Zbigniew Korona

Model-based fusion for multisensor target recognition

A multisensor feature-based fusion approach to target recognition using a framework of model-theory is proposed. The best discrimination basis algorithm (BDBA) based on the best basis selection technique and the sensory data fusion system (SDFS) based on logical models and theories are applied for feature extraction. The BDBA selects the most discriminant basis. The SDFS first selects features, which are interpretable in terms of symbolic knowledge about the domain, from the most discriminant basis determined for each sensor separately. Then, it fuses these features into one combined feature vector. The SDFS uses formal languages to describe the domain and the sensing process. Models represent sensor data, operations on data, and relations among the data. Theories represent symbolic knowledge about the domain and about the sensors. Fusion is treated as a goal-driven operation of combining languages, models, and theories related to different sensors into one combined language, one combined model of the world and one combined theory. The results of our simulations show that the recognition accuracy of the proposed automatic multisensor feature based recognition system (AMFRS) is better than the recognition accuracy of a system that performs recognition using most discriminant wavelet coefficients (MDWC) as features. The AMFRS utilizes a model-theory framework (SDFS) for feature selection, while MDWC are selected from all the most discriminant bases determined for each sensor using a relative entropy measure.

A formal approach to the design of feature-based multi-sensor recognition systems

Abstract This paper shows an example of developing a fusion system in a formal framework, i.e., through the use of formal operators in the development process. Two main concepts of formal methods are theories and models. In our approach, the development of a fusion system consists of operations on theories and models. We show, on a simple example, how theories and models are combined in the process of designing a fusion system. We also compare the performance of a system developed according to our approach with a more traditional system.

Multisensor integration in the tracking of landing aircraft

An algorithm is presented for tracking a landing aircraft using two different passive sensors, a laser range finder (LRF) and an infrared camera (FLIR). The main feature of this algorithm is its ability to identify and compensate for plume disturbance. The algorithm is based on the extended Kalman filter (EKF) and the filtering confidence function (FCF) which introduces a learning approach to the tracking problem. The results of a simulation using the learning tracking algorithm and the extended Kalman filter alone are presented and compared.<<ETX>>

Wavelet-based recognition using model theory for feature selection

An increase in accuracy and reduction in computational complexity of the common wavelet- based target recognition techniques can be achieved by using interpretable features for recognition. In this work, the Best Discrimination Basis Algorithm (BDBA) is applied to select the most discriminant complete orthonormal wavelet basis for recognition purposes. The BDBA uses a relative entropy criterion as a discriminant measure. Then, interpretable features are selected from the most discriminant basis by utilizing symbolic knowledge about the domain. The domain theory that contains this symbolic knowledge is implemented in a backpropagation neural network. The output of the backpropagation neural network gives a final recognition decision. The results of our simulations show that the recognition accuracy of the proposed Automatic Feature Based Recognition System (AFBRS) is better than the recognition accuracy of a system that performs recognition using the Most Discriminant Wavelet Coefficients (MDWC).

A fusion and learning algorithm for landing aircraft tracking: compensating for exhaust plume disturbance

An algorithm is presented for tracking a landing aircraft using fusion of two different passive sensors, a laser range finder (LRF) and a forward-looking infrared (FLIR) camera. The main feature of this algorithm is its ability to identify and compensate for an exhaust plume disturbance. The algorithm is based on the extended Kalman filter (EKF) and the filtering confidence function (FCF) which introduces a learning approach to the tracking problem. The results of a simulation using the learning tracking algorithm and the EKF alone are presented and compared. >

Q2 Symbolic Reasoning about Noisy Dynamic Systems

Symbolic reasoning about continuous dynamic systems requires consistent qualitative abstraction functions and a consistent symbolic model. Classically, symbolic reasoning systems have utilized a box partition of the system space to achieve qualitative abstraction, but boxes can not provide a consistent abstraction. Our Q2 methodology abstracts a provably consistent symbolic representation of noise-free general dynamic systems. However, the Q2 symbolic representation has not been previously evaluated for efficacy in the presence of noise. We evaluate the effects of noise on Q2 symbolic reasoning in the domain of maneuver detection. We demonstrate how the Q2 methodology derives a symbolic abstraction of a general dynamic system model used in evaluating maneuver detectors. Simulation results represented by ROC curves show that the Q2 based maneuver detector is superior to a box-based detector. While no method is consistent in the presence of noise, the Q2 methodology is superior to the classic boxs approach for deriving qualitative decisions about noisy dynamic systems.

Model theory based fusion framework with application to multisensor target recognition

In this work, we present a model theory based fusion methodology for multisensor wavelet-features based recognition called automatic multisensor feature-based recognition system (AMFRS). The goal of this system is to increase accuracy of the commonly used wavelet-based recognition techniques by incorporating symbolic knowledge (symbolic features) about the domain and by utilizing a model theory based fusion framework for multisensor feature selection.

Multiresolutional multisensor target identification

An algorithm for multiresolutional multisensor target identification is presented. It uses the computationally efficient scale sequential approach to hypothesis testing during identification and implements the inverse discrete wavelet transform to fuse data from different multiresolution sensors. The authors show that fusion leads to an increase in the probability of correct identification without a significant increase in the number of computations.<<ETX>>

Landing aircraft tracking using fusion of FLIR and laser range finder

An algorithm is presented for tracking a landing aircraft using two different passive sensors, a laser range finder and an infrared camera. The main feature of this algorithm is that it is able to identify and compensate for abrupt disturbances. The algorithm is based on the extended Kalman filter (EKF) and the filtering confidence function (FCF) which introduces a learning approach to the tracking problem. The results of simulation using this learning tracking algorithm and the extended Kalman filter alone are presented and compared.