Aeronautical telemetry suffers from multipath interference, which can be resolved through the use of equalizers at the receiver. The coefficients of data-aided equalizers are computed from a channel estimate. Most channels seen in aeronautical telemetry are sparse, meaning that most of the coefficients of the channel are zero or nearly zero. The maximum likelihood (ML) estimate does not always produce a sparse channel estimate. This thesis surveys a number of sparse estimation algorithms that produce a sparse channel estimate and compares the post-equalizer bit error rates (BER) using these sparse estimates with the post-equalizer BER using the ML estimate. I show that the generalized Orthogonal Matching Pursuit (GOMP) performs the best followed by the Sparse Estimation based on Validation Re-estimated Least Squares (SPARSEVA-RE) and the Least Absolute Shrinkage and Selection Operator (LASSO).
| Identifer | oai:union.ndltd.org:BGMYU2/oai:scholarsarchive.byu.edu:etd-7391 |
| Date | 01 June 2017 |
| Creators | Hogstrom, Christopher James |
| Publisher | BYU ScholarsArchive |
| Source Sets | Brigham Young University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | All Theses and Dissertations |
| Rights | http://lib.byu.edu/about/copyright/ |
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