1 / 24

Angle of Arrival ( AoA )

Angle of Arrival ( AoA ) . Calen Carabajal EECS 823. Introduction to Angle of Arrival Physics. “Angle between propagation direction of an incident wave and some reference direction” ( orientation) Plane wave impinging upon array. Visual Understanding.

jenna
Download Presentation

Angle of Arrival ( AoA )

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Angle of Arrival (AoA) Calen Carabajal EECS 823

  2. Introduction to Angle of Arrival Physics • “Angle between propagation direction of an incident wave and some reference direction” (orientation) • Plane wave impinging upon array

  3. Visual Understanding • Plane wave impinges on array of antennas with the same orientation and radiation pattern • Time delay corresponds to a phase shift between antennas • To the right, red lines represent wave front, each with the same relative phase • Red dot corresponds incidence of wave front • Results in a zero-valued response for this phase

  4. Wave Propagation and Antenna: Specific Case • Constant amplitude assumed • Received signal for each antenna is • Summing signals together results in • Observations • – broadside vsboresight • Cancellation k*r • Frequency dependence

  5. Antenna Arrays, Steering Vector • For identical antennas with radiation pattern and orientation ,the overall radiation pattern is • Wave number • Steering vector can take various forms • Specified to arbitrary point of origin • Represents relative phases at each antenna

  6. Applications of Angle of Arrival Estimation: Wireless Sensor Networks • Wireless Sensor Networks • May use antenna array on each sensor node • Geodesic location of cell phones • Emergency phone calls

  7. Applications of Angle of Arrival: Remote Sensing • AoA estimation provides also allows further characterization of target. Adaptive processes can take advantage of this knowledge • Beamsteering/Nullsteering • Angle-of-arrival-assisted Radio Interferometry • Ground moving objects • Coupled with other data (range, Doppler), can extract target location

  8. Limitations of Angle of Arrival Estimation: The Cramer-Rao Bound • CRB provides lower bound on variance in estimations • Provides a theoretical limit on ability to discern angle of arrival • Considers corrupting noise on the signal • The Cramer-Rao Bound for AoA estimation is • : covariance of noise vector • N : number of elements in array • d : distance between array elements

  9. Limitations of Angle of Arrival Estimation: Effect of Multipath • Consider either a smooth surface or rough surface • Specular surface results in two components—direct component and image component • Rough surface results in both the above components as well as diffuse components • Fading • In extreme case, may result in signal cancellation • Approach: Multi-taper Method

  10. Limitations in Angle of Arrival Estimation: Array-based Ambiguities • Ambiguities can introduced to the estimation by the array itself • Linear array has infinite ambiguities • Planar array has two

  11. Limitations in Angle of Arrival Estimation: Atmospheric Turbulence • Generally small (a few microradians) • Can be significant depending on the application • Guided missiles

  12. Estimation Algorithms • Correlation • Maximum Likelihood Estimation • MUSIC: Multiple Signal Classification • ESPIRIT: Estimation of Signal Parameters using Rotational Invariance Techniques • Matrix Pencil

  13. Estimation Algorithms: Correlation • Non-adaptive estimation • Wish to estimate • The function has a maximum at • Optimal for single-user situation • Equivalent to DFT of x

  14. Estimation Algorithms: Maximum Likelihood Estimation • Generalize n to an interference vector • Vector has property that • Both magnitude and AoA are unknown parameters • MLE described by • AoA estimate is where maximum likelihood estimate of spectrum takes maximum • Observations • Requires a priori knowledge of interference covariance matrix • Highly intensive • Impractical algorithm

  15. Estimation Algorithms: MUSIC • Multiple Signal Classification • Adaptive technique based on orthogonality of uncorrelated signal covariance matrix • is N x M steering matrix of M steering vectors • All eigenvectors are orthogonal to the M signal steering vectors • Pseudo-spectrum

  16. Estimation Algorithms: MUSIC

  17. Estimation Algorithms: Root-MUSIC • Addresses problem of accuracy in MUSIC due to discretization and need for human interaction • Uses a model of the signal-- • Algorithm • First requires calculation of correlation matrix R • provides an estimation of R • Decompose R into Q by • Partition Q for smallest eigenvalues, • , sum diagonals of this matrix provides • ; • Roots near unit circle provide , for

  18. Estimation Algorithm: ESPRIT • Estimation of Signal Parameters using Rotational Invariance Techniques • Operates based on constant phase shift within S matrix • Algorithm • First requires calculation of correlation matrix R • provides an estimation of R • Decompose R into Q by • Partition to find , M largest eigenvalues of Q. • Matrix C defined by • Estimate • Calculate AoA with , for • is provided as mth element of diagonal matrix

  19. Estimation Algorithms: Matrix Pencil • Non-statistical technique • Time based signal • Again, • Estimate poles using multiple samples of x • Use X matrices (as shown) to calculate the system roots • , for

  20. Summary of Methods Discussed • MUSIC/Root-MUSIC • Requires assumption of N > M, resolving up to N-1 signals. • Large number of signals • ESPRIT • Requires assumption that N > M as well • Large number of signals • Pencil Matrix • Maximum value N/2 for even N, (N+1)/2 for odd • Does not require large number of samples • ½ time of Root-MUSIC, less computation • If coherent detector is present, same accuracy as Root-MUSIC

  21. Passive Radar for Detection of Ground Moving Objects • Recently developed for border security • Utilizes AoA MUSIC technique alongside range-Doppler technique for target location • Test operation at 1 GHz using a cell phone antenna emitting a BPSK signal

  22. Passive Radar for Detection of Ground Moving Objects

  23. References • http://www.comm.utoronto.ca/~rsadve/Notes/DOA.pdf • http://soma.mcmaster.ca/papers/Paper_112.pdf • http://www4.ncsu.edu/~mlsichit/Research/Publications/aoaLocalizationSecon06.pdf • Combined Use of Various Passive Radar Techniques and Angle of Arrival using MUSIC for the Detection of Ground Moving Objects. Chan et al. http://ieeexplore.ieee.org.www2.lib.ku.edu:2048/stamp/stamp.jsp?tp=&arnumber=5997047 • Angle-of-Arrival of a Radar Beam in Atmospheric Turbulence. McMillan et al. http://ieeexplore.ieee.org.www2.lib.ku.edu:2048/stamp/stamp.jsp?tp=&arnumber=999728

  24. Questions?

More Related