This paper compares several particle filtering variants for speech feature enhancement in non-stationary noise environments. By analyzing the random processes of clean speech, noise and noisy speech, appropriate proposal densities are derived. The performances of the resulting particle filters, i.e. modified Sampling-ImportanceResampling (mod-SIR), auxiliary SIR and likelihood particle filter, ...

An integrity monitoring system is an inseparable part of global positioning system (GPS). According to the measurement noise feature of GPS receiver and the degeneracy phenomenon and alleviating the sample impoverishment problem in particle filter (PF). An approach to fault detection and isolation (FDI) for GPS receiver autonomous integrity monitoring (RAIM) based on genetic resampling particle...

Reliability of a navigation system is one of great importance for navigation purposes. Therefore, an integrity monitoring system is an inseparable part of aviation navigation system. Failures or faults due to malfunctions in the systems should be detected and repaired to keep the integrity of the system intact. According to the characteristic of GPS (Global Positioning System) receiver noise di...

The particle filter method has been used in data assimilation problems for estimating states of nonlinear dynamic systems when both system errors and observation errors are present, and are possibly non-Gaussian. Weighted particles are used to represent the probability density function of the system states at any time. The particles are propagated through the system evolvement and the weights a...

We propose, for the superpositional sensor scenario, a hybrid between the multi-Bernoulli filter and the cardinalized probability hypothesis density (CPHD) filter. We use a multi-Bernoulli random finite set (RFS) to model existing targets and we use an independent and identically distributed cluster (IIDC) RFS to model newborn targets and targets with low probability of existence. Our main cont...

Selecting appropriately the proposal kernel of particle filters is an issue of significant importance, since a bad choice may lead to deterioration of the particle sample and, consequently, waste of computational power. In this paper we introduce a novel algorithm approximating adaptively the so-called optimal proposal kernel by a mixture of integrated curved exponential distributions with logi...

Abstract- Tracking a target which is sensed by a collection of randomly deployed, limited-capacity, and short-ranged sensors is a tricky problem and, yet applicable to the empirical world. In this paper, this challenge has been addressed a by introducing a nested algorithm to track a maneuvering target entering the sensor field. In the proposed nested algorithm, different modules are to fulfill...

The particle filter (PF) is a novel technique that has sufficiently good estimation results for the nonlinear/non-Gaussian systems. However, PF is inconsistent that caused mainly by loss of particle diversity in resampling step and unknown a priori knowledge of the noise statistics. This paper introduces a new modified particle filter called adaptive unscented particle filter (AUPF) to overcome th...