Browsing by Author "Dehghanian, Vahid"
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- ItemOpen AccessDetection Performance of Polarization and Spatial Diversities for Indoor GNSS Applications(2012-03-18) Zaheri, Mohammadreza; Broumandan, Ali; Dehghanian, Vahid; Lachapelle, GérardMultipath fading in the form of signal power fluctuation poses a formidable challenge to GNSS signal detection in harsh multipath environments such as indoors. Antenna diversity techniques such as polarization and spatial diversities can be used to combat multipath fading in wireless propagation channels. This paper studies and compares GPS signal detection performance enhancements arising from the spatial and polarization diversity techniques. Performance enhancements are quantified from a theoretical perspective and later verified based on several test measurements in various indoor environments. Enhancement is quantified based on measuring the correlation coefficient values between diversity branches, SNR levels, and computing the level crossing rate and average fade duration. In addition, the processing gain is quantified and the performance of each individual diversity system is evaluated. Experimental results show that, for a given target detection performance in terms of the probability of false alarm and the probability of detection, the required input SNR level to meet the target detection performance can be significantly reduced utilizing the diversity system.
- ItemOpen AccessDetection Performance of Polarization and Spatial Diversities for Indoor GNSS Applications(Hindawi Publishing Corporation, 2012-01-10) Zaheri, Mohammadreza; Broumandan, Ali; Dehghanian, Vahid; Lachapelle, Gérard
- ItemOpen AccessDiversity Gain through Antenna Blocking(Hindawi Publishing Corporation, 2011-10-19) Dehghanian, Vahid; Nielsen, John; Lachapelle, Gérard
- ItemOpen AccessEnhancing Wireless Received Signal Strength-based Indoor Location Systems(2017) Li, Yuqi; Nielsen, John; Lachapelle, Gérard; Ling, Pei; Sesay, Abu; Dehghanian, Vahid; O'Keefe, KyleThe ever-growing demand for Location Based Services has significantly boosted the research and development need for indoor positioning systems. Of various indoor positioning solutions, techniques making use of Received Signal Strength (RSS) of wireless signals of opportunity have gained extensive interest due to the ubiquitous wireless signal infrastructure and the readily available RSS measurements with standard mobile devices. However, the performance of RSS-based indoor positioning systems is highly affected by significant uncertainties in RSS due to many factors affecting wireless propagations. To enhance the performance of an RSS-based indoor positioning system, from a Bayesian filtering theory perspective, a better estimation of the a posteriori distribution of position is needed. This can be done through a better modelling of RSS measurements to mitigate uncertainties and/or incorporating prior information. This thesis specifically explores mitigating RSS uncertainties by modelling those due to human body shadowing and incorporating prior information from widely available security cameras and building maps. The characterization of RSS measurements indoors is first demonstrated using data collected in various environments. Experimental results characterize the RSS sensitivity to location and the uncertainty incurred by body shadowing effects on RSS measurements. Based on the characterization, an empirical model with a small number of parameters estimated from training data is proposed to model the RSS loss due to body shadowing. An estimator based on this model is proposed to improve positioning. Experimental results show that when the user heading is known, the positioning obviously improves. When the heading is unknown, and thus needs to be jointly estimated, the improvement becomes less apparent. This thesis then investigates the use of security cameras and building maps to enhance RSS-based positioning. An estimator based on computer vision processing is proposed to estimate user’s heading in corridors. Based on this, a camera-aided RSS system based on Kalman-filter is proposed and it is experimentally shown that a 37.5% improvement in horizontal position estimation occurs. To further incorporate building map information, a map-camera-aided RSS system based on particle filters is proposed. Experimental results indicate that the use of map constraints further bring 44.4% improvement in the across track direction.
- ItemOpen AccessGeneralized diversity gain of a mobile antenna(2011) Dehghanian, Vahid; Nielsen, John; Lachapelle, Gérard
- ItemOpen AccessOn the Capacity of Densely Packed Arrays with Mutual Coupling and Correlated Noise(2015-09-17) Dehghanian, Vahid; Nielsen, JohnCapacity of a wireless link can be enhanced by increasing the number of receive antennas. However, imposed receiver physical size constraints necessitate that the antenna elements be in close proximity, which typically reduces the overall link capacity of the wireless channel. Counterintuitively, under certain conditions the capacity of the overall link can be enhanced by decreasing antenna spacings. The focus of this paper is that of identifying the fundamental mechanisms and the conditions that give rise to this excess capacity. Closed-form expressions that directly quantify this capacity gain are derived based on a representative circuit theoretic model. Interesting insights are developed about the impact of different noise and interference sources and the limiting effect of heat losses in the antenna system. The capacity analysis is subsequently generalized to encompass the effect of antenna current deformation and load mismatch due to mutual coupling, based on the standard Method of Moments (MoM) analysis, demonstrating similar capacity enhancement behavior as predicted by the closed-form expressions.
- ItemOpen AccessSensor Fusion-based Framework for Floor Localization(2018-07-03) Haque, Fahimul; Dehghanian, Vahid; Fapojuwo, Abraham Olatunji; Nielsen, Jørgen S.; Messier, Geoffrey G.Floor localization is at the heart of indoor positioning systems (IPSs) in multi-storey buildings with a variety of commercial, industrial, and health and safety applications. The prevalence of wireless technologies along with the integration of micro electro-mechanical sensors (e.g. barometers) in handheld devices and wearable gadgets of current vintage have prompted a surge in research and development efforts in the IPS area. Received signal strength (RSS), barometric altimetry (BA), and differential barometric altimetry (DBA) are three well-known methods of floor localization. However, the RSS-based methods lack the required accuracy, BA-based methods are prone to random errors due to local changes in the air pressure, e.g. from approaching weather systems, and DBA-based methods require installation of additional infrastructure (e.g. reference nodes and ad-hoc network for real-time information exchange). Fusion of BA and RSS is a viable solution for floor localization; nevertheless, available fusion algorithms are rather heuristic. In this dissertation, a theoretical framework is developed for fusing BA and Wi-Fi RSS measurements. The proposed framework involves a novel Monte Carlo Bayesian inference algorithm, for processing RSS measurements, and then fusion with BA using a Kalman Filter scheme. As demonstrated by our experimental results, the proposed sensor fusion algorithm achieves floor localization accuracy of 97% on average. The algorithm does not require new infrastructure, and has low computational complexity, hence, can be readily integrated into various state-of-the-art mobile devices.