Knowledge-Based Adaptive Beamsteering for Enhanced Detection and Interference Mitigation
Abstract
Modern-day radar systems continue to increase their adaptability to our changing world. One adaptation is the continued improvement of detection-and-track functionality. Knowledge-based (KB) adaptive beamsteering utilizes a probabilistic representation of a search area to improve overall detection-and-track performance. In this thesis, we consider the search-and-detection problem using adaptive beamsteering for 1-dimensional (1-D) and 2-dimensional (2-D) spatial illumination. We propose a technique that directs the antenna beam on a region of space or cell area with the largest probability as opposed to a popular technique that utilizes the largest uncertainty. To compare detection performance, we allow the whole angular space being interrogated to be interfered with correlated random interference and/or jammers as opposed to discrete interferers. To evaluate performance, we vary the interference or jammer to noise power ratio and the jammer to signal power ratio. The results demonstrate that adaptive beamsteering, when allowed to capitalize on the probability map, can improve detection probability significantly and mitigate random interference and jammer noise.
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 01, 2021
- Accession Number
- AD1221938
Entities
People
- Monica T. Lavris
Organizations
- Naval Postgraduate School