The recognition of partially occluded targets is a difficult problem in the field of synthetic aperture radar (SAR) target recognition. To eliminate the effect of occlusion, the intuitive idea is to determine the exact location and the size of the occluded area. However, this is very difficult, even impossible in practice. In order to avoid this difficulty and to improve the recognition performance for the partially occluded target, a fusion strategy of the sparse representation (SR) model based on randomly erased images is proposed to recognize the partially occluded target. The proposed method randomly erases some areas many times in both the test samples and the training samples. The erased training samples in each erasure are used to sparsely represent the corresponding erased test sample. Finally, all the SR results are fused to recognize the test sample. The proposed method utilizes random erasure to eliminate the possible occluded region. In addition, this method uses the fusion strategy to overcome under-erasing of the occluded region and erroneous erasure of the unoccluded region. The key parameter of the proposed method is the erasure ratio only. Although the erasure is random, the recognition performance of the method is relatively stable. Therefore, the method can eliminate the influence of occlusion without determining the details of occlusion. The experimental results show that the proposed method is significantly better than the state-of-the-art methods in the case of occlusion. Additionally, the recognition performance of the proposed method is similar to some comparison methods in the case of no occlusion.

The doughnut-shaped radiation energy pattern of orbital angular momentum (OAM) beams has been a longstanding challenge in electromagnetic (EM) vortex imaging. In this paper, an effective EM vortex imaging scheme is developed based on the orthogonal polyphase-coded OAM multiplexing beam with mitigated beam energy divergence. Firstly, vortex EM waves are modulated with different orthogonal polyphase codes and transmitted simultaneously to illuminate the imaging scenario. The codes change the phase relationship between different transmitted signals, leading to a convergent energy distribution around the beam axis. Then, an OAM demultiplexing method is proposed to separate the radar echo of each OAM mode, which maintains the individual spiral phase fronts at the same time. Finally, the two-dimensional fast Fourier transform (FFT) is applied to the demodulated signals to reconstruct the target image. Simulation results show that the proposed imaging method has a higher azimuth resolution than the traditional method with small elevation angles and performs much better in low SNR environments. Furthermore, an experiment in an anechoic chamber validates the potential of the proposed method. This work can benefit the practical applications of OAM imaging technology.

A novel machine learning method named extended support vector data description with negative examples (ESVDD‐neg) is developed to classify the fast Fourier transform‐magnitude feature of complex high‐resolution range profile (HRRP), motivated by the problem of radar automatic target recognition. The proposed method not only inherits the close non‐linear boundary advantage of support vector data description with negative examples model but also incorporates a new learning paradigm named learning using privileged information into the model. It leads to the appealing application with no assumptions regarding the distribution of data and needs less training samples and prior information. Besides, the second order central moment is selected as privileged information for better recognition performance, weakening the effect of translation sensitivity, and the normalisation contributes to eliminating the amplitude sensitivity. Hence, there will be a remarkable improvement of recognition accuracy not only with small training dataset but also under the condition of low signal‐to‐noise ratio. Numerical experiments based on two publicly UCI datasets and HRRPs of four aircrafts demonstrate the feasibility and superiority of the proposed method. The noise robust ESVDD‐neg is ideal for HRRP‐based radar target recognition.

Partially occluded target recognition is a problem that is rarely studied but must be faced in the field of synthetic aperture radar target recognition. The difficulty of this problem is finding out the location of the occluded area and eliminating the effects of occlusion. In this paper, an adaptive weighting method based on subimage sparse model is proposed to recognize the partially occluded targets. This method divides the test image (query) and the dictionary into subimages and subdictionaries by the same gridding method. Then, the sparse reconstruction error of each subimage is calculated to design weighting coefficients for the subimage. By multiplying the subimages and subdictionaries by the corresponding weighting coefficients, the weighted query and the weighted dictionary are obtained. Finally, the sparse representation recognition method is utilized to classify the weighted query. The core idea of the proposed method is that the sparse reconstruction error of the image is defined as the sum of the sparse reconstruction error of all the image pixels, and the difference of the sparse reconstruction error of the subimage between unoccluded condition and occluded condition is obvious. So the sparse reconstruction error of the subimage can be easily extracted and used as an indicator for determining the occluded area. By setting small weighting coefficients in the occluded area, the influence of occlusion is reduced and the recognition performance of partially occluded target is improved. Experimental results demonstrate that the proposed method leads to better recognition result in comparison to some state-of-the-art methods under the occlusion condition.

A forward–backward labeled multi-Bernoulli (LMB) smoother is proposed for multi-target tracking. The proposed smoother consists of two components corresponding to forward LMB filtering and backward LMB smoothing, respectively. The former is the standard LMB filter and the latter is proved to be closed under LMB prior. It is also shown that the proposed LMB smoother can improve both the cardinality estimation and the state estimation, and the major computational complexity is linear with the number of targets. Implementation based on the Sequential Monte Carlo method in a representative scenario has demonstrated the effectiveness and computational efficiency of the proposed smoother in comparison to existing approaches.

When conventional motion compensation algorithms that are fit for a single target are applied to cooperative targets imaging, a well-focused image cannot be obtained due to the low correlation between adjacent returned signals. In this paper, a parametric compensation method is proposed for the imaging of cooperative targets. First, the problem of the imaging is formulated by analyzing the translational motion of the target