Luigi Chisci

• PhD
• Professor at University of Florence

Underwater source localization from a passive array of acoustic sensors is a challenging problem, especially in complex environments characterized by multipath and reverberation effects, irregular seabed geometry, and low signal-to-noise ratio. This paper proposes a recursive Bayesian approach that propagates a spectral-element approximation of the...

p>This paper proposes a method for tracking 3D extended target based on superquadric models.</p

p>This paper proposes a method for tracking 3D extended target based on superquadric models.</p

In this article, the interest is on the fusion of labeled random finite set (LRFS) densities computed by sensors having different fields of view (FoVs). In order to deal with different FoVs of the local densities, the global label space is divided into disjoint subspaces, which represent the exclusive FoVs and the common FoV of the agents, and a me...

In this article, we propose a novel approach to distributed joint detection, tracking, and classification (D-JDTC) of multiple targets by means of a multisensor network. The proposed approach relies on labeled multi-Bernoulli (LMB) random finite set modeling of the multisensor state, and consists of two main tasks, that is, local filtering in each...

This letter addresses Time-Of-Arrival estimation for an indoor positioning system (IPS) made up of distributed 5G small cells by focusing on the Iterative Adaptive Approach (IAA) with Orthogonal Frequency Division Multiplexing (OFDM) signals. While IAA has been successfully applied to many other areas, its application to 5G IPS is not straightforwa...

This paper addresses state estimation of linear systems with special attention on unknown process and measurement noise covariances, aiming to enhance estimation accuracy while ensuring stability. To this end, the full information estimation problem over a finite interval is first addressed. Then, a novel adaptive variational Bayesian (VB) moving h...

This paper deals with state estimation over distributed sensor networks with unknown process noise covariance matrix (PNCM) and measurement noise covariance matrices (MNCMs). The unknown PNCM and MNCMs are modeled with constrained inverse Wishart distributions and estimated together with the state trajectory within a moving horizon window. Each loc...

This paper aims to solve the problem of distributed joint detection, tracking and classification (D‐JDTC) of a target on a peer‐to‐peer sensor network. The target can be present or not, can belong to different classes, and depending on its class can behave according to different kinematic modes. Accordingly, it is modelled as a suitably extended Be...

Point source estimation aims to detect and localize a concentrated diffusive source as well as to estimate its intensity and induced field from pointwise-in-time-and-space measurements of sensors spread over the area to be monitored. The space–time dynamics of the diffused field is modeled by an advection–diffusion–reaction partial differential equ...

A key objective of multi-agent surveillance systems is to monitor a much larger region than the limited field-of-view (FoV) of any individual agent by successfully exploiting cooperation among multiple agents. Whenever either a centralized or a distributed approach is pursued, this goal cannot be achieved unless an appropriately designed fusion s...

An event-triggered consensus filter is proposed in this letter for state estimation in distributed sensor networks based on the hybrid consensus on measurement and consensus on information scheme. For bandwidth reduction and energy saving, an event-triggered transmission strategy is developed in which each node selectively transmits only the most r...

This correspondence investigates the problem of reducing energy and bandwidth consumption of a sensor network for distributed multi-target tracking by the labeled multi-Bernoulli filter. To this end, an event-triggered method is adopted together with a consensus strategy for each sensor node to transmit only Bernoulli components that achieve enough...

This paper addresses state estimation of linear systems with special attention on unknown process and measurement noise covariances, aiming to enhance estimation accuracy while preserving the stability guarantee of the Kalman filter. To this end, the full information estimation problem over a finite interval is firstly addressed. Then, a novel adap...

This paper focuses on \textit{joint detection, tracking and classification} (JDTC) of a target via multi-sensor fusion. The target can be present or not, can belong to different classes, and depending on its class can behave according to different kinematic modes. Accordingly, it is modeled as a suitably extended Bernoulli \textit{random finite set...

An adaptive consensus filter for sensor networks with unknown process and measurement noise statistics is proposed in this letter. The variational Bayes (VB) approach is exploited to get local estimates of unknown noise covariances with prior inverse Wishart distributions. A distributed averaging approach on exponential-class densities is applied f...

This paper addresses distributed multi-target tracking over a network of sensors having different fields-of-view (FoVs). Specifically, a cardinalized probability hypothesis density (CPHD) filter is run at each sensor node. Due to the fact that each sensor node has limited FoV, the standard fusion methods need to be suitably modified. In fact, the m...

A key objective of multi-agent surveillance systems is to monitor a much larger region than the limited field-of-view (FoV) of any individual agent by successfully exploiting cooperation among multi-view agents. Whenever either a centralized or a distributed approach is pursued, this goal cannot be achieved unless an appropriately designed fusion s...

Multi-detection (MD) systems are characterized by multiple observation modes (OMs) and hence simultaneously produce multiple measurements for each target. The key challenge in exploiting MD systems for multi-target tracking (MTT), compared to single-detection (SD) systems, is the significant amount of extra computational burden involved in order to...

This article addresses simultaneous localization and mapping (SLAM) via probability hypothesis density (PHD) filtering. The resulting approach, named PHD-SLAM, has demonstrated its effectiveness, especially when measurements provided by the sensors onboard the vehicle are highly contaminated by missdetections and clutter. However, since the pr...

An improved adaptive variational Bayesian cubature information fusion algorithm for nonlinear multi-sensor systems with uncertain noise statistics is proposed in this paper. Aiming to estimate uncertain process and measurement noise covariances in nonlinear systems, the variational Bayesian theory is combined with the inverse Wishart distribution....

This paper presents a new solution for multi-target tracking over a network of sensors with limited spatial coverage. The proposed solution is based on the centralized data fusion architecture. The main contribution of the paper is the introduction of a new track-to-track fusion approach in which the posterior distributions of multi-target states,...

Aiming towards state estimation and information fusion for nonlinear systems with heavy-tailed measurement noise, a variational Bayesian Student’s t-based cubature information filter (VBST-CIF) is designed. Furthermore, a multi-sensor variational Bayesian Student’s t-based cubature information feedback fusion (VBST-CIFF) algorithm is also derived....

Complex real-world phenomena are often modeled as dynamical systems on networks. In many cases of interest, the spectrum of the underlying graph Laplacian sets the system stability and ultimately shapes the matter or information flow. This motivates devising suitable strategies, with rigorous mathematical foundation, to generate Laplacians that pos...

This article addresses fully distributed multirobot (multivehicle) simultaneous localization and mapping (SLAM). More specifically, a multivehicle scenario is considered, wherein a team of vehicles explore the scene of interest in order to cooperatively construct the map of the environment by locally updating and exchanging map information in a nei...

This paper presents a novel approach to the localization of moving targets in a complex environment based on the measurement of the perturbations induced by the target presence on an independently‐generated time‐varying electromagnetic field. Field perturbations are measured via a set of sensors deployed over the domain of interest and used to dete...

This paper addresses distributed multi-target tracking (DMTT) over a network of sensors having different fields-of-view (FoVs). Specifically, a cardinality probability hypothesis density (CPHD) filter is run at each sensor node. Due to the fact that each sensor node has a limited FoV, the commonly adopted fusion methods become unreliable. In fact,...

Complex real-world phenomena are often modeled as dynamical systems on networks. In many cases of interest, the spectrum of the underlying graph Laplacian sets the system stability and ultimately shapes the matter or information flow. This motivates devising suitable strategies, with rigorous mathematical foundation, to generate Laplacian that poss...

The linear opinion pool (LinOP) provides a potential solution to the problem of information fusion. However, the LinOP cannot be directly applied to multi-object fusion since the resulting fused multi-object density, in general, no longer belongs to the same family of the local ones, thus it cannot be utilized as prior information for the next recu...

This paper addresses fusion of labeled random finite set (LRFS) densities according to the criterion of minimum information loss (MIL). The MIL criterion amounts to minimizing the (weighted) sum of Kullback-Leibler divergences (KLDs) with the fused density appearing as righth- and argument of the KLDs. The optimal fused density following the MIL ru...

This paper addresses fusion of labeled random finite set (LRFS) densities according to the criterion of minimum information loss (MIL). The MIL criterion amounts to minimizing the (weighted) sum of Kullback-Leibler divergences (KLDs) with the fused density appearing as righthand argument of the KLDs. In order to ensure the fused density to be consi...

This article proposes a novel robust feedback linearization control scheme for affine uncertain nonlinear systems subject to matched uncertainties and constraints on the control input. In this method, instead of placing the linearized system poles at exact locations, radial paths in the open left‐hand plane are selected to freely move the poles so...

This paper deals with direct adaptive fuzzy control for uncertain affine nonlinear descriptor systems. Two cases are considered: in the first one, it is assumed that the control gain is known, while in the second one, it is an unknown-but-bounded symmetric positive definite matrix. To account for uncertainties in the system dynamics, a fuzzy system...

This paper deals with state estimation of a spatially distributed system given noisy measurements from pointwise‐in‐time‐and‐space threshold sensors spread over the spatial domain of interest. A maximum a posteriori probability (MAP) approach is undertaken and a moving horizon (MH) approximation of the MAP cost function is adopted. It is proved tha...

This paper presents a new solution for statistical fusion of multi-sensor information acquired from different fields of view, in a centralized sensor network. The focus is on applications that involve tracking unknown number of objects with time-varying states. Our solution is a track-to-track fusion method in which the information contents of post...

This paper deals with direct adaptive fuzzy control for uncertain affine nonlinear descriptor systems. Two cases are considered: in the first one it is assumed that the control gain is known while in the second one it is an unknown-but-bounded symmetric positive definite matrix. To account for uncertainties in the system dynamics, a fuzzy system is...

A stochastic reaction-diffusion model is studied on a networked support. In each patch of the network, two species are assumed to interact following a non-normal reaction scheme. When the interaction unit is replicated on a directed linear lattice, noise gets amplified via a self-consistent process, which we trace back to the degenerate spectrum of...

This paper addresses distributed registration of a sensor network for multitarget tracking. Each sensor gets measurements of the target position in a local coordinate frame, having no knowledge about the relative positions (referred to as drift parameters) and azimuths (referred to as orientation parameters) of its neighboring nodes. The multitarge...

This paper focuses on reducing communication bandwidth and, consequently, energy consumption in the context of distributed multitarget tracking (DMT) over a peer-to-peer sensor network. A consensus cardinalized probability hypothesis density (CCPHD) filter with event-triggered communication is developed by enforcing each node to broadcast its local...

This paper focuses on indirect adaptive fuzzy control of nonlinear descriptor systems described by both uncertain algebraic and differential equations aiming to guarantee asymptotic tracking of a regular and impulse-free descriptor reference model. The proposed controller exploits the universal approximation capability of Takagi–Sugeno–Kang (TSK) f...

A stochastic reaction-diffusion model is studied on a networked support. In each patch of the network two species are assumed to interact following a non-normal reaction scheme. When the interaction unit is replicated on a directed linear lattice, noise gets amplified via a self-consistent process which we trace back to the degenerate spectrum of t...

Generalized covariance intersection (GCI) has been effective in fusing multiobject densities from multiple agents for multitarget tracking and mapping purposes. From an information-theoretic viewpoint, it has been shown that GCI fusion essentially minimizes the weighted information gain (WIG) from local densities to the fused one. In this paper, th...

This paper deals with distributed registration of a sensor network for target tracking in the presence of false and/or missed measurements. Each sensor acquires measurements of the target position in local coordinates, having no knowledge about the relative positions (referred to as drift parameters) of its neighboring nodes. A distributed Bernoull...

The aim of this paper is to devise a strategy that is able to reduce communication bandwidth and, consequently, energy consumption in the context of distributed state estimation over a peer-to-peer sensor network. Specifically, a distributed Bayes filter with event-triggered communication is developed by enforcing each node to transmit its local in...

This paper addresses distributed registration of a sensor network for multitarget tracking. Each sensor gets measurements of the target position in a local coordinate frame, having no knowledge about the relative positions (referred to as drift parameters) and azimuths (referred to as orientation parameters) of its neighboring nodes. The multitarge...

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. For a linear system and noise distributions with log-concave probability density function, the proposed MH-MAP...

This paper addresses multi-agent multi-object tracking with labeled random finite sets via Generalized Covariance Intersection (GCI) fusion. While standard GCI fusion of Labeled Multi-Object (LMO) densities is labelwise and hence fully parallelizable, previous work unfortunately revealed that its fusion performance is highly sensitive to the unav...

The paper addresses Kalman filtering over a peer-to-peer sensor network with a careful eye towards data transmission scheduling for reduced communication bandwidth and, consequently, enhanced energy efficiency and prolonged network lifetime. A novel consensus Kalman filter algorithm with event-triggered communication is developed by enforcing each...

This paper proposes analytical expressions for the fusion of certain classes of labeled multi-object 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 multi-object densities. Information fusion...

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} rewirin...

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 rand...

Control of multidimensional systems on complex network: Supplementary information. (PDF)

In this paper, consensus-based Kalman filtering is extended to deal with the problem of joint target tracking and sensor self-localization in a distributed wireless sensor network. The average weighted Kullback-Leibler divergence, which is a function of the unknown drift parameters, is employed as the cost to measure the discrepancy between the fus...

This paper deals with secure state estimation against switching mode and signal attacks on cyber-physical systems, possibly affected by adversarial extra fake measurement injection. A stochastic Bayesian approach is undertaken by exploiting Bernoulli and Poisson random sets for modeling the attack existence and, respectively, fake measurements, as...

This paper deals with the simultaneous localization and mapping (SLAM) problem for autonomous vehicles or mobile robots. More specifically, a multi-vehicle scenario is considered wherein a team of vehicles explore the scene of interest in order to cooperatively construct the map of the environment by locally updating and exchanging map information...

Multidimensional systems coupled via complex networks are widespread in nature and thus frequently invoked for a large plethora of interesting applications. From ecology to physics, individual entities in mutual interactions are grouped in families, homogeneous in kind. These latter interact selectively, through a sequence of self-consistently regu...

A general scheme is proposed and tested to control the symmetry breaking instability of a homogeneous solution of a spatially extended multispecies model, defined on a network. The inherent discreteness of the space makes it possible to act on the topology of the inter-nodes contacts to achieve the desired degree of stabilization, without altering...

The paper deals with resilient state estimation of cyber-physical systems subject to switching signal attacks and fake measurement injection. In particular, the random set paradigm is adopted in order to model the switching nature of the signal attack and the fake measurement injection via Bernoulli and/or Poisson random sets. The problem of jointl...

The paper deals with decentralized state estimation for spatially distributed systems described by linear partial differential equations from discrete in-space-and-time noisy measurements provided by sensors deployed over the spatial domain of interest. A fully scalable approach is pursued by decomposing the domain into possibly overlapping subdoma...

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...

A general scheme is proposed and tested to control the symmetry breaking instability of a homogeneous solution of a spatially extended multispecies model, defined on a network. The inherent discreteness of the space makes it possible to act on the topology of the inter-nodes contacts to achieve the desired degree of stabilization, without altering...

This paper addresses 6-DOF (degree-of-freedom) tactile localization, i.e. the pose estimation of tridimensional objects given tactile measurements. This estimation problem is fundamental for the operation of autonomous robots that are often required to manipulate and grasp objects whose pose is a-priori unknown. The nature of tactile measurements,...

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, t...

Robust and performing navigation systems for Autonomous Underwater Vehicles (AUVs) play a discriminant role towards the success of complex underwater missions involving one or more AUVs. The quality of the filtering algorithm for the estimation of the AUV navigation state strongly affects the performance of the overall system. In this paper, the au...

The paper deals with decentralized state estimation for spatially distributed systems described by linear partial differential equations from discrete in-space-and-time noisy measurements provided by sensors deployed over the spatial domain of interest. A fully scalable approach is pursued by decomposing the domain into overlapping subdomains assig...

The paper addresses state estimation for linear discrete-time systems with binary (threshold) measurements. A Moving Horizon Estimation (MHE) approach is followed and different estimators, characterized by two different choices of the cost function to be minimized and/or by the possible inclusion of constraints, are proposed. Specifically, the cost...