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Development of a ML Algorithm to Improve Radar Detection of Golf Balls Using MFCW Radar
Thesis (MEng)--Stellenbosch University, 2026.
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| Format: | Thesis |
| Language: | English |
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Stellenbosch : Stellenbosch University
2026
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| _version_ | 1867613861452972032 |
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| access_status_str | Open Access |
| author | Holtshausen, Damian Luke |
| author2 | Grootboom, L. L. |
| author_browse | Grootboom, L. L. Holtshausen, Damian Luke |
| author_facet | Grootboom, L. L. Holtshausen, Damian Luke |
| author_sort | Holtshausen, Damian Luke |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (MEng)--Stellenbosch University, 2026. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/136067 |
| institution | Stellenbosch University (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:42:51.481Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/136067 Development of a ML Algorithm to Improve Radar Detection of Golf Balls Using MFCW Radar Holtshausen, Damian Luke Grootboom, L. L. Steyn, W. Niesler, T. Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering. Thesis (MEng)--Stellenbosch University, 2026. Holtshausen, D. L. 2026. Development of a ML Algorithm to Improve Radar Detection of Golf Balls Using MFCW Radar. Unpublished masters thesis. Stellenbosch: Stellenbosch University [online]. Available: https://scholar.sun.ac.za/items/4937e1fc-e3d8-4bf4-9cfe-98ea33d8af2c This thesis addresses the challenge of reliably detecting small targets, specifically golf balls, using Multi-Frequency Continuous-Wave (MFCW) radar in environments contaminated by significant noise and clutter. Although radar systems are effective at tracking targets, the performance of a radar system is heavily constrained by unwanted returns from external sources, such as fans, which often generate Doppler frequencies comparable to the target return signal. A comprehensive MFCW radar system simulation was developed, incorporating realistic physics and detailed models of critical interference targets. This enabled the design and testing of various detection algorithms. This thesis compares Constant False Alarm Rate (CFAR) detectors (including CA-, OS-, VI-, TM-, CHA- and WAI-CFAR) against modern Machine Learning (ML) techniques, including multinomial and binary Logistic Regression (LR) and Gradient Boosted Tree classifiers (XGBoost). For the ML models, a bin specific classification approach was explored, which utilised features which were engineered from statistical properties and signal peak characteristics. The models were trained on data collected from four international golf driving ranges. Results confirmed that classical CFAR detectors struggled in this cluttered environment, as seen by the false positive rates (FPR) which far exceeded the threshold that each detector was designed for. In contrast, the ML techniques, specifically XGBoost, which was used in a bin specific classifier, performed significantly better. The best model obtained a Recall (Probability of Detection) of 88.84%, with a False Positive Rate (FPR) of 0.431%. While the best performing CFAR detector, OS-CFAR obtained a recall of 81.42% with an FPR of 0.635%. These results indicate that ML models are capable of learning complex features beyond what is capable with CFAR detectors, allowing for more reliable detection of targets in environments with a low Signal to Clutter Ratio (SCR). Masters 2026-04-21T13:02:42Z 2026-04-21T13:02:42Z 2026-03 Thesis https://scholar.sun.ac.za/handle/10019.1/136067 en Stellenbosch University 116 pages : ill. application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Holtshausen, Damian Luke Development of a ML Algorithm to Improve Radar Detection of Golf Balls Using MFCW Radar |
| title | Development of a ML Algorithm to Improve Radar Detection of Golf Balls Using MFCW Radar |
| title_full | Development of a ML Algorithm to Improve Radar Detection of Golf Balls Using MFCW Radar |
| title_fullStr | Development of a ML Algorithm to Improve Radar Detection of Golf Balls Using MFCW Radar |
| title_full_unstemmed | Development of a ML Algorithm to Improve Radar Detection of Golf Balls Using MFCW Radar |
| title_short | Development of a ML Algorithm to Improve Radar Detection of Golf Balls Using MFCW Radar |
| title_sort | development of a ml algorithm to improve radar detection of golf balls using mfcw radar |
| url | https://scholar.sun.ac.za/handle/10019.1/136067 |
| work_keys_str_mv | AT holtshausendamianluke developmentofamlalgorithmtoimproveradardetectionofgolfballsusingmfcwradar |