Multitarget Tracking Using Multiple Hypothesis Tracking

The track‐oriented multiple hypothesis tracking (MHT) algorithm is one of the most advanced algorithms for multisensor Multitarget tracking (MTT) for real‐world complex problems. An MTT system uses one or more sensors such as radar, sonar, electro‐optical, video, infra‐red, multispectral, hyperspectral, and unattended ground sensor (acoustic and seismic). Two classes of Sequential Monte Carlo (SMC)‐based multitarget algorithms are commonly used: Particle filter (PF)‐based algorithms; and Markov Chain Monte Carlo (MCMC)‐based algorithms. MHT is particularly effective in dense multitarget settings and in highly cluttered environments. The chapter discusses the current nonlinear filtering algorithms. Suitable filters can be selected for the track filter given the nature and complexity of the tracking problem. A collection of dynamic and measurement models commonly used in filters are presented for a wide variety of tracking applications. The chapter presents a detailed hybrid‐state derivation of the track‐oriented MHT equations following the formulation by Kurien with some minor modifications.