Sofia Giompapa

Least Squares Estimation and Cramér–Rao Type Lower Bounds for Relative Sensor Registration Process

This paper concerns the study of the Cramér-Rao type lower bounds for relative sensor registration (or grid-locking) problem. The theoretical performance bound is of fundamental importance both for algorithm performance assessment and for prediction of the best achievable performance given sensor locations, sensor number, and accuracy of sensor measurements. First, a general description of the relative grid-locking problem is given. Afterwards, the measurement model is analyzed. In particular, the nonlinearity of the measurement model and all the biases (attitude biases, measurement biases, and position biases) are taken into account. Finally, the Cramér-Rao lower bound (CRLB) is discussed and two different types of CRLB, the Hybrid CRLB (HCRLB) and the Modified CRLB (MCRLB), are calculated. Theoretical and simulated results are shown.

Maritime border control multisensor system

This focuses on the classification task performed into a multi-sensor system for the coastal surveillance. The system is composed of two platforms of sensors: a land-based platform equipped with a land based radar, an Automatic Identification System (AIS) and an infrared camera (IR); an airborne platform carrying an airborne radar that can operate in a spotlight Synthetic Aperture Radar (SAR) mode, a video camera, and a second IR camera. The tasks performed by the system are the detection, tracking, identification, and classification of multiple targets, the evaluation of their threat level, and the selection of an intervention on them. The classification algorithm implemented inside the system exploits an analytical approach based on the confusion matrix (CM) of the imaging sensors that belong to the system. Some measures of effectiveness (MoE) of the system are evaluated, considering both cases where an ideal error-free classification process and a non-ideal classification process are performed.

On the identifiability problem in the presence of random nuisance parameters

This paper concerns with the identifiability of a vector of unknown deterministic parameters. In many practical applications, the data model is affected by additional random parameters whose estimation is not strictly required, the so-called nuisance parameters. In these cases, the classical definition of identifiability, which requires the calculation of the Fisher Information Matrix (FIM) and of its rank, is often difficult or impossible to perform. Instead, the Modified Fisher Information Matrix (MFIM) can be computed. We generalize the main results on the identifiability problem to take the presence of random nuisance parameters into account. We provide an alternative definition of identifiability that can be always applied but that is weaker than the classical definition, and we investigate the relationships between the identifiability condition and the MFIM. Finally, we apply the obtained results to the identifiability in presence of nuisance parameters to the relative grid-locking problem for netted radar system.

Computer Simulation of an Integrated Multi-Sensor System for Maritime Border Control

This work describes a computer simulator for an integrated command and control (C2) multi-sensor system acting in a maritime border control scenario. The analyzed system is composed of two platforms of multiple sensors: a land based platform, equipped with a vessel traffic system (VTS) radar, an infrared camera (IR) and an automatic identification system (AIS); an airborne platform, carrying an airborne early warning radar (AEWR) and an IR camera. The mission of the system is the detection, tracking and identification of multiple naval targets inside a sea region, their threat level evaluation and the selection of an intervention against possible threat targets, in order to inspect their nature. The measures of effectiveness (MoE) of the integrated system are evaluated, i.e. the system performance during the detection, the threat evaluation process and the intervention.

A Model for a Human Decision-Maker in a Command and Control Radar System: Surveillance Tracking of Multiple Targets

This work presents a deterministic approach to the problem of modelling the human behaviour in a command and control radar system and it considers the fusion of information between the operator and the system. The implementation and the results of a case study are presented where a human operator performs a tracking operation of multiple targets in a sea region. The mission performed by the operator is the surveillance of a coast area and the selection of a system action against possible threat targets, in order to check their identity. An analytical model of human memory has been investigated where the human decision maker is represented as a subsystem involved with two operational blocks, corresponding to the situation assessment process and the response selection process that he performs. The operator performance is evaluated by mean of his error probability in these two processes

