91 research outputs found
SLAM using LTE Multipath Component Delays
Cellular radio based localization can be an important complement or alternative to other localization technologies, as base stations continuously transmit signals of opportunity with beneficial positioning properties. In this paper, we use the long term evolution (LTE) cell-specific reference signal for this purpose. The multipath component delays are estimated by the ESPRIT algorithm, and the estimated multipath component delays of different snapshots are associated by global nearest neighbor with a Kalman filter. Rao-Blackwellized particle filter based simultaneous localization and mapping (SLAM) is then applied to estimate the position of user equipment and that of the base station and virtual transmitters. In a measurement campaign, data from one base station was logged, and the analysis based on the data shows that, at the end of the measurement, the SLAM performance is 11 meters better than that with only inertial measurement unit (IMU)
Extended FastSLAM Using Cellular Multipath Component Delays and Angular Information
Opportunistic navigation using cellular signals is appealing for scenarios where other navigation technologies face challenges. In this paper, long-term evolution (LTE) downlink signals from two neighboring commercial base stations (BS) are received by a massive antenna array mounted on a passenger vehicle. Multipath component (MPC) delays and angle-of-arrival (AOA) extracted from the received signals are used to jointly estimate the positions of the vehicle, transmitters, and virtual transmitters (VT) with an extended fast simultaneous localization and mapping (FastSLAM) algorithm. The results show that the algorithm can accurately estimate the positions of the vehicle and the transmitters (and virtual transmitters). The vehicle’s horizontal position error of SLAM fused with proprioception is less than 6 meters after a traversed distance of 530 meters, whereas un-aided proprioception results in a horizontal error of 15 meters
Extended FastSLAM Using Cellular Multipath Component Delays and Angular Information
Opportunistic navigation using cellular signals is appealing for scenarios
where other navigation technologies face challenges. In this paper, long-term
evolution (LTE) downlink signals from two neighboring commercial base stations
(BS) are received by a massive antenna array mounted on a passenger vehicle.
Multipath component (MPC) delays and angle-of-arrival (AOA) extracted from the
received signals are used to jointly estimate the positions of the vehicle,
transmitters, and virtual transmitters (VT) with an extended fast simultaneous
localization and mapping (FastSLAM) algorithm. The results show that the
algorithm can accurately estimate the positions of the vehicle and the
transmitters (and virtual transmitters). The vehicle's horizontal position
error of SLAM fused with proprioception is less than 6 meters after a traversed
distance of 530 meters, whereas un-aided proprioception results in a horizontal
error of 15 meters
MilliSonic: Pushing the Limits of Acoustic Motion Tracking
Recent years have seen interest in device tracking and localization using
acoustic signals. State-of-the-art acoustic motion tracking systems however do
not achieve millimeter accuracy and require large separation between
microphones and speakers, and as a result, do not meet the requirements for
many VR/AR applications. Further, tracking multiple concurrent acoustic
transmissions from VR devices today requires sacrificing accuracy or frame
rate. We present MilliSonic, a novel system that pushes the limits of acoustic
based motion tracking. Our core contribution is a novel localization algorithm
that can provably achieve sub-millimeter 1D tracking accuracy in the presence
of multipath, while using only a single beacon with a small 4-microphone
array.Further, MilliSonic enables concurrent tracking of up to four smartphones
without reducing frame rate or accuracy. Our evaluation shows that MilliSonic
achieves 0.7mm median 1D accuracy and a 2.6mm median 3D accuracy for
smartphones, which is 5x more accurate than state-of-the-art systems.
MilliSonic enables two previously infeasible interaction applications: a) 3D
tracking of VR headsets using the smartphone as a beacon and b) fine-grained 3D
tracking for the Google Cardboard VR system using a small microphone array
Indoor wireless communications and applications
Chapter 3 addresses challenges in radio link and system design in indoor scenarios. Given the fact that most human activities take place in indoor environments, the need for supporting ubiquitous indoor data connectivity and location/tracking service becomes even more important than in the previous decades. Specific technical challenges addressed in this section are(i), modelling complex indoor radio channels for effective antenna deployment, (ii), potential of millimeter-wave (mm-wave) radios for supporting higher data rates, and (iii), feasible indoor localisation and tracking techniques, which are summarised in three dedicated sections of this chapter
Flexible Density-based Multipath Component Clustering Utilizing Ground Truth Pose
Accurate statistical characterization of electromagnetic propagation is necessary for the design and deployment of radio systems. State-of-the-art channel models such as the Enhanced COST 2100 Channel Model utilize the concept of clusters of multipath components, and characterize channels by their inter- and intra-cluster statistics. Automatic clustering algorithms have been proposed in literature, but the subjective nature of the problem precludes any from being deemed objectively correct. In this paper, a new algorithm is proposed, based on density-reachability and ground truth receiver pose, with the explicit focus of extracting clusters for the purpose of channel characterization. Measurements of downlink signals from a commercial LTE base station by a passenger vehicle driving in an urban environment with a massive antenna array on the roof are used to evaluate the repeatability and intuitiveness of the proposed clustering algorithm
Location-Enabled IoT (LE-IoT): A Survey of Positioning Techniques, Error Sources, and Mitigation
The Internet of Things (IoT) has started to empower the future of many
industrial and mass-market applications. Localization techniques are becoming
key to add location context to IoT data without human perception and
intervention. Meanwhile, the newly-emerged Low-Power Wide-Area Network (LPWAN)
technologies have advantages such as long-range, low power consumption, low
cost, massive connections, and the capability for communication in both indoor
and outdoor areas. These features make LPWAN signals strong candidates for
mass-market localization applications. However, there are various error sources
that have limited localization performance by using such IoT signals. This
paper reviews the IoT localization system through the following sequence: IoT
localization system review -- localization data sources -- localization
algorithms -- localization error sources and mitigation -- localization
performance evaluation. Compared to the related surveys, this paper has a more
comprehensive and state-of-the-art review on IoT localization methods, an
original review on IoT localization error sources and mitigation, an original
review on IoT localization performance evaluation, and a more comprehensive
review of IoT localization applications, opportunities, and challenges. Thus,
this survey provides comprehensive guidance for peers who are interested in
enabling localization ability in the existing IoT systems, using IoT systems
for localization, or integrating IoT signals with the existing localization
sensors
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