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Multipath and Interference Mitigation in GNSS Receiver by Anil Kumar

By: Contributor(s): Material type: TextTextPublication details: IIT Jodhpur Department of Electrical Engineering 2017Description: xii,56p. HBSubject(s): DDC classification:
  • 621.84 K963M
Summary: "As positioning/navigation/timing (PNT) based applications continue to expand strongly in consumer and commercial segments, demand is also growing for uninterrupted, and ubiquitous access to position/location information in all environments, indoors and outdoors. The standard ‘global navigation satellite system (GNSS)’ receiver works well in outdoor line-of-sight environments. However, in dense urban settings and indoor environments, due to signal multipath, shadowing, noise and interference caused by communication system operating in adjacent frequency bands, the accuracy of GNSS receivers is severely degraded rendering PNT solutions useless for many applications. For obtaining the position solution, the signal coming from the satellite is correlated with the reference signal available at navigation receiver. Standard receiver takes the advantage of triangular shape of the correlation function, but in case of multipath it is effectively the some of many such triangles each corresponding to a multipath component. The disturbance caused in correlation function leads to an error in position solution. In urban and indoor environments, along with multipath, shadowing and interference are additional problems that are responsible for low received signal power. In such a case of weak signals, receiver acquisition fails and no position solution is obtained. In the presented work we provide novel algorithmic solutions to mitigate signal multipath and interference effects on PNT solutions in dense urban settings and indoor environments. The performance improvements of the proposed solutions are demonstrated using recorded GNSS satellite signal and MATLAB based software defined GNSS receiver and the results show that these methods perform within desired accuracy limits in very general multipath and interference environments. The comparison shows that performance of these algorithms is far better than the existing state of art solutions."
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"As positioning/navigation/timing (PNT) based applications continue to expand strongly
in consumer and commercial segments, demand is also growing for uninterrupted, and ubiquitous
access to position/location information in all environments, indoors and outdoors. The
standard ‘global navigation satellite system (GNSS)’ receiver works well in outdoor line-of-sight
environments. However, in dense urban settings and indoor environments, due to signal
multipath, shadowing, noise and interference caused by communication system operating in
adjacent frequency bands, the accuracy of GNSS receivers is severely degraded rendering PNT
solutions useless for many applications.
For obtaining the position solution, the signal coming from the satellite is correlated with
the reference signal available at navigation receiver. Standard receiver takes the advantage of
triangular shape of the correlation function, but in case of multipath it is effectively the some of
many such triangles each corresponding to a multipath component. The disturbance caused in
correlation function leads to an error in position solution. In urban and indoor environments,
along with multipath, shadowing and interference are additional problems that are responsible
for low received signal power. In such a case of weak signals, receiver acquisition fails and no
position solution is obtained.
In the presented work we provide novel algorithmic solutions to mitigate signal multipath
and interference effects on PNT solutions in dense urban settings and indoor environments. The
performance improvements of the proposed solutions are demonstrated using recorded GNSS
satellite signal and MATLAB based software defined GNSS receiver and the results show that
these methods perform within desired accuracy limits in very general multipath and interference
environments. The comparison shows that performance of these algorithms is far better than the
existing state of art solutions."

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