Research Areas

MSL is a highly active research group which is principally concerned with problems associated with the control of sensing systems, including bandwidth allocation and distributed computing for networks of cooperating sensors. These research areas span topics in information theory, control theory, target tracking and data fusion, communications and signal processing. Our philosophy is to determine new fundamental results and then use these to guide the development of practical solutions to practical problems.

MSL's achievements can be grouped into four main areas which are described below. Details of current and past projects in these fields can be found by following the links on the Projects

Sensor Signal Processing

The recent technological advances in sensing hardware requires sophisticated processing techniques to take full advantage of their capabilities. Here the focus is to make use of information theory, statistical signal processing and physics to develop algorithms to extract useful information for the detection, characterisation and recognition of objects in noisy environments.

See also: Current projects in Sensor Signal Processing

Adaptive Sensor Networks

Advances in sensor technologies, computation devices and algorithms have created enormous opportunities for significant improvements to the task of building a coherent picture of an environment over large areas. Unfortunately, as information requirements grow, conventional network processing techniques require ever-increasing bandwidth between sensors and processors, as well as potentially exponentially complex methods for extracting information from the data. To address these problems it is necessary that sensing and computation be jointly engineering.

At MSL we take the perspective that a sensor system is a, possibly distributed, collection of sensing components with limitations imposed by power, bandwidth, computing resources, placement and other physical constraints. This sensor system collects measurements from an evolving scene, and over time and within its limitations, adapts its performance to the scene in order to optimally extract information from the environment. The focus here is on the development of adaptive sensor scheduling and sensor management algorithms.

See also: Current projects in Adaptive Sensor Networks

Situation Awareness

Situation awareness is the process of identifying and quantifying threats to the attainment of one's objective. More than two millennia of military history attest to the enduring importance of situation awareness. Until the advent of automation, situation awareness was consider a product of training and experience. Recent advances in sensors and networking means operators are now confronted with large volumes of data that may change rapidly. Drawing rapid inferences from such large masses of data now exceeds human capabilities. Thus, there is a need for reliable automated inference under uncertainty — this is the principal requirement of situation awareness. Our work in this field is designed to deliver robust, automated inferential support tools to the human decision-maker, thereby releasing him to focus on the most demanding issues.

See also: Current projects in Situation Awareness

Target Tracking

Target tracking is a fundamental tool for any single or multi-sensor surveillance system. Such sensor systems, e.g. radars or sonars, report measurements from many diverse sources, only some of which are from the objects of interest. Target tracking algorithms must be capable of detecting, locating and often identifying these objects of interest. In the case of multi-sensor system, tracking algorithms must also be capable of registering the data from each sensor system and fusing it to create a single, coherent picture. Our work in this field is on designing target tracking algorithms that are both computationally efficient and accurate.

See also: Current projects in Target Tracking