  In many problems, complex non-Gaussian and/or nonlinear models are required
to accurately describe a physical system of interest. In such cases, Monte
Carlo algorithms are remarkably flexible and extremely powerful approaches to
solve such inference problems. However, in the presence of a high-dimensional
and/or multimodal posterior distribution, it is widely documented that standard
Monte-Carlo techniques could lead to poor performance. In this paper, the study
is focused on a Sequential Monte-Carlo (SMC) sampler framework, a more robust
and efficient Monte Carlo algorithm. Although this approach presents many
advantages over traditional Monte-Carlo methods, the potential of this emergent
technique is however largely underexploited in signal processing. In this work,
we aim at proposing some novel strategies that will improve the efficiency and
facilitate practical implementation of the SMC sampler specifically for signal
processing applications. Firstly, we propose an automatic and adaptive strategy
that selects the sequence of distributions within the SMC sampler that
minimizes the asymptotic variance of the estimator of the posterior
normalization constant. This is critical for performing model selection in
modelling applications in Bayesian signal processing. The second original
contribution we present improves the global efficiency of the SMC sampler by
introducing a novel correction mechanism that allows the use of the particles
generated through all the iterations of the algorithm (instead of only
particles from the last iteration). This is a significant contribution as it
removes the need to discard a large portion of the samples obtained, as is
standard in standard SMC methods. This will improve estimation performance in
practical settings where computational budget is important to consider.
