Naval Target Classification by Fusion of Multiple Imaging Sensors Based on the Confusion Matrix

This paper presents an algorithm for the classification of targets based on the fusion of the class information provided by different imaging sensors. The outputs of the different sensors are combined to obtain an accurate estimate of the target class. The performance of each imaging sensor is modelled by means of its confusion matrix (CM), whose elements are the conditional error probabilities in the classification and the conditional correct classification probabilities. These probabilities are used by each sensor to make a decision on the target class. Then, a final decision on the class is made using a suitable fusion rule in order to combine the local decisions provided by the sensors. The overall performance of the classification process is evaluated by means of the "fused" confusion matrix, i.e. the CM pertinent to the final decision on the target class. Two fusion rules are considered: a majority voting (MV) rule and a maximum likelihood (ML) rule. A case study is then presented, where the developed algorithm is applied to three imaging sensors located on a generic air platform: a video camera, an infrared camera (IR), and a spotlight Synthetic Aperture Radar (SAR)

An identifiability criterion in the presence of random nuisance parameters

This paper concerns with the identifiability of an unknown deterministic vector in the presence of random nuisance parameters. In these cases, the classical definition of identifiability, which requires calculation of the Fisher Information Matrix (FIM) and of its rank, is often difficult or impossible to be implemented. Instead, the Modified FIM (MFIM) can be usually computed. We generalize the main results on parameter identifiability to take the presence of random nuisance parameters into account. We provide an alternative definition of identifiability that can be always applied also in the presence of nuisance parameters and we investigate the relationships between the classical and the new identifiability conditions. Finally, the new definition of identifiability is applied to a common estimation problem in netted radar systems: the relative grid-locking problem

On the Application of the Expectation-Maximization Algorithm to the Relative Grid-Locking Problem

An important prerequisite for successful multisensory integration is that the data from the reporting sensors are trans- formed to a common reference frame free of systematic or registration bias errors. If not properly corrected, the registration errors can seriously degrade the global surveillance system performance. The relative sensor registration (or grid-locking) process aligns remote data to local data under the assumption that the local data are bias free and that all biases reside with the remote sensor. In this paper, we take into account all registration errors involved in the grid-locking problem. An EM-based estimator of these bias terms is derived and its statistical performance compared to the hybrid Cramer-Rao lower bound (HCRLB)

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

An important prerequisite for successful multisensor integration is that the data from the reporting sensors are transformed to a common reference frame free of systematic or registration bias errors. If not properly corrected, the registration errors can seriously degrade the global surveillance system performance by increasing tracking errors and even introducing ghost tracks. The relative sensor registration (or grid-locking) process aligns remote data to local data under the assumption that the local data are bias free and that all biases reside with the remote sensor. In this paper, we consider all registration errors involved in the grid-locking problem, i.e., attitude, measurement, and position biases. A linear least squares (LS) estimator of these bias terms is derived and its statistical performance compared to the hybrid Cramér-Rao lower bound (HCRLB) as a function of sensor locations, sensors number, and accuracy of sensor measurements

An Expectation-Maximization-Based Approach to the Relative Sensor Registration for Multi-Target Scenario

An important prerequisite for successful multisensor integration is that the data from the reporting sensors are transformed to a common reference frame free of systematic or registration bias errors. If not properly corrected, the registration errors can seriously degrade the global surveillance system performance. The relative sensor registration (or grid-locking) process aligns remote data to local data under the assumption that the local data are bias free and that all biases reside with the remote sensor. In this paper we consider a multi-target scenario and we address here the problem of jointly estimating all registration errors involved in the grid-locking problem. An Expectation-Maximization (EM) estimator of all bias errors is derived and its statistical performance compared to the hybrid Cramér-Rao lower bound (HCRLB