MAPPING THE KALMAN TRACKING ALGORITHM ONTO THE TRANSPUTER NETWORK

The Kalman tracking algorithm estimates position, velocity, and accele ration of a target from noisy measurement. A parallel Kalman algorithm is derived using the row-column partitioning with the modified state vector representation for multiprocessor realization. Mapping the task s onto the multiprocessor system to minimize the time needed to comple te all the tasks is an NP hard problem, and it arises when the task de pendency structure of a parallel algorithm differs from the processor interconnection topology or when the number of processes generated by the algorithm exceeds the number of processors available. The efficien t mapping of 3D-3S parallel Kalman tracking algorithm onto the network of 10 transputers, which are connected in tree structure that achieve s a speedup of 6 is presented here.