Simple statistical models for clutter are desirable for parametric modelling of sensors and the development of constant false alarm rate detection processing. In the case of radar sensors and sea clutter there are a few widely known and accepted 'standard' models that can be employed. For passive infra-red sensors there are fewer models and no such widely accepted model applicable to sea clutter. In this paper a statistical model for the behaviour of sea clutter in the long-wave infra-red is presented. The model is based upon many of the same assumptions that lead, in the case of radar, to the well-known and widely used K-distribution model. It is compared with real long-wave infra-red sea clutter data gathered in trials from a variety of locations.
This paper describes the use of an image query database (IQ-DB) tool as a means of implementing a validation strategy for synthetic long-wave infrared images of sea clutter. Specifically it was required to determine the validity of the synthetic imagery for use in developing and testing automatic target detection algorithms. The strategy adopted for exploiting synthetic imagery is outlined and the key issues of validation and acceptance are discussed in detail. A wide range of image metrics has been developed to achieve pre-defined validation criteria. A number of these metrics, which include post processing algorithms, are presented. Furthermore, the IQ-DB provides a robust mechanism for configuration management and control of the large volume of data used. The implementation of the IQ-DB is reviewed in terms of its cardinal point specification and its central role in synthetic imagery validation and EOSS progressive acceptance.
The addition of an advanced EO subsystem to an in-service tracker system is reviewed in terms of the sensor modelling and proving activities. For the latter, emphasis is placed on model verification and validation techniques that will lead to a validation case which will then be used to gain equipment acceptance with the UK Royal Navy. The approach to modelling encompasses parametric and image-flow models. The relationship between these different representations is described together with their interaction with the EO equipment and the project development lifecycle. The algorithms generated for the image flow model will be used as the basis for the EO subsystem detection, tracking, and data association software. Issues arising from model validation activities are addressed in detail and include the validation approach, appropriate metrics, coverage of the operational envelope and the use of synthetic imagery to augment trials data.
KEYWORDS: Sensors, Missiles, Radar, Detection and tracking algorithms, Data fusion, Electro optics, Electro optical sensors, Monte Carlo methods, Computer simulations, Algorithm development
In this paper we consider the tracking of small distant objects using Radar and Electro-Optical (EO) sensors. In particular we address the problem of data association after coalescence - this happens when two objects become sufficiently close (in angular terms) that they can no longer be resolved by the EO sensor. Some moments later they de-coalesce and the resulting detections must be associated with the existing tracks in the EO sensor. Traditionally this would be solved by making use of the velocity vectors of the objects prior to coalescence. This approach can work well for crossing objects, but when the objects are largely moving in a direction radial to the sensor it becomes problematic. Here we investigate the use of data fusion to combine Radar range with a brightness measure derived from an EO sensor to enhance the accuracy of data association. We present a number of results on the performance of this approach taking into account target motion, atmospheric conditions and sensor noise.
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