An integrated multi-sensor data fusion algorithm and autopilot iImplementation in an uninhabited surface craft

Unmanned surface vehicles (USVs) are able to accomplish difficult and challenging tasks both in civilian and defence sectors without endangering human lives. Their ability to work round the clock makes them well-suited for matters that demand immediate attention. These issues include but not limited to mines countermeasures, measuring the extent of an oil spill and locating the source of a chemical discharge. A number of USV programmes have emerged in the last decade for a variety of aforementioned purposes. Springer USV is one such research project highlighted in this paper. The intention herein is to report results emanating from data acquired from experiments on the Springer vessel whilst testing its advanced navigation, guidance and control (NGC) subsystems. The algorithms developed for these systems are based on soft-computing methodologies. A novel form of data fusion navigation algorithm has been developed and integrated with a modified optimal controller. Experimental results are presented and analysed for various scenarios including single and multiple waypoints tracking and fixed and time-varying reference bearings. It is demonstrated that the proposed NGC system provides promising results despite the presence of modelling uncertainty and external disturbances.

A Context-Dependent Algorithm for Merging Uncertain Information in Possibility Theory

The need to merge multiple sources of uncertaininformation is an important issue in many application areas,especially when there is potential for contradictions betweensources. Possibility theory offers a flexible framework to represent,and reason with, uncertain information, and there isa range of merging operators, such as the conjunctive anddisjunctive operators, for combining information. However, withthe proposals to date, the context of the information to be mergedis largely ignored during the process of selecting which mergingoperators to use. To address this shortcoming, in this paper,we propose an adaptive merging algorithm which selects largelypartially maximal consistent subsets (LPMCSs) of sources, thatcan be merged through relaxation of the conjunctive operator, byassessing the coherence of the information in each subset. In thisway, a fusion process can integrate both conjunctive and disjunctiveoperators in a more flexible manner and thereby be morecontext dependent. A comparison with related merging methodsshows how our algorithm can produce a more consensual result.

Evidential Fusion for Gender Profiling

Gender profiling is a fundamental task that helps CCTV systems to
provide better service for intelligent surveillance. Since subjects being detected
by CCTVs are not always cooperative, a few profiling algorithms are proposed
to deal with situations when faces of subjects are not available, among which
the most common approach is to analyze subjects’ body shape information. In
addition, there are some drawbacks for normal profiling algorithms considered
in real applications. First, the profiling result is always uncertain. Second, for a
time-lasting gender profiling algorithm, the result is not stable. The degree of
certainty usually varies, sometimes even to the extent that a male is classified
as a female, and vice versa. These facets are studied in a recent paper [16] using
Dempster-Shafer theory. In particular, Denoeux’s cautious rule is applied for
fusion mass functions through time lines. However, this paper points out that if
severe mis-classification is happened at the beginning of the time line, the result
of applying Denoeux’s rule could be disastrous. To remedy this weakness,
in this paper, we propose two generalizations to the DS approach proposed in
[16] that incorporates time-window and time-attenuation, respectively, in applying
Denoeux’s rule along with time lines, for which the DS approach is a special
case. Experiments show that these two generalizations do provide better results
than their predecessor when mis-classifications happen.