There is an increasing emphasis on the intelligent use of multiple sensor assets within military applications which is
driven by a number of factors. Firstly, the deployment of multiple, co-operative sensors can provide a much greater
situational awareness which is a key factor in military decision making at both strategic and tactical levels. Secondly,
through careful and timely asset management, military tempo and effectiveness can be maintained and even enhanced
such that the mission objectives are optimally prosecuted. Thirdly, intrinsic limitations of individual sensors and their
processing demands can be reduced or even eliminated. From a mission perspective, this renders the constraints and
frailties of the associated with the sensor network transparent to the military end users. Underpinning all of these factors
is the need to adaptively control and manipulate the various sensor search vectors in both space and time. Such a design
and operational capability is provided through Cerberus, an advanced design tool developed by Waterfall Solutions Ltd.
Within this paper, investigations into a range of different military applications using the Cerberus design environment
are reported and assessed in terms of the associated military objectives. These applications include the use of both
manned and uninhabited air vehicles as well as land and sea based sensor platforms. The use and benefits of available a
priori knowledge such as digital terrain data and mission intelligence can also be exploited within the Cerberus
environment to great military advantage.
KEYWORDS: Sensors, Algorithm development, Detection and tracking algorithms, Monte Carlo methods, Computer simulations, Data fusion, Target detection, Systems engineering, Unattended ground sensors, Systems modeling
The nature of co-operating Uninhabited Vehicle (UV) systems is such that performance enhancements are likely to be a
result of greatly increased system complexity. Complexity emerges through the interaction of multiple autonomous UVs
reacting to their current surroundings. This complexity presents a fundamental challenge to the specification, design
and evaluation of such systems, and drives the need for new approaches to the systems engineering. For applications
involving multiple autonomous UVs, research into collective and emergent behaviour offers potential benefits in terms
of improved system performance and the utilisation of individual UVs with lower processing complexity.
This paper reports on the development of a new simulation framework that addresses the systems engineering issues and
allows novel algorithms to be created and assessed. Examples are given of how the framework has been used to develop
and assess the performance of individual and multiple UVs, as well as unattended ground sensors. Furthermore, a
variety of novel algorithms developed using the framework are described and example results are provided. These
include co-operative UV missions requiring improved detection performance and the improved management of
unattended ground sensors to minimise power usage.
KEYWORDS: Sensors, Missiles, Electro optical modeling, Cameras, Radar, Data modeling, Image processing, Signal detection, Electro optics, Control systems
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.
The Kalman filter, which is optimal with respect to Gaussian distributed noisy measurements, is commonly used in the Multiple Hypothesis Tracker (MHT) for state update and prediction. It has been shown that when filtering noisy measurements distributed with asymptotic power law tails the Kalman filter underestimates the state error when the tail exponent is less than two and overestimates it when the tail exponent is greater that two. This has severe implications for tracking with the MHT which uses the estimated state error for both gating and probability calculations. This paper investigates the effects of different tail exponent values on the processes of track deletion and creation in the MHT.
This paper investigates how the targeting capability of a distributed data fusion system can be improved though the use of intelligent sensor management. The research reported here builds upon previous results from QinetiQ's air-to-ground fusion programme and sensor management research. QinetiQ's previously reported software test-bed for developing and evaluating data fusion algorithms has been enhanced to include intelligent sensor management functions and weapon fly-out models. In this paper details of the enhancements are provided together with a review of the sensor management algorithms employed. These include flight path optimization of airborne sensors to minimize target state estimation error, sensor activation control and sightline management of individual sensors for optimal targeting performance. Initial results from investigative studies are presented and conclusions are drawn.
KEYWORDS: Data fusion, Filtering (signal processing), Error analysis, Sensors, Target recognition, Matrices, Target detection, Detection and tracking algorithms, Global Positioning System, Monte Carlo methods
This paper examines the requirement for accurate estimates of the statistical correlations between measurements in a distributed air-to-ground targeting system. The study uses results from a distributed multi-platform targeting simulation based on a level-1 data fusion system to assess the extent to which correlated measurements can degrade system performance, and the degree to which these effects need to be included to obtain a required level of accuracy. The data fusion environment described in the paper incorporates a range of target tracking and data association algorithms, including several variants of the standard Kalman filter, probabilistic association techniques and Reid's multiple hypothesis tracker. A variety of decentralized architectures are supported, allowing comparison with the performance of equivalent centralized systems. In the analysis, consideration is given to constraints on the computational complexities of the fusion system, and the availability of estimates of the measurement correlations and platform-dependent biases. Particular emphasis is placed on the localisation accuracy achieved by different algorithmic approaches and the robustness of the system to errors in the estimated covariance matrices.
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