66 research outputs found
A priori knowledge-free fast positioning approach for BeiDou receivers
A Global Navigation Satellite System (GNSS) receiver usually needs a
sufficient number of full pseudorange measurements to obtain a position
solution. However, it is time-consuming to acquire full pseudorange information
from only the satellite broadcast signals due to the navigation data features
of GNSS. In order to realize fast positioning during a cold or warm start in a
GNSS receiver, the existing approaches require an initial estimation of
position and time or require a number of computational steps to recover the
full pseudorange information from fractional pseudoranges and then compute the
position solution. The BeiDou Navigation Satellite System (BDS) has a unique
constellation distribution and a fast navigation data rate for geostationary
earth orbit (GEO) satellites. Taking advantage of these features, we propose a
fast positioning technique for BDS receivers. It simultaneously processes the
full and fractional pseudorange measurements from the BDS GEOs and non-GEOs,
respectively, which is faster than processing all full measurements. This
method resolves the position solution and recovers the full pseudoranges for
non-GEOs simultaneously within 1 s theoretically and does not need an estimate
of the initial position. Simulation and real data experiments confirm that the
proposed technique completes fast positioning without a priori position and
time estimation, and the positioning accuracy is identical with the
conventional single-point positioning approach using full pseudorange
measurements from all available satellites
Range-only Collaborative Localization for Ground Vehicles
High-accuracy absolute localization for a team of vehicles is essential when
accomplishing various kinds of tasks. As a promising approach, collaborative
localization fuses the individual motion measurements and the inter-vehicle
measurements to collaboratively estimate the states. In this paper, we focus on
the range-only collaborative localization, which specifies the inter-vehicle
measurements as inter-vehicle ranging measurements. We first investigate the
observability properties of the system and derive that to achieve bounded
localization errors, two vehicles are required to remain static like external
infrastructures. Under the guide of the observability analysis, we then propose
our range-only collaborative localization system which categorize the ground
vehicles into two static vehicles and dynamic vehicles. The vehicles are
connected utilizing a UWB network that is capable of both producing
inter-vehicle ranging measurements and communication. Simulation results
validate the observability analysis and demonstrate that collaborative
localization is capable of achieving higher accuracy when utilizing the
inter-vehicle measurements. Extensive experimental results are performed for a
team of 3 and 5 vehicles. The real-world results illustrate that our proposed
system enables accurate and real-time estimation of all vehicles' absolute
poses.Comment: Proceedings of the 32nd International Technical Meeting of the
Satellite Division of The Institute of Navigation (ION GNSS+ 2019
Attention-Block Deep Learning Based Features Fusion in Wearable Social Sensor for Mental Wellbeing Evaluations
With the progressive increase of stress, anxiety and depression in working and living environment, mental health assessment becomes an important social interaction research topic. Generally, clinicians evaluate the psychology of participants through an effective psychological evaluation and questionnaires. However, these methods suffer from subjectivity and memory effects. In this paper, a new multi- sensing wearable device has been developed and applied in self-designed psychological tests. Speech under different emotions as well as behavior signals are captured and analyzed. The mental state of the participants is objectively assessed through a group of psychological questionnaires. In particular, we propose an attention-based block deep learning architecture within the device for multi-feature classification and fusion analysis. This enables the deep learning architecture to autonomously train to obtain the optimum fusion weights of different domain features. The proposed attention-based architecture has led to improving performance compared with direct connecting fusion method. Experimental studies have been carried out in order to verify the effectiveness and robustness of the proposed architecture. The obtained results have shown that the wearable multi-sensing devices equipped with the attention-based block deep learning architecture can effectively classify mental state with better performance
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