OA3602 Search Theory and Detection

Search and detection as stochastic processes. Characterization of detection devices, use and interpretation of sweep widths and lateral range curves, true range curves. Measures of effectiveness of search-detection systems. Allocation of search efforts, sequential search. Introduction to the statistical theory of signal detection. Models of surveillance fields, barriers, tracking and trailing.

Prerequisite

OS2103 or OA3101

Lecture Hours

4

Lab Hours

0

Course Learning Outcomes

·      Demonstrate an understanding of Search Path and Coverage Models to include exhaustive search (cookie-cutter detection, detection time distribution, circle packing), barrier search (target coverage, advancing and retiring barriers, non-uniformly distributed barrier crossing point, random target speeds, and barrier search game bounds and solution), trapping circles and spirals (modeling with ordinary differential equations, computing trapping spiral path, Archimedes spiral and log spiral search), and the effect of counter-detection.

·      Demonstrate an understanding of Firing Theory to firing errors (dispersion), target location errors (bias), damage radius errors, cookie-cutter weapon, diffuse Gaussian weapons, and circular error probable (CEP).

·      Demonstrate an understanding of Discrete and Continuous Search to include probability distribution of random number of discrete glimpses for initial detection, probability distribution for random time to initial detection for homogeneous and non-homogeneous detection rate models, with application of random search for Bayesian updated of target location probability map.

·      Demonstrate an understanding of Sonar Models to include decibel units, Passive Sonar Equation, Instantaneous Probability of Detection, modeling detection probability over a time interval with the Poisson Scan Model and Lambda-Sigma Detection Model.

·      Demonstrate an understanding of Lateral Range Curves and Sweep Width.

·      Demonstrate an understanding of Optimal Search for a Stationary Target including Discrete Search and Continuous Search.

·      Demonstrate an understanding of Receiver Operating Characteristic (ROC) Curves to include probability of detection, probability of false alarm, derivation of ROC Curve for a single look for a deterministic signal in Gaussian noise, generalization for n-looks, and optimization of detection threshold.