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Contents
Introduction: Sensor array processing and spectral estimation applications.
Review: Linear Algebra and Matrix Analysis.
Spectral Estimation Problem: Autocorrelation and power spectral density; random and deterministic signals, temporal spectral estimation applications.
Non-Parametric Methods: Periodogram and Correlogram. Improved Periodogram-based methods: Blackman Tukey, Bartlett Method, Welch Method.
Parametric Methods for Rational Spectra: Auto-regressive (AR), Moving average (MA), ARMA, Yule-Walker (YW), Least-Squares method
Parametric Methods for Line Spectra: Sinusoidal signal in noise model, covariance matrix model, nonlinear least squares (NLS), MUSIC, ESPRIT.
Filter Bank Methods: Filter bank interpretation of periodogram, Capon Method, Relation between Capon and Blackman Tukey, Relation between Capon and AR.
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.
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
Petre Stoica and Randolph Moses, “Spectral Analysis of Signals,” Prentice Hall, 2005.
H. L. Van Trees, "Optimum Array Processing - Part IV of Detection, Estimation and Modulation Theory," Wiley Interscience, 2002.
D. Manolakis, V. Ingle, and S. M. Kogon, "Statistical and Adaptive Signal Processing," Artech House, 2005.
Evaluation
Midterm Exam: 30%; Major Exam: 40%; Assignments & Term-Paper: 30%.
Audit Pass will require a minimum of 40%.
Attendance Policy: As per institute policy (One Grade Down if < 75%)
Lecture Timing: Tue & Fri (2-3.30 pm)
Venue: Bharti (IIA-101).
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