  The amplitude information of target returns has been incorporated into many
tracking algorithms for performance improvements. One of the limitations of
employing amplitude feature is that the signal-to-noise ratio (SNR) of the
target, i.e., the parameter of amplitude likelihood, is usually assumed to be
known and constant. In practice, the target SNR is always unknown, and is
dependent on aspect angle hence it will fluctuate. In this paper we propose a
hybrid labeled multi-Bernoulli (LMB) filter that introduces the signal
amplitude into the LMB filter for tracking targets with unknown and fluctuating
SNR. The fluctuation of target SNR is modeled by an autoregressive gamma
process and amplitude likelihoods for Swerling 1 and 3 targets are considered.
Under Rao-Blackwell decomposition, an approximate Gamma estimator based on
Laplace transform and Markov Chain Monte Carlo method is proposed to estimate
the target SNR, and the kinematic state is estimated by a Gaussian mixture
filter conditioned on the target SNR. The performance of the proposed hybrid
filter is analyzed via a tracking scenario including three crossing targets.
Simulation results verify the efficacy of the proposed SNR estimator and
quantify the benefits of incorporating amplitude information for multi-target
tracking.
