2026-05-17T18:12:44Zhttps://keep.lib.asu.edu/oai/request

oai:keep.lib.asu.edu:node-2014652025-05-12T19:35:22Zoai_pmh:alloai_pmh:repo_items

201465 https://hdl.handle.net/2286/R.2.N.201465 http://rightsstatements.org/vocab/InC/1.0/ All Rights Reserved 2025 75 pages Masters Thesis Academic theses en Zhang, Pinjia Papandreou-Suppappola, Antonia Chakrabarti, Chaitali Herschfelt, Andrew Arizona State University Partial requirement for: M.S., Arizona State University, 2025 Field of study: Electrical Engineering Sharing spectrum between radar and communication systems is essential to support the rapid advances in wireless technology with only a limited amount of resources. One of the many challenges that radar faces when sharing bandwidth is the large increase in interference due to the presence of multiple communication users. The presence of communication signals within the same frequency band may degrade the radar performance, leading to false detection or large errors in estimating tracking parameters. As a result, advanced signal processing methods are needed to reduce the effect of the interference. At the same time, real-time processing requirements can limit the complexity of such algorithms. This thesis considers the problem of estimating the position of a target when the radar is sharing spectrum with multiple communication users and thus faces high levels of interference. We propose an adaptive estimation method that aims to maintain high estimation accuracy while reducing the need for computationally intensive estimation algorithms. The adaptive method selects between two estimation algorithms using a threshold that depends on the signal-to-interference-plus-noise ratio (SINR). The first method is the asymptotically efficient maximum-likelihood estimator. Although fast to implement, this estimator approaches optimal performance—because it maximizes the measurement-likelihood function—only at high SINRs. The second method is computationally intensive to implement but maintains high accuracy at low SINR. This method is our extension of a learning-based approach that combines time-frequency feature extraction with Gaussian-mixture modeling. Using simulations with radar operating at very high spectrum-sharing frequencies, we demonstrate the computational efficiency and estimation performance of the proposed adaptive method, when compared to other methods, under time-varying SINR conditions. Electrical Engineering Computationally Efficient Estimation Feature-Based Learning High-Frequency Communication Spectrum Sharing Time-Varying Environment Feature Based Learning for Computation-Efficient Estimation in High Frequency Time-varying Spectrum Sharing Environments