Castro, J.; Vila, J.; Moragrega, A.; Closas, P.; Fernandez, J. International journal of distributed sensor networks Vol. 13, num. 8 DOI: 10.1177/1550147717722158 Data de publicació: 2017-08-29 Article en revista
One of the major challenges in Bayesian filtering is the curse of dimensionality. The quadrature Kalman filter (QKF) is the method of choice in many real-life Gaussian problems, but its computational complexity increases exponentially with the dimension of the state. As a promising solution to overcome the filter limitations in such scenarios, we further explore the multiple state-partitioning approach, which considers the partition of the original space into several subspaces, with the goal to apply a low-dimensional filter at each partition. In this contribution, the key idea is to take advantage of the estimation uncertainty provided by the QKF to improve the interaction among filters and avoid the point estimate approximation performed in the original Multiple QKF (MQKF). The new filter formulation, named Improved MQKF, considers Gauss-Hermite quadrature rules to propagate the subspaces of interest, together with cubature rules for marginalization purposes. The nested quadrature-cubature approximation provides robustness and improves the filter performance. Simulation results for a multiple target tracking scenario are provided to support the discussion.
Wireless localization by time-of-arrival (TOA) measurements is typically corrupted by non-line-of-sight (NLOS) conditions, causing biased range measurements that can degrade the overall positioning performance of the system. In this article, we propose a localization algorithm that is able to mitigate the impact of NLOS observations by employing a heavy-tailed noise statistical model. Modeling the observation noise by a skew t-distribution allows us to, on the one hand, employ a computationally light sigma-point Kalman filtering method while, on the other hand, be able to effectively characterize the positive skewed non-Gaussian nature of TOA observations under LOS/NLOS conditions. Numerical results show the enhanced performance of such approach.
Castro, J.; Vila, J.; Closas, P.; Fernandez, J. Asilomar Conference on Signals, Systems, and Computers p. 706-710 DOI: 10.1109/ACSSC.2014.7094539 Data de presentació: 2014-11-04 Presentació treball a congrés
Received Signal Strength (RSS) localization is widely
used due to its simplicity and availability in most mobile devices.
The RSS channel model is defined by the propagation losses
and the shadow fading. These parameters might vary over time
because of changes in the environment. In this paper, the problem
of tracking a mobile node by RSS measurements is addressed,
while simultaneously estimating a two-slope RSS model. The
methodology considers a Kalman filter with Interacting Multiple
Model architecture, coupled to an on-line estimation of the
observation’s variance. The performance of the method is shown
through numerical simulations in realistic scenarios.
Bayesian ltering appears in many signal processing problems,reason why it attracted the attention of many researchers to develop efficient algorithms, yet computationally
a ordable. In many real systems, it is appropriate to consider α-stable noise distributions to model possible outliers
or impulsive behavior in the measurements. In this paper, we consider a nonlinear state-space model with Gaussian process noise and symmetric α-stable measurement noise. To obtain a robust estimation framework we consider that both process and measurement noise statistics are unknown.
Using the product property of α-stable distributions we rewrite the measurement noise in a conditionally Gaussian form. Within this framework, we propose an hybrid sigma-point/Monte Carlo approach to solve the Bayesian ltering problem, what leads to a robust method against both outliers and a weak knowledge of the system.