کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6939798 870056 2017 15 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Radar HRRP target recognition with deep networks
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
Radar HRRP target recognition with deep networks
چکیده انگلیسی
Feature extraction is the key technique for radar automatic target recognition (RATR) based on high-resolution range profile (HRRP). Traditional feature extraction algorithms usually utilize shallow architectures, which result in the limited capability to characterize HRRP data and restrict the generalization performance for RATR. Aiming at those issues, in this paper deep networks are built up for HRRP target recognition by adopting multi-layered nonlinear networks for feature learning. To learn the stable structure and correlation of targets from unlabeled data, a deep network called Stacked Corrective Autoencoders (SCAE) is further proposed via taking the advantage of the HRRP's properties. As an extension of deep autoencoders, SCAE is stacked by a series of Corrective Autoencoders (CAE) and employs the average profile of each HRRP frame as the correction term. The covariance matrix of each HRRP frame is considered for establishing an effective loss function under the Mahalanobis distance criterion. We use the measured HRRP data to show the effectiveness of our methods. Furthermore, we demonstrate that with the proper optimization procedure, our model is also effective even with a moderately incomplete training set.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 61, January 2017, Pages 379-393
نویسندگان
, , ,