Spatial Array Processing
(3-0-0-3)
CMPE Degree: This course is Not Applicable for the CMPE degree.
EE Degree: This course is Not Applicable for the EE degree.
Lab Hours: 0 supervised lab hours and 0 unsupervised lab hours.
Technical Interest Group(s) / Course Type(s): Digital Signal Processing
Course Coordinator:
Prerequisites: ECE 4270
Catalog Description
Introduce application areas where signals are sampled over space and time.Transfer knowledge of time-based techniques to spatial processing.
Develop algorithms unique to spatial processing.
Textbook(s)
Array Signal Processing: Concepts and TechniquesCourse Outcomes
Not Applicable
Strategic Performance Indicators (SPIs)
Not Applicable
Topical Outline
1. Introduction
a. Propagating Waves
b. Wavenumber-Frequency Space
c. Apertures
2. Conventional Beamforming
a. Delay-and-Sum Beamforming (Plane Waves and Spherical Waves)
b. Filter-and-Sum Beamforming
c. Quadrature Demodulation
d. Conventional Narrowband Beamforming
e. Conventional Wideband Beamforming
3. Second-Order Statistical Modeling
a. Stochastic Narrowband Models
b. Signal to Noise Ratios
c. Time Averaging
d. Spatial Averaging and Co-arrays
4. Subspace Methods
a. Constrained Optimization
b. MVDR Beamforming
c. Pisarenko Harmonic Decomposition
d. Subspace Methods: Eigenvalue Method and MUSIC
e. Root MUSIC
f. ESPRIT
g. Robust Constrained Estimation
5. Estimation-Theoretic Methods
a. Introduction to Estimation Theory
b. 'Stochastic' and 'Deterministic' Signal Gaussian Models
c. Maximum-Likelihood Estimation
d. Introduction to Cramer-Rao Bounds
e. Cramer-Rao Bounds for Arrays
f. Transformations of Cramer-Rao Bounds
g. Model Order Estimation
h. Unconstrained Covariance Estimation