The Seawolf Mid-Life Update (SWMLU) programme is a major upgrade of the UK Royal Navy's principal point defence weapon system. The addition of an Electro-Optic sensor to the pre-existing 'I' and 'K' band radars presents a significant engineering challenge. The processing of the data from the 3 sensors into a fused picture such that a coherent view of which objects represent targets and own missiles is a key element of the overall system design and is critical to achieving the required system performance. Without this coherent view of the detected objects incorrect guidance commands will be issued to the Seawolf missiles resulting in a failure to successfully intercept the target. This paper reviews the sensor data association problem as it relates to the SWMLU system and outlines identified solution strategies. The SWMLU sensors provide complementary data that can be exploited through data association to maximise tracking accuracy as well as maintaining performance under sensor lost-lock conditions. The sensor data association approach utilises radar and EO properties from spatial and temporal domains. These characteristics are discussed in terms of their contribution to the SWMLU data association problem where it will be shown that the use of object attributes from the EO sensor and their behaviour over time is a critical performance factor.
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.
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.
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.