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Spatial Signal Processing with Emphasis on Emitter Localization

Spatial Signal Processing with Emphasis on Emitter Localization. Anthony J. Weiss. What to expect?. The course covers the important theory developments associated with Emitter Location from 1939. We will try to obtain detailed understanding of the results

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Spatial Signal Processing with Emphasis on Emitter Localization

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  1. Spatial Signal Processing with Emphasis on Emitter Localization Anthony J. Weiss Spatial Signal Processing

  2. What to expect? • The course covers the important theory developments associated with Emitter Location from 1939. • We will try to obtain detailed understanding of the results • We will not discuss in detail “existing systems” • After the course you should be able to: • Recommend a localization technique • Evaluate a theoretical Lower bound on performance • Estimate the localization performance as a function of sensors-source geometry, SNR, observation time, signal parameters (bandwidth, center frequency), model errors, localization technique, etc. Spatial Signal Processing

  3. Contents • Introduction • Understanding Geographic Coordinates • Complex representation of RF Signals • Complex Random variables and vectors - probability density function and the complex gradient operator • Lower Bounds on Parameter Estimation Errors • Statistical Theory of Passive Location Systems (CEP, GDOP, uncertainty ellipse) • DOA Estimation of Single Signal • MLE – Deterministic Unknown Signal • MLE – Known Signal • MLE – Gaussian Signal • CRLB - Deterministic Unknown Signal • CRLB – Known Signal • CRLB – Gaussian Signal • Beam-forming • Capon’s Beam-former • Null Steering • Diversely Polarized Signals and Antennas Spatial Signal Processing

  4. Contents • DOA Estimation Multiple Signals • MLE – Deterministic Unknown Signals • MLE – Known Signals • MLE – Gaussian Signals • CRLB - Deterministic Unknown Signals • CRLB – Known Signals • CRLB – Gaussian Signals • MUSIC Algorithm • Beam space MUSIC • Root MUSIC • IQML • ESPRIT • Weighted Subspace Fitting • EM Algorithm • Alternating Projection • MODE (optional) • Mono-pulse (optional) • Detection of Signal Subspace Dimension Spatial Signal Processing

  5. Contents • DOA Based Localization • MLE and CRLB • Stansfield’s Algorithm • DOA GDOP • DOA CEP • TOA/DTOA Measurements • Methods (generalized CC, Leading Edge) • Lower bounds (CRLB, Modified Ziv-Zakai) • Localization based on TDOA/TOA • Maximum Likelihood • DPD • Closed form solutions • Received Signal Strength Measurements • Localization based on RSS • DDOP Measurements • DDOP Localization • DDOP+DTOA Localization • DPD (Direct Position Determination) • Outliers Spatial Signal Processing

  6. Contents • Calibration of time, location, gain, phase array orientation • Model Errors • Localization of nodes in Sensors network • Single Site Location • Robust Estimator (M estimator, L estimators, p-norm) • Employment of sparsity for outliers removal Spatial Signal Processing

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