Multi-Sensor Data Fusion for Helicopter Guidance

Doyle, R.S. and Harris, C.J. (1995) Multi-Sensor Data Fusion for Helicopter Guidance

Abstract

Helicopter crews are required to carry out a wide range of duties most of which involve operational safety. One particular flight safety problem that has relevance to a variety of different types of rotorcraft is assisting the pilot in obstacle avoidance. Obstacles, in this context, may typically include other aircraft, terrain features, buildings, bridges, overhead cables and poles. The problem is especially difficult in bad weather conditions, (such as fog, snow, and heavy rain), in the presence of heavy smoke, or dust, or at night. Also it must be noted that the dynamics of the moving obstacles (other aircraft etc) will not in general be linear or indeed even known a priori. In order to avoid obstacles effectively a system must have knowledge about the obstacles' positions and likely future positions relative the system's own aircraft. Since the information being provided by the sensors will be imperfect, (i.e. it will have some uncertainty associated with it), and since the process model, which must be used to predict any future positions, will also be uncertain, the required positions must be robustly estimated. Since the dynamics of moving obstacles will be a priori unknown, it will be necessary to learn process models for them. Since the dynamics of the obstacles may not be linear, the process models must be capable of reflecting non-linear behaviour. The uncertain information produced by the various sensors will be related to required knowledge about the obstacles by a sensor model, however this relationship need not be linear, and may even have to be learnt. The information sources of interest may be distributed over many platforms, therefore the architecture of the data fusion system must reflect the spatially distributed nature of the problem. In essence then, the main aim of this research is the design an estimator which is capable of dealing with non-linear process and sensor models, which may result from learning processes, and with distributed information sources.

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