## ECE 693 Array Signal Processing, Fall 2022

Introduction to the antenna array signal processing. The course will focus on theory and applications of the antenna arrays. Topics will be covered:

1. Introduction to antenna array basics. Array factor, phased-array, array manifold.

2. Beam synthesis. Sidelobe Optimization: Dolph-Chebychev arrays; Minimax Design: Parks-McClellan-Rabiner algorithm.

3. Beamforming techniques: delay and sum beamformer, MVDR, MPDR.

4. Adaptive beamforming techniques: optimization with constraints, gradient based algorithms, least mean square (LMS), sample matrix inversion (SMI), eigen-analysis approach.

5. Direction-of-arrival (DOA) estimation algorithms: brief introduction of parameter estimation theory, Cramer-Rao lower bound (CRLB), coherent signals and spatial smoothing, Nonlinear Least Square (NLS), Covariance Matching, CLEAN, SLIM, IAA, SPICE, Subspace Methods: MUSIC and ESPRIT.

6. Advanced topics: spatial-time adaptive processing (STAP), beamspace processing, MIMO array, sparse array, mutual coupling, thinning array.

Books for reference:

[1] D. Johnson, Array Signal Processing: Concepts and Techniques, Pearson, 1993.

[2] R. Monzingo, R. Haupt, T. Miller, Introduction to Adaptive Arrays (2nd Edition), SciTECH Publishing, 2011.

[3] R. Haupt, Antenna Arrays: A Computational Approach, Willey, 2010.

[4] H. Van Trees, Optimum Array Processing, Wiley, 2002.

1. Introduction to antenna array basics. Array factor, phased-array, array manifold.

2. Beam synthesis. Sidelobe Optimization: Dolph-Chebychev arrays; Minimax Design: Parks-McClellan-Rabiner algorithm.

3. Beamforming techniques: delay and sum beamformer, MVDR, MPDR.

4. Adaptive beamforming techniques: optimization with constraints, gradient based algorithms, least mean square (LMS), sample matrix inversion (SMI), eigen-analysis approach.

5. Direction-of-arrival (DOA) estimation algorithms: brief introduction of parameter estimation theory, Cramer-Rao lower bound (CRLB), coherent signals and spatial smoothing, Nonlinear Least Square (NLS), Covariance Matching, CLEAN, SLIM, IAA, SPICE, Subspace Methods: MUSIC and ESPRIT.

6. Advanced topics: spatial-time adaptive processing (STAP), beamspace processing, MIMO array, sparse array, mutual coupling, thinning array.

Books for reference:

[1] D. Johnson, Array Signal Processing: Concepts and Techniques, Pearson, 1993.

[2] R. Monzingo, R. Haupt, T. Miller, Introduction to Adaptive Arrays (2nd Edition), SciTECH Publishing, 2011.

[3] R. Haupt, Antenna Arrays: A Computational Approach, Willey, 2010.

[4] H. Van Trees, Optimum Array Processing, Wiley, 2002.

## ECE 693 Radar Signal Processing, Fall 2021

Introduction of broad radar signal processing concepts including both fundamental and advanced topics.

1. Radar fundamentals including radar equations, link budget analysis, radar waveforms and ambiguity function (AF).

2. Principle of linear frequency modulated continuous waveform (FMCW), dynamic range, range and Doppler estimation, constant false alarm rate (CFAR) detector.

3. Basics of antenna array, beamforming techniques, direction-of-arrival (DOA) estimation.

4. High resolution spectrum estimation algorithms: compressive sensing, Prony's method, matrix pencil, ESPRIT, MUSIC.

5. Fundamentals of multi-input multi-output (MIMO) radar: waveform orthogonality, virtual array synthesis.

6. Advanced topics in automotive radar systems for autonomous driving.

Textbook:

[1] Mark Richards, Fundamentals of Radar Signal Processing (2nd Edition), McGraw-Hill, 2014.

1. Radar fundamentals including radar equations, link budget analysis, radar waveforms and ambiguity function (AF).

2. Principle of linear frequency modulated continuous waveform (FMCW), dynamic range, range and Doppler estimation, constant false alarm rate (CFAR) detector.

3. Basics of antenna array, beamforming techniques, direction-of-arrival (DOA) estimation.

4. High resolution spectrum estimation algorithms: compressive sensing, Prony's method, matrix pencil, ESPRIT, MUSIC.

5. Fundamentals of multi-input multi-output (MIMO) radar: waveform orthogonality, virtual array synthesis.

6. Advanced topics in automotive radar systems for autonomous driving.

Textbook:

[1] Mark Richards, Fundamentals of Radar Signal Processing (2nd Edition), McGraw-Hill, 2014.

## ECE 593 Special Topics - Automotive Radar Signal Processing, Fall 2020

Introduction to the recent emerging millimeter wave (mmW) automotive radar technology for autonomous driving. Topics will be covered:

1. Introduction to environmental perception of autonomous driving with multi sensing modalities.

2. Radar fundamentals including radar equations, link budget analysis, radar waveforms and ambiguity function (AF).

3. Principle of linear frequency modulated continuous waveform (FMCW), dynamic range, range and Doppler estimation, constant false alarm rate (CFAR) detector.

4. Fundamentals of multi-input multi-output (MIMO) radar and waveform orthogonality.

5. Basics of antenna array, beamforming, direction-of-arrival (DOA) estimation.

6. High resolution spectrum estimation algorithms, including compressive sensing, subspace methods.

There is no textbook. Classic papers will be distributed for discussion.

1. Introduction to environmental perception of autonomous driving with multi sensing modalities.

2. Radar fundamentals including radar equations, link budget analysis, radar waveforms and ambiguity function (AF).

3. Principle of linear frequency modulated continuous waveform (FMCW), dynamic range, range and Doppler estimation, constant false alarm rate (CFAR) detector.

4. Fundamentals of multi-input multi-output (MIMO) radar and waveform orthogonality.

5. Basics of antenna array, beamforming, direction-of-arrival (DOA) estimation.

6. High resolution spectrum estimation algorithms, including compressive sensing, subspace methods.

There is no textbook. Classic papers will be distributed for discussion.

## ECE 408/508 Communications and Labs, Spring 2020, Spring 2021, Spring 2022

Introduction to analog and digital communication systems, baseband and bandpass signal representations, power spectrum density, filtering, amplitude and angle modulations, analog-to-digital converter, sampling theorem, random process, advanced digital modulation/demodulation, optimal receiver design, channel equalization and system performance analysis.

Textbook:

[1] John G. Proakis and Masoud Salehi, Fundamentals of Communication Systems (2nd Edition), Pearson, 2013. [Required]

[2] Upamanyu Madhow, Introduction to Communication Systems, Cambridge University Press, 2014. [Used for Labs]

[3] Leon W. Couch, Digital & Analog Communication Systems (8th Edition), Pearson, 2013. [Reference]

Textbook:

[1] John G. Proakis and Masoud Salehi, Fundamentals of Communication Systems (2nd Edition), Pearson, 2013. [Required]

[2] Upamanyu Madhow, Introduction to Communication Systems, Cambridge University Press, 2014. [Used for Labs]

[3] Leon W. Couch, Digital & Analog Communication Systems (8th Edition), Pearson, 2013. [Reference]

## ECE 370 Signals and Systems, Fall 2020, Fall 2021, Fall 2022

Introduction to time domain and frequency domain analysis of continuous and discrete signals and systems, Fourier series, Fourier transform, Laplace transform, Z-transform, numerical implementation using Matlab.

Textbook:

[1] A. V. Oppenheim and A. S. Willsky, Signals and Systems (2nd Edition), Pearson, 1996.

Textbook:

[1] A. V. Oppenheim and A. S. Willsky, Signals and Systems (2nd Edition), Pearson, 1996.

## ECE 320 Fundamentals of Electrical Engineering, Fall 2019

Introduction to circuit analysis, methods, resistive circuits, AC circuits, first order transients, AC power, operational amplifiers, and machines.

Textbook:

[1] G. Rizzoni and J. Kearns, Principles and Applications of Electrical Engineering (6th Edition), McGraw Hill, 2016.

Textbook:

[1] G. Rizzoni and J. Kearns, Principles and Applications of Electrical Engineering (6th Edition), McGraw Hill, 2016.