Dual-circular polarimetric (DCP) high-resolution range profile (HRRP) provides similar target information to the full polarimetric HRRP and has the same data volume as the dual-linear polarimetric HRRP, thus it is significant to investigate the capability of DCP HRRP for target recognition. In this paper, a novel feature extraction method is proposed for the DCP HRRP-based target recognition. First, due to the good capability of characterizing the target polarimetric structure, the m-χ decomposition is exploited to obtain three scattering components of targets corresponding to the odd-bounce, even-bounce and randomly polarized scattering mechanisms along the radar line-of-sight (LOS), respectively. However, these three scattering components are infeasible to be directly utilized for target recognition due to the high dimensionality and redundancy. Considering this, a novel feature fusion method named as sparsity-preserving discriminant correlation analysis (SPDCA) is proposed to fuse the three scattering components. The SPDCA method can obtain the low-dimensional projection feature with good class separability by preserving both the global structure and local sparsity property of the original data. Besides, the redundancy of the three scattering components is eliminated by the SPDCA method. The results of experiments conducted on the simulated data of 10 civilian vehicles and real data of 3 military vehicles demonstrate the effectiveness and robustness of the proposed feature extraction method.

For high-resolution range profile (HRRP)-based radar automatic target recognition (RATR), adequate training data are required to characterize a target signature effectively and get good recognition performance. However, collecting enough training data involving HRRP samples from each target orientation is hard. To tackle the HRRP-based RATR task with limited training data, a novel dynamic learning strategy is proposed based on the single-hidden layer feedforward network (SLFN) with an assistant classifier. In the offline training phase, the training data are used for pretraining the SLFN using a reduced kernel extreme learning machine (RKELM). In the online classification phase, the collected test data are first labeled by fusing the recognition results of the current SLFN and assistant classifier. Then the test samples with reliable pseudolabels are used as additional training data to update the parameters of SLFN with the online sequential RKELM (OS-RKELM). Moreover, to improve the accuracy of label estimation for test data, a novel semi-supervised learning method named constraint propagation-based label propagation (CPLP) was developed as an assistant classifier. The proposed method dynamically accumulates knowledge from training and test data through online learning, thereby reinforcing performance of the RATR system with limited training data. Experiments conducted on the simulated HRRP data from 10 civilian vehicles and real HRRP data from three military vehicles demonstrated the effectiveness of the proposed method when the training data are limited.

This paper presents a $\mathcal {G}_p^0$ mixture model-based ship detection method for polarimetric synthetic aperture radar (PolSAR) images. The proposed method is based on the assumption that target pixels can be regarded as a general class and that the PolSAR data contain abundant structure and textural information, which may help distinguish target from azimuth ambiguities and clutter. In the proposed method, a weighted combination of $\mathcal {G}_p^0$ distributions is used to characterize the PolSAR data to balance the complexity of parameter estimation and modeling accuracy. The proposed method is capable of automatically determining the number of class with an iterative expectation-maximization algorithm incorporating the $\mathcal {G}_p^0$ distribution. Besides, a prescreening process is integrated to realize computational acceleration. Instead of clustering all pixels in the PolSAR data, only potential target pixels selected in the prescreening stage are clustered. Therefore, fewer class is required to reach convergence due to the removal of most complex background. As a result, better computational efficiency can be achieved. After the clustering, the cluster corresponding to the targets can be distinguished conveniently with the averaged SPAN value of each cluster. The effectiveness and efficiency of the proposed method has been validated by using actual PolSAR datasets and by contrasting the proposed approach with other methods. Experimental results demonstrate its superiority in improving target detection rate while reducing false alarms caused by clutter and azimuth ambiguities.

Faced with increasingly complex electronic jamming environments, intra-pulse agility has become a primary method of anti-interrupted-sampling repeater jamming (ISRJ) for radar systems. However, existing intra-pulse agile signals suffer from high autocorrelation sidelobe levels and limited jamming suppression capabilities. These issues restrict the performance of intra-pulse agile signals in complex electromagnetic environments.This paper proposes an anti-interrupted-sampling repeater jamming method based on Simulated Annealing–2-optimization (SA-2opt) parallel optimization of waveforms and fractional domain extraction. Firstly, the proposed method employs the SA-2opt parallel optimization algorithm to optimize the joint frequency and chirp rate encoding waveform. Then, the received signal is subjected to the fractional Fourier transform (FrFT) and inverse transform to extract the target signal. Finally, jamming detection is conducted based on the multi-dimensional features of the pulse-compressed signal. After this detection, a time-domain filter is constructed to achieve jamming suppression. The optimized waveform exhibits the following advantages: the sub-pulses are orthogonal to each other, and autocorrelation sidelobe levels are as low as -20.7dB. The method proposed in this paper can achieve anti-ISRJ in the case of a high jamming-to-signal ratio (JSR). Simulation experiments validate both the effectiveness of the optimized waveform in achieving low autocorrelation sidelobes and the anti-ISRJ performance of the proposed method.