Cone Bearing Estimation Utilizing a Hybrid HMM and IFM Smoother Filter Formulation

Cone penetration testing (CPT) is a widely used geotechnical engineering in-situ test for mapping soil profiles and assessing soil properties. In CPT, a cone on the end of a series of rods is pushed into the ground at a constant rate and resistance to the cone tip is measured (qm). The qm values are utilized to characterize the soil profile. Unfortunately, the measured cone tip resistance is blurred and/or averaged which can result in the distortion of the soil profile characterization and the inability to identify thin layers. This paper outlines a novel and highly effective algorithm for obtaining cone bearing estimates qt from averaged or smoothed qm measurements. This qt optimal filter estimation technique is referred to as the qtHMM-IFM algorithm and it implements a hybrid hidden Markov model and iterative forward modelling technique. The mathematical details of the qtHMM-IFM algorithm are outlined in this paper along with the results from challenging test bed. The test bed simulations have demonstrated that the qtHMM-IFM algorithm can derive accurate qt values from challenging averaged qm profiles. This allows for greater soil resolution and the identification and quantification of thin layers in a soil profile.

Indoor localization for pedestrians with real-time capability using multi-sensor smartphones

The localization of persons or objects usually refers to a position determined in a spatial reference system.Outdoors,this is usually accomplished with Global Navigation Satellite Systems(GNSS).However,the automatic positioning of people in GNSS-free environments,especially inside of buildings(indoors)poses a huge challenge.Indoors,satellite signals are attenuated,shielded or reflected by building components(e.g.walls or ceilings).For selected applications,the automatic indoor positioning is possible based on different technologies(e.g.WiFi,RFID,or UWB).However,a standard solution is still not available.Many indoor positioning systems are only suitable for specific applications or are deployed under certain conditions,e.g.additional infrastructures or sensor technologies.Smartphones,as popular cost-effective multi-sensor systems,is a promising indoor localization platform for the mass-market and is increasingly coming into focus.Today’s devices are equipped with a variety of sensors that can be used for indoor positioning.In this contribution,an approach to smartphone-based pedestrian indoor localization is presented.The novelty of this approach refers to a holistic,real-time pedestrian localization inside of buildings based on multisensor smartphones and easy-to-install local positioning systems.For this purpose,the barometric altitude is estimated in order to derive the floor on which the user is located.The 2D position is determined subsequently using the principle of pedestrian dead reckoning based on user's movements extracted from the smartphone sensors.In order to minimize the strong error accumulation in the localization caused by various sensor errors,additional information is integrated into the position estimation.The building model is used to identify permissible(e.g.rooms,passageways)and impermissible(e.g.walls)building areas for the pedestrian.Several technologies contributing to higher precision and robustness are also included.For the fusion of different linear and non-linear data,an advanced algorithm based on the Sequential Monte Carlo method is presented.