CRL 704: Sensor Array Signal Processing

Contents

1. Introduction: Sensor array processing and spectral estimation applications.

2. Review: Linear Algebra and Matrix Analysis.

3. Spectral Estimation Problem: Autocorrelation and power spectral density; random and deterministic signals, temporal spectral estimation applications.

4. Non-Parametric Methods: Periodogram and Correlogram. Improved Periodogram-based methods: Blackman Tukey, Bartlett Method, Welch Method.

5. Parametric Methods for Rational Spectra: Auto-regressive (AR), Moving average (MA), ARMA, Yule-Walker (YW), Least-Squares method

6. Parametric Methods for Line Spectra: Sinusoidal signal in noise model, covariance matrix model, nonlinear least squares (NLS), MUSIC, ESPRIT.

7. Filter Bank Methods: Filter bank interpretation of periodogram, Capon Method, Relation between Capon and Blackman Tukey, Relation between Capon and AR.

8. Spatial Array Processing: Array Model, ULA, Modulation-demodulation process, Optimum Beamforming, Generalized Sidelobe Canceller, Capon, MVDR, Adaptive beamforming. DOA estimation methods: NLS, YW-MUSIC, ESPRIT, Compressive Sensing based DoA estimation techniques.

9. Source localization: Methods for source localization: time of arrival (TOA), Time-delay of arrival (TDOA), angle of arrival (AoA), received signal strength (RSS)-based methods.

Pre-requisites: Signals and Systems, Communication Engineering, Basic Linear Algebra

References

1. Petre Stoica and Randolph Moses,  “Spectral Analysis of Signals,” Prentice Hall, 2005.

2. H. L. Van Trees, "Optimum Array Processing - Part IV of Detection, Estimation and Modulation Theory," Wiley Interscience, 2002.

3. D. Manolakis, V. Ingle, and S. M. Kogon, "Statistical and Adaptive Signal Processing," Artech House, 2005.

Evaluation

1. Midterm Exam: 30%;  Major Exam: 40%;  Assignments & Term-Paper: 30%.

2. Audit Pass will require a minimum of 35%.

Attendance Policy: No attendance policy.

Lecture Timing

Tue and Fri: 5.00-6.30 pm.

Venue: CARE Seminar Room (Block III-122).