Result: An adaptive resource allocation architecture applied to line tracking

Current line tracker approaches only address resources in the form of memory and processing time requirements. These architecture approaches limit the amount of complex algorithmic processing a line tracker may perform to provide the correct solution. Past approaches include single-hypothesis Kalman filter-based tracking and a-b tracking. Both approaches assume a real time recursive data processing requirement, and are fairly memory intensive. These architectures are not expandable. Recent research has demonstrated the benefits of a multiple hypothesis, multiple model sonar line tracking solution, achieved at significant computational cost. We have developed an adaptive architecture that trades computational resources for algorithm complexity based on environmental conditions. A Fuzzy Logic Rule-Based approach is applied to adaptively assign algorithmic resources to meet system requirements. The resources allocated by the Fuzzy Logic algorithm include (a) the number of hypotheses permitted (yielding multi-hypothesis and single-hypothesis modes), (b) the number of signal models to use (yielding an interacting multiple model capability), (c) a new track likelihood for hypothesis generation, (d) track attribute evaluator activation (for signal to noise ratio, frequency bandwidth, and others), and (e) adaptive cluster threshold control. Algorithm allocation is driven by a comparison of current throughput rates to a desired real time rate. The Fuzzy Logic Controlled (FLC) line tracker, a single hypothesis line tracker, and a multiple hypothesis line tracker are compared on real sonar data. System resource usage results demonstrate the utility of the FLC line tracker.