cooperation among sensor nodes Whenever a centralized or distributed information fusion approach is undertaken, this goal cannot be achieved unless a suitable fusion approach is devised This paper proposes a novel approach for dealing with different FoVs within the context of Generalized Covariance Intersection (GCI) fusion The approach can be used to perform multi-object tracking on both a centralized and a distributed peer-to-peer sensor network Simulation experiments on realistic tracking scenarios demonstrate the effectiveness of the proposed solution

Distributed Averaging of Exponential-Class Densities With Discrete-Time Event-Triggered Consensus

Abstract: The paper addresses discrete-time event-driven consensus on exponential-class probability densities (including Gaussian, binomial, Poisson, Rayleigh, Wishart, Inverse Wishart, and many other distributions of interest) completely specified by a finite-dimensional vector of so-called natural parameters. First, it is proved how such exponential classes are closed under Kullback-Leibler fusion (average), and how the latter is equivalent to a weighted arithmetic average over the natural parameters. Then, a novel event-driven transmission strategy is proposed in order to trade off the data-communication rate and, hence, energy consumption, versus consensus speed and accuracy. A theoretical analysis of the convergence properties of the proposed algorithm is provided by exploiting the Fisher metric as a local approximation of the Kullback-Leibler divergence. Some numerical examples are presented in order to demonstrate the effectiveness of the proposed event-driven consensus. It is expected that the latter can be successfully exploited for energy- and/or bandwidth-efficient networked state estimation.

Centralized Multiple-View Information Fusion for Multi-Object Tracking Using Labeled Multi-Bernoulli Filters

Abstract: In many applications, the states of an unknown number of objects need to be estimated using measurements that are acquired from multiple sensors with different fields of view. When object labels are part of their states, the problem is called the multi-sensor multi-object tracking problem. This paper presents a new solution for statistical fusion of multisensor information in such problems where the sensors form a centralized network. Assuming that a labeled multi-Bernoulli (LMB) filter is running at each sensor node, we suggest a new approach to fuse the multiple LMB posteriors in a centralized manner. The fused posterior is designed to incorporate all the information provided by multiple sensor nodes for each object label. Numerical experiments involving challenging multi-sensor multi-object tracking scenarios show that the proposed method outperforms the state of the art.

Event-Triggered Consensus Bernoulli Filtering

Abstract: This paper focuses on reducing communication bandwidth and, consequently, energy consumption in the context of distributed target detection and tracking over a peer-to-peer sensor network. A consensus Bernoulli filter with event-triggered communication is developed by enforcing each node to transmit its local information to the neighbors only when a suitable measure of discrepancy between the current local posterior and the one predictable from the last transmission exceeds a preset threshold. Two information-theoretic criteria, i.e. Kullback-Leibler divergence and Hellinger distance, are adopted in order to measure the discrepancy between random finite set densities. The performance of the proposed event-triggered consensus Bernoulli filter is evaluated through simulation experiments.

MAP moving horizon state estimation with binary measurements.

Abstract: The paper addresses state estimation for discrete-time systems with binary (threshold) measurements by following a Maximum A posteriori Probability (MAP) approach and exploiting a Moving Horizon (MH) approximation of the MAP cost-function. It is shown that, for a linear system and noise distributions with log-concave probability density function, the proposed MH-MAP state estimator involves the solution, at each sampling interval, of a convex optimization problem. Application of the MH-MAP estimator to dynamic estimation of a diffusion field given pointwise-in-time-and-space binary measurements of the field is also illustrated and, finally, simulation results relative to this application are shown to demonstrate the effectiveness of the proposed approach.

Spectral control for ecological stability

Abstract: A system made up of N interacting species is considered. Self-reaction terms are assumed of the logistic type. Pairwise interactions take place among species according to different modalities, thus yielding a complex asymmetric disordered graph. A mathematical procedure is introduced and tested to stabilise the ecosystem via an ad hoc rewiring of the underlying couplings. The method implements minimal modifications to the spectrum of the Jacobian matrix which sets the stability of the fixed point and traces these changes back to species–species interactions. Resilience of the equilibrium state appear to be favoured by predator-prey interactions.

Robust feedback linearization for input-constrained nonlinear systems with matched uncertainties

Abstract: This paper deals with robust feedback linearization control of nonlinear systems with matched uncertainties and subject to constraints on the control input. The proposed approach not only finds an appropriate pole placement for the linearized system to ensure the satisfaction of input constraints, but also tries to improve the speed of response by effectively exploiting the available control authority. The procedure guarantees the satisfaction of input signal constraints based on the invariant set theorem. Simulation results on an uncertain input-constrained nonlinear spring-mass system demonstrate the effectiveness of the proposed approach in terms of both response speed and constraint satisfaction.

Spectral control for ecological stability

Abstract: A system made up of N interacting species is considered Self-reaction terms are assumed of the logistic type Pairwise interactions take place among species according to different modalities, thus yielding a complex asymmetric disordered graph A mathematical procedure is introduced and tested to stabilise the ecosystem via an {\it ad hoc} rewiring of the underlying couplings The method implements minimal modifications to the spectrum of the Jacobian matrix which sets the stability of the fixed point and traces these changes back to species-species interactions Resilience of the equilibrium state appear to be favoured by predator-prey interactions