  A comprehensive review of the literature on manoeuvring target tracking for
both uncluttered and cluttered measurements is presented. Various discrete-time
dynamical models including non-random input, random-input and switching or
hybrid system manoeuvre models are presented. The problem of manoeuvre
detection is covered. Classical and current filtering methods for manoeuvre
tracking are presented including multi-level process noise, input estimation,
variable dimension filtering, two-stage filter, the interacting multiple model
algorithm, and generalised pseudo-Bayesian algorithms. Various extensions of
these algorithms to the case of cluttered measurements are also described and
these include: joint manoeuvre and measurement association, probabilistic data
association and multi-hypothesis tracking. Smoothing schemes, including IMM
smoothing and batch expectation-maximisation using the Viterbi algorithm, are
also described. The use of amplitude information for target measurement
discrimination is discussed. It is noted that although many manoeuvre tacking
techniques exist, the literature contains surprisingly few performance
comparisons to guide the design engineer although a performance benchmark has
recently been introduced.
