Distributed‐ and shared‐memory parallelizationsof assignment‐based data association formultitarget tracking

Abstract

To date, there has been a lack of efficient and practical distributed‐ and shared‐memoryparallelizations of the data association problem for multitarget tracking. Filling this gap is oneof the primary focuses of the present work. We begin by describing our data association algorithmin terms of an Interacting Multiple Model (IMM) state estimator embedded into anoptimization framework, namely, a two‐dimensional (2D) assignment problem (i.e., weightedbipartite matching). Contrary to conventional wisdom, we show that the data association (oroptimization) problem is not the major computational bottleneck; instead, the interface to theoptimization problem, namely, computing the rather numerous gating tests and IMM stateestimates, covariance calculations, and likelihood function evaluations (used as cost coefficientsin the 2D assignment problem), is the primary source of the workload. Hence, for both ageneral-purpose shared‐memory MIMD (Multiple Instruction Multiple Data) multiprocessorsystem and a distributed‐memory Intel Paragon high‐performance computer, we developedparallelizations of the data association problem that focus on the interface problem. For theformer, a coarse‐grained dynamic parallelization was developed that realizes excellent performance(i.e., superlinear speedups) independent of numerous factors influencing problemsize (e.g., many models in the IMM, denseycluttered environments, contentious target‐measurementdata, etc.). For the latter, an SPMD (Single Program Multiple Data) parallelization wasdeveloped that realizes near‐linear speedups using relatively simple dynamic task allocationalgorithms. Using a real measurement database based on two FAA air traffic control radars, weshow that the parallelizations developed in this work offer great promise in practice.