Abstract:

Multiple hypothesis tracking(MHT) is a Bayesian association method that evaluates association hypotheses among multiple scans and makes evaluation based decisions. Comparing with the single hypothesis method, MHT can work reasonably under 10~100 times lower signal-noise ratio (SNR) but it needs much more computational load. The implementation of track-oriented MHT (TOMHT) is studied and some key points are investigated, include calculating the track score, generating the track tree, modeling track clustering, hypotheses generating as problems in graph theory and N-scan pruning, etc. In the TOMHT framework, an adaptive association depth (AAD) method is proposed. This method makes the association depth change adaptively with the complexity of scenarios. Its performance is investigated by several simulation experiments on tracking closely targets. The results and analysis show that the performance of AAD-MHT is nearly the same as MHT but the computational load is much lower.