Optimization of Orthogonal Waveform Using Memetic Algorithm with Iterative Greedy Code Search
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| Vydáno v: | Remote Sensing vol. 17, no. 5 (2025), p. 856 |
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| Další autoři: | , |
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MDPI AG
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| On-line přístup: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 3176395066 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2072-4292 | ||
| 024 | 7 | |a 10.3390/rs17050856 |2 doi | |
| 035 | |a 3176395066 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231556 |2 nlm | ||
| 100 | 1 | |a Wang, Wanbin | |
| 245 | 1 | |a Optimization of Orthogonal Waveform Using Memetic Algorithm with Iterative Greedy Code Search | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The orthogonality of transmitted waveforms is an important factor affecting the performance of MIMO radar systems. The orthogonal coded signal is a commonly adopted waveform in MIMO radar, and its orthogonality depends on the used orthogonal discrete code sequence set (ODCSs). Among existing optimization algorithms for ODCSs, the results designed by the greedy code search-based memetic algorithm (MA-GCS) have exhibited the best autocorrelation and cross-correlation properties observed so far. Based on MA-GCS, we propose a novel hybrid algorithm called the memetic algorithm with iterative greedy code search (MA-IGCS). Extensions involve replacing the greedy code search used in MA-GCS with a more efficient approach, iterative greedy code search. Furthermore, we propose an “individual uniqueness strategy” and incorporate it into our algorithm to preserve population diversity throughout iteration, thereby preventing premature stagnation and ensuring the continued pursuit of feasible solutions. Finally, the design results of our algorithm are compared with the MA-GCS. Experimental results demonstrate that the MA-IGCS exhibits superior search capability and generates more favorable design results than the MA-GCS. | |
| 653 | |a MIMO communication | ||
| 653 | |a Waveforms | ||
| 653 | |a Deep learning | ||
| 653 | |a Algorithms | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Searching | ||
| 653 | |a Optimization | ||
| 653 | |a Greedy algorithms | ||
| 653 | |a Radar equipment | ||
| 653 | |a Code Division Multiple Access | ||
| 653 | |a Design | ||
| 653 | |a Methods | ||
| 653 | |a Cross correlation | ||
| 653 | |a Heuristic | ||
| 653 | |a Optimization algorithms | ||
| 653 | |a Radar systems | ||
| 653 | |a Orthogonality | ||
| 653 | |a Efficiency | ||
| 653 | |a Parameter estimation | ||
| 700 | 1 | |a Lu, Qian | |
| 700 | 1 | |a Zhou, Yun | |
| 773 | 0 | |t Remote Sensing |g vol. 17, no. 5 (2025), p. 856 | |
| 786 | 0 | |d ProQuest |t Biological Science Index | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3176395066/abstract/embedded/ITVB7CEANHELVZIZ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3176395066/fulltextwithgraphics/embedded/ITVB7CEANHELVZIZ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3176395066/fulltextPDF/embedded/ITVB7CEANHELVZIZ?source=fedsrch |