1. GNSS Multipath Channel Characterization and Direction of Arrival Estimation by Moving Antenna
Mohammad Hatef Keshvadi
Position, Location And Navigation (PLAN) Group
Department of Geomatics Engineering
University of Calgary
ENGO February 4th, 2010

2. Contents Motivation Overview Literature review and previous works
Outdoor data collection
Challenges
Future work
ENGO GNSS indoor channel characterization in dense multipath environments by moving antenna

3. Motivation
Significant improvement in “Doppler Estimation” for indoor and dense multipath environments.
Amplification of GNSS signals along the AoA using BF.
By BF we will be able to track desired signals from several directions and also track multipath/interfering signals. Also one can steer peaks of Array Gain towards desired signals while pointing the nulls towards unwanted signals.
Implementation of such technique for synthetic arrays can be done via moving the antenna. Also in contrast with real arrays, inter-channel phases and coupling between antennas will vanish so we will not need calibration in addition to less hardware and computational complexity.
Beamforming
ENGO GNSS indoor channel characterization in dense multipath environments by moving antenna

4. Overview The main objectives: Method
Identifying Angle of Arrival (AoA)
Beamforming (BF)
Method
Real data collection by a circular synthetic antenna array, in:
Open sky environment
Mild multipath environment
Dense multipath environment
Finding the AoA and BF
Software receiver (GSNRx) output (for outdoor)
Acquisition code outputs in Matlab® (for indoor)
ENGO GNSS indoor channel characterization in dense multipath environments by moving antenna

5. Literature Review and Previous Works (1/4)
The main idea of synthetic array antennas is first used in radars
The idea is brought to GNSS society in (Broumandan 2007)
In (Broumandan 2007, Pany 2008a, Pany 2008b, Soloviev 2008) circular and linear motion was used to generate synthetic arrays
Main applied methods were:
FFT based methods for BF (Soloviev 2008)
In (Lin 2009) several adaptive BF and AoA methods were employed
ENGO GNSS indoor channel characterization in dense multipath environments by moving antenna

6. Literature Review and Previous Works (2/4)Σ
AOA
Estimation
Multipath
Rejection ,
signal amplification w
Complex weights
Beamforming
For BF, AoA must be known precisely.
In practice, AoA is estimated.
Angle of Arrival Estimation for Handset Location Application

7. Literature Review and Previous Works (3/4)
BF methods used in (Lin 2009):
Delay and Sum (Beam Scan) (Lin 2009, Van Trees 2002):
Minimum Variance Distortionless Response (MVDR) (Lin 2009, Van Trees 2002) :
Linear Constraint Minimum Variance/Power (LCMV) (Lin 2009, Van Trees 2002) :
Generally BF is performed via multiplying the spatial signal samples ina complex weighting factor (w)
These methods are all linear they can be applied to both post correlation (MP detection/mitigation) or pre-correlation (interference detection/mitigation) samples.
Delay and Sum:
Classical BF method
Optimum in White Gaussian Noise (WGN) to maximize the Array SNR (ASNR)
MVDR:
Optimum to MAX. both ML and max. ASNR sense.
For both correlated and uncorrelated WGN.
For known LOS and correlation matrix
It takes MP and interference into account.
LCMV:
Generalized MVDR
Using more linear constraints
In above equations:
N: No. of arrays
V: array manifold vector
Rn: correlation of unwanted signals
C: constraint matrix that contains all the steering vectors
g: constraint vector that contains all the numerical values for the constraints
ENGO GNSS indoor channel characterization in dense multipath environments by moving antenna

8. Literature Review and Previous Works (4/4)
AoA estimation methods used in (Lin 2007) include:
Beam Scan (Lin 2007, Van Trees 2002, Allen 2005):
Multiple Signal Classification (MUSIC) (Lin 2007, Van Trees 2002, Allen 2005):
Other Possible Methods:
ESPIRIT
Maximum Likelyhood based methods (Van Trees 2002, Allen 2005)
Beam scan:
Classical methods to estimate angle of arrival
Scans the power of signal at each direction to find the strongest reception angle.
MUSIC:
By finding orthogonal subspaces to noise subspace.
High Resolution
Correlation matrix and No. of Desired signals must be known perfectly
Computationally complex.
Where Q is the eigenvectors of signals covariance matrix
ENGO GNSS indoor channel characterization in dense multipath environments by moving antenna

9. Outdoor Data Collection (1/3)
To test the developed codes for AoA estimation
AoA estimation methods: Beam Scan, MUSIC
Location: CCIT rooftop
Date and Time: 21Jan, 2010 – 10:00~11:00 am
Angular Velocity: 0.1 rev/sec
Two Radiuses: 50 cm, 63.5 cm
The main goal is to test and verify the codes to further employ them for indoor environment. This test is some how simillar to TAO’s paper, however he has used a linear approximation for clock drift and satellite to base doppler effect. I used a reference antenna instead.
We use a Ref. antenna to remove receiver’s clock drift and also the doppler effect induced from the relative motion of the satellite and an static antenna hence remaining only with the doppler of the moving antenna w.r.t ref. antenna.
Moving antenna
Reference antenna
ENGO GNSS indoor channel characterization in dense multipath environments by moving antenna

10. Outdoor Data Collection (2/3)
Results – Beam Scan for PRN2 and PRN29:
Beam Scan results for PRN 29
Elevation (deg)
Azimuth (deg)
Beam Scan results for PRN 02
Elevation (deg)
Azimuth (deg)
AoA Spectrum
AoA Spectrum
Elevation = 78.7o
Elevation = 32.5o
Elevation angle is estimated precisely
Estimation of azimuth angle, depends on the selected initial epoch for each set.
ENGO GNSS indoor channel characterization in dense multipath environments by moving antenna

11. Future Work MUSIC algorithm is under development
Correcting deficiencies in codes
Studying more material on telecommunication engineering and array processing
Implementing Beamforming methods (Beam Scan, MVDR, LCMP)
Collecting more data in environments with more multipath (foliage, urban canyons, indoor environments)
ENGO GNSS indoor channel characterization in dense multipath environments by moving antenna

12. Challenges Exact number of arriving signals must be know for MUSIC.
Longer coherent integration time in indoor, resulting in fewer synthetic antennas.
12/13
ENGO GNSS indoor channel characterization in dense multipath environments by moving antenna 12

13. Thank you for you attention
ENGO GNSS indoor channel characterization in dense multipath environments by moving antenna

14. References:
Broumandan A, et al. Direction of Arrival Estimation of GNSS Signals Based on Synthetic Antenna Array. Proc. of ION GNSS07.
Pany T, et al. Synthetic Phased Array Antenna for Carrier/Code Multipath Mitigation. Proc. of ENC-GNSS08, Toulouse.
Pany T, et al. Demonstration of Synthetic Phased Array Antenna for Carrier/Code Multipath Mitigation. Proc. of GNSS08.
Soloviev A, et al. Synthetic Aperture GPS Signal Processing: Concept and Feasibility Demonstration. Proc. of ITM09.
Lin T, et al. Robust Beamforming for GNSS Synthetic Antenna Arrays, Proc. Of ION GNSS 2009, sep 2009, Savannah, GA.
Van Trees, H. L., Optimum Array Processing, Part IV, Detection, Estimation and Modulation Theory, John Wiley and Sons, Inc., NY 2002.
Allen B, Ghavami M, Adaptive Array Systems, Fundamentals and Applications, John Wiley and Sons, Inc., 2005.