Distributed Sensor Networks for Scene Analysis in GPS Denied Environments

Active and passive scene mapping and exploration of a region of interest using multiple- heterogeneous sensors on multiple airborne moving platforms is an important problem in many defence and civilian applications. It has many applications ranging from multi-target tracking (MTT), classification, navigation, surveying and mapping, and many others. Sensor modalities used in such problems may include electronic support measures (ESM), multiple-radars (including moving target indicators (MTI), infra-red (providing high-frame rate bearing estimates), and electro-optics (EO) including Lidar and camera-based systems. Each sensor provides varying degrees of accuracy, response time, and performance, and indeed the data acquired by one sensor can improve the accuracy of another.
In GPS denied environments, sensing from an array of sensor arrays is challenging as the location of the sensor node is crucial information. Moreover, dynamic placement of a sensor array to enhance scene analysis through sensor management by, for example, making a designated movement of an uncrewed aerial vehicle (UAV), also depends on self-localisation data. Although dead-reckoning techniques can help, simultaneous localisation and tracking (or mapping) are key algorithmic techniques.
Sensor fusion is also a major problem, whereby multiple heterogeneous sensors may be co-located on a single platform, or distributed across many platforms, with central or distributed data fusion, and with each configuration offering their own challenges and opportunities. In particular, finding the optimal trade-off between a distributed processing approach, in which information is exchanged directly between sensors, and a centralised fusion for delivering high-level inference to the operator. For information exchanged between sensors, it is crucial to understand the capability of the system in the presence of interference (from active jamming to weather conditions) and incorporating additional knowledge that can indicate the degradation in the sensor's performance.
Although there is a plethora of different combinations of sensing configurations, implicit problems, and potential solutions to each scenario, there are several common-themes to each of these sensor fusion and management problems. These include
1. understanding how to optimally quantify and incorporate auxiliary information, such as meteorology reports, models of target manoeuvres, any expected constraints on trajectories (for example civilian flight paths), and measurement reliability;
2. understanding what information between heterogenous sensors should be exchanged
directly to enhance optimal sensing and detection, and how the fusion centre will incorporate the available information;
3. understanding efficient algorithms for enabling scene analysis and mapping (including target
tracking, detection, and classification).

Although difficult to incorporate everything that is desired in these systems, the design of the sensor network can generally be expressed in terms of probabilistic graphical models for multi-target tracking.
This PhD project will use recent advances in Bayesian inference techniques using scalable and flexible message-passing framework, in which auxiliary information can be incorporated, and data association and hyper-parameter estimation is implicitly achieved. This PhD will be concerned with the best representation for auxiliary information, representations for sensor hyper-parameters, and investigation of how mixed discrete-and-continuous variables can be estimated. This project will build on and compliment the underpinning work in UDRC Phase 3 WP1.2.