Multiple Model Multi-Bernoulli Filters for Manoeuvering Targets

Abstract

The cardinality balanced multitarget multi-Bernoulli (CBMeMBer) filter is a recursive, multitarget tracking mechanism based on the random finite set (RFS) theory using the finite set statistics (FISST) framework. It provides an estimate of the number of targets in a given scenario space, along with the most likely locations of those targets. It also provides this estimate without the expensive operation of multidimensional assignment between measurements and target estimates. Unlike other RFS methods, the CBMeMBer filter outputs an estimate of the actual multitarget probability density function. Current implementations include a nonlinear sequential Monte Carlo (SMC) approximation, as well as an analytical Gaussian mixture (GM) solution. A new MeMBer recursion for tracking multiple targets traveling under multiple motion models is introduced. The multiple model CBMeMBer (MM-CBMeMBer) filter presented here uses jump Markov models (JMM) to extend the standard CBMeMBer recursion to allow for multiple target motion models. This extension is implemented using both the SMC- and GM-based CBMeMBer approximations. The recursive prediction and update equations are presented for both implementations. Each multiple model implementation is validated against its respective standard CBMeMBer implementation, as well as against each other. This validation is done using a simulated scenario containing multiple manoeuvering targets. A variety of metrics, including estimate accuracy, model detection capability, and algorithm computational efficiency are used for performance evaluation. The new method is shown to improve results in several metrics with only a minor increase in computational complexity.