A Multi-Channel Radar Forward-Looking Imaging Algorithm Based on Super-Resolution Technique

التفاصيل البيبلوغرافية
العنوان: A Multi-Channel Radar Forward-Looking Imaging Algorithm Based on Super-Resolution Technique
المؤلفون: Jiang Zhangtao, She Caiyun, Shen Jun, Yang Chengjie, Xiaodong Zou
المصدر: 2018 China International SAR Symposium (CISS).
بيانات النشر: IEEE, 2018.
سنة النشر: 2018
مصطلحات موضوعية: Signal processing, business.industry, Computer science, Resolution (electron density), ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Estimator, Image (mathematics), law.invention, Azimuth, Target angle, law, Monopulse radar, Computer vision, Artificial intelligence, Radar, business
الوصف: Owing to the ability of accurate angle estimation, monopulse technique can be applied in airborne or missile-borne radar forward-looking imaging procedure. However due to the limited spatial domain DOF (degree of freedom), when more than one target appears in beam simultaneously, monopulse technique will fail to estimate angle of targets accurately. To deal with this problem, the multi-channel radar forward-looking imaging algorithm based on super resolution, which can further improve the azimuth resolution as compared to monopulse imaging algorithm, is investigated. By introducing MUSIC (multiple signal classification) algorithm into multi-channel radar forward-looking imaging procedure, multi-channel forward-looking imaging algorithm is proposed, with estimation of source number completed by GDE (Gerschrion disk estimator) algorithm. The entire signal processing steps are provided in detail in this paper. At last, the performance of the proposed algorithm is evaluated by simulation results, with the spatial domain DOF increased, accuracy of target angle estimation is improved and the performance of azimuth resolution of forward-looking area image is promoted.
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_________::a49509696e9aac08eb5853b02b64210f
https://doi.org/10.1109/sars.2018.8552029
رقم الأكسشن: edsair.doi...........a49509696e9aac08eb5853b02b64210f
قاعدة البيانات: OpenAIRE