We consider approximate inference in a class of switching linear Gaussian State Space models which includes the switching Kalman Filter and the more general case of switch transitions dependent on the continuous hidden state. The method is a novel form of Gaussian sum smoother consisting of a single forward and backward pass, and compares favourably against a range of competing techniques, incl...

In this paper we consider a problem of blind co-channel signal separation, the goal of which is to estimate multiple co-channel digitally modulated signals using an antenna array. We consider the joint maximum likelihood estimation [1] and present a sequential algorithm, which is referred to as sequential joint maximum likelihood (SJML) algorithm. In addition we also apply the sequential least ...

Orbit determination in application to the estimation of impact probability has the goal of determining the evolution of the state probability density function (pdf) and determining a measure of the probability of collision. Nonlinear gravitational interaction and non-conservative forces can make the pdf far from Gaussian. This work implements three nonlinear sequential estimators: the Extended ...

Identifying a maximally-separated set of signals is important in the design of modems. The notion of optimality is dependent on the model chosen to describe noise in the measurements; while some analytic results can be derived under the assumption of Gaussian noise, no such techniques are known for choosing signal sets in the non-Gaussian case. To obtain numerical solutions for non-Gaussian det...

In this paper, we consider the noncoherent code acquisition problem using sequential schemes. To alleviate the computational complexity of the maximum likelihood method, simplified schemes are proposed and analyzed for the truncated sequential probability ratio test. The performance of the simplified and original schemes are compared in additive white Gaussian noise and slowly varying fading ch...

Nonlinear non-Gaussian state-space models arise in numerous applications in statistics and signal processing. In this context, one of the most successful and popular approximation techniques is the sequential Monte-Carlo (SMC) algorithm, also known as the particle filter. Nevertheless, this method tends to be inefficient when applied to high-dimensional problems. In this chapter, we present, an...

The aim of this study was to determine the extent of metal pollutions and the identification of their major sources in the vicinity of the Sangan iron mine occurring in NE Iran. Soil samples were collected from the vicinity of the mine site and analyzed for heavy metals. In addition, the chemical speciation of these metals was investigated by means of the sequential extraction procedure. The st...

The adaptive TAP Gibbs free energy for a general densely connected probabilistic model with quadratic interactions and arbritary single site constraints is derived. We show how a specific sequential minimization of the free energy leads to a generalization of Minka’s expectation propagation. Lastly, we derive a sparse representation version of the sequential algorithm. The usefulness of the app...

We develop a simulation-based method for the online updating of Gaussian process regression and classification models. Our method exploits sequential Monte Carlo to produce a thrifty sequential design algorithm, in terms of computational speed, compared to the established MCMC alternative. The latter is less ideal for sequential design since it must be restarted and iterated to convergence with...