Naval target classification by fusion of IR and EO sensors

This paper describes the classification function of naval targets performed by an infrared camera (IR) and an electro-optical camera (EO) that operate in a more complex multisensor system for the surveillance of a coastal region. The following naval targets are considered: high speed dinghy, motor boat, fishing boat, oil tanker. Target classification is automatically performed by exploiting the knowledge of the sensor confusion matrix (CM). The CM is analytically computed as a function of the sensor noise features, the sensor resolution, and the dimension of the involved image database. For both the sensors, a database of images is generated exploiting a three-dimensional (3D) Computer Aided Design (CAD) of the target, for the four types of ship mentioned above. For the EO camera, the image generation is simply obtained by the projection of the 3D CAD on the camera focal plane. For the IR images simulation, firstly the surface temperatures are computed using an Open-source Software for Modelling and Simulation of Infrared Signatures (OSMOSIS) that efficiently integrates the dependence of the emissivity upon the surface temperature, the wavelength, and the elevation angle. The software is applicable to realistic ship geometries. Secondly, these temperatures and the environment features are used to predict realistic IR images. The local decisions on the class are made using the elements of the confusion matrix of each sensor and they are fused according to a maximum likelihood (ML) rule. The global performance of the classification process is measured in terms of the global confusion matrix of the integrated system. This analytical approach can effectively reduce the computational load of a Monte Carlo simulation, when the sensors described here are introduced in a more complex multisensor system for the maritime surveillance.

Tartaglia-Pascal’s triangle: a historical perspective with applications

The aim of this paper is to provide a historical perspective of Tartaglia-Pascal’s triangle with its relations to physics, finance, and statistical signal processing. We start by introducing Tartaglia’s triangle and its numerous properties. We then consider its relationship with a number of topics: the Newton binomial, probability theory (in particular with the Gaussian probability density function, pdf), the Fibonacci sequence, the heat equation, the Schrödinger equation, the Black–Scholes equation of mathematical finance and stochastic filtering theory. Thus, the main contribution of this paper is to present a systematic review of the triangle properties, its connection to statistical theory, and its numerous applications. The paper has mostly a scientific-educational character and is addressed to a wide circle of readers. Sections 7 and 8 are more technical; thus, they may be of interest to more expert readers.

Impact of Flight Disturbances on Airborne Radar Tracking

This correspondence is devoted to the study of the impact of flight disturbances due to the atmospheric turbulence on airborne radar tracking. The performance of a turbulence-ignorant tracking filter is assessed in a turbulent simulated scenario. The turbulence is modelled according to the Dryden model, a correlated, zero-mean, Gaussian random process. A quasi-ellipsoidal racetrack course is chosen for the air platform that carries the radar. The performance of the turbulence-ignorant tracking algorithm is analyzed in terms of mean value and standard deviation of the estimation errors for each component of the position and velocity vectors and compared with the posterior Cramer-Rao lower bound (PCRLB), evaluated for the ideal case of absence of platform vibrations.

Study of the classification task into an integrated multisensor system for maritime border control

This work focuses on the classification task performed into a multi-sensor system for the coastal surveillance. The system is composed of two platforms of sensors: a land based platform, equipped with a land based radar, an automatic identification system (AIS) and an infrared camera (IR); an airborne platform, carrying an airborne radar that can operate in a spotlight synthetic aperture radar (SAR) mode, a video camera, and a second IR camera. The tasks performed by the system are the detection, tracking, identification, and classification of multiple targets, the evaluation of their threat level, and the selection of an intervention on them. The classification algorithm implemented inside the system exploits an analytical approach based on the confusion matrix (CM) of the imaging sensors that belong to the system. Some measures of effectiveness (MoE) of the system are evaluated, considering both the cases where an ideal error free classification process and a non-ideal classification process are performed.

Maritime border control computer simulation

This work describes a computer simulator for an integrated command and control (C2) multi-sensor system acting in a maritime border control scenario. The analyzed system is composed of two platforms of multiple sensors: a land-based platform, equipped with a Vessel Traffic System (VTS) radar, an Infrared camera (IR), and an Automatic Identification System (AIS); an airborne platform, carrying an Airborne Early Warning Radar (AEWR) and an IR camera. The mission of the system is the detection, tracking, and identification of multiple naval targets inside a sea region, their threat level evaluation, and the selection of an intervention against possible threat targets, in order to inspect their nature. The Measures of Effectiveness (MoE) of the integrated system are evaluated, i.e., the system performance during the detection, the threat evaluation process, and the intervention.