A Novel Sequential Monte Carlo Method Based Synthetic Aperture Reconstruction Approach for Real-Time 3D Wireless Local Positioning

Abstract

In this paper, we introduce a precise real-time 3D indoor localization concept using a combination of the MISAS (multilateral inverse synthetic aperture secondary radar) approach and SMCM (sequential Monte Carlo method). The synthetic aperture approach is a promising locating technique with effective multipath suppression. However, its practical use is restricted by the huge computational load of the reconstruction algorithms. We use a weighted-particle set -which we reduce subsequently by resampling -to represent the PDF (probability density function) instead of the highly complex, standard, grid-based approach. Simulation runs and experiments, carried out with a 5.8 GHz FMCW secondary radar system with a bandwidth of only 140 MHz, show that a robot TCP (tool center point) can be localized in 3D in the mmrange using a few hundred particles and operating in a dense multipath environment. This novel extension is a promising solution for real-time, 3D MISAS localization applications.

Mie Series for Electromagnetic Scattering of a Conducting Sphere Coated with Chiral Metamaterials

“…Based on the spherical harmonics vector function in CMMs, the electromagnetic fields inside and outside of a conducting sphere coated with CMMs are expanded. After applying the boundary conditions on the interfaces of all regions, the co-polarized and cross-polarized bistatic radar cross section (RCS) [18] of a conducting sphere coated with different kinds of CMMs are given. How to overcome the numerical instability of Bessel function in the Mie series formula is also discussed.…”

Bayesian multiple target tracking

“…The PF has been widely used in several real-time array processing applications, examples of which include: localisation of remotely operated underwa-ter vehicles using SONAR [150], bearings-only tracking using SONAR [151], TBD filtering using RADAR [152,153], 3D indoor tracking using RADAR [154], detecting voice activity [155], audio source separation [156], detecting seismic activity [157], catching shovelnose guitarfish [2], tracking ballistic missiles [158] and robot assisted heart diagnosis [159]. In spite of being computationally expensive [45,46,160], the PF is regarded as a well established Bayes' filter and still attracts great attention from the research community.…”

“…Bayesian MTT is useful in many applications. Examples include aircraft and missile tracking using RADAR [1,152,154,158], vessel and fish tracking using SONAR [2,99,100], acoustic and seismic activity tracking [155-157, 192, 214], space debris and spacecraft tracking [3,106,107], heart-beat tracking from ECG [4,9,147], and many others. This thesis added to the body of knowledge in Bayesian MTT by proposing several original innovative ideas that overcome problems with state-ofthe-art tracking systems.…”