The SeaWolf Mid-Life Update (SWMLU) programme is a major upgrade to the UK Royal Navy's principal point defence weapon system. The update includes the addition of an Electro-Optic (EO) sensor to upgraded 'I' and 'K' band radars. The update presents a significant engineering challenge both in terms of hardware integration and software processing. The processing of sensor data into a coherent fused picture is a key element of the overall system design, and is critical to achieving the required system performance. Further to the fusion of object locations, derived object properties from both the spatial and temporal domains are also incorporated to create a highly detailed picture.
Core functionality of the data fusion process is the association of objects between sensors and the labelling of objects into targets and own missiles. The data association results have a direct influence on overall system performance and labelling accuracy of objects is crucial to satisfy the system performance requirements.
This paper discusses the data association and object labelling process followed in the SWMLU system and highlights sources of error and confusion for the EO sensor case. The effects of incorrect data associations are presented at the system-level. A number of software test environments for the EO sensor subsystem are introduced and analysed with a focus on data association.
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.