Mapping the Kalman tracking algorithm onto the transputer network

The Kalman tracking algorithm estimates position, velocity, and acceleration 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 tasks onto the multiprocessor system to minimize the time needed to complete all the tasks is an NP hard problem, and it arises when the task dependency 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 efficient mapping of 3D-3S parallel Kalman tracking algorithm onto the network of 10 transputers, which are connected in tree structure that achieves a speedup of 6 is presented here.