High resolution range profile (HRRP) is being known as one of the most powerful tools for radar target recognition. The main problem with range profile for radar target recognition is its sensitivity to aspect angle. To overcome this problem, consecutive samples of HRRP were assumed to be identically independently distributed (IID) in small frames of aspect angles in most of the related works. Here, considering the physical circumstances of maneuver of an aerial target, we have proposed dynamic system which models the short dependency between consecutive samples of HRRP in a segment of the whole HRRP sequence. Dynamic system (DS) is used to model the sequence of PCA (principal component analysis) coefficients extracted from the sequence of HRRPs. Considering this, we have proposed a method called PCA+DS. we have also proposed a segmentation algorithm which segments the HRRP sequence reliably. Experimental results using simulated data showed that our method based on PCA+DS achieves better recognition results compared to the method based on factor analysis (FA). © 2014 IEEE.
Radar target recognition using dynamic system model / Ajorloo, A.; Hadavi, M.; Bastani, M. H.; Nayebi, M. M.. - (2014), pp. 1446-1450. ( 2014 IEEE Radar Conference, RadarCon 2014 Cincinnati, OH, usa ) [10.1109/RADAR.2014.6875828].
Radar target recognition using dynamic system model
Ajorloo A.;
2014
Abstract
High resolution range profile (HRRP) is being known as one of the most powerful tools for radar target recognition. The main problem with range profile for radar target recognition is its sensitivity to aspect angle. To overcome this problem, consecutive samples of HRRP were assumed to be identically independently distributed (IID) in small frames of aspect angles in most of the related works. Here, considering the physical circumstances of maneuver of an aerial target, we have proposed dynamic system which models the short dependency between consecutive samples of HRRP in a segment of the whole HRRP sequence. Dynamic system (DS) is used to model the sequence of PCA (principal component analysis) coefficients extracted from the sequence of HRRPs. Considering this, we have proposed a method called PCA+DS. we have also proposed a segmentation algorithm which segments the HRRP sequence reliably. Experimental results using simulated data showed that our method based on PCA+DS achieves better recognition results compared to the method based on factor analysis (FA). © 2014 IEEE.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.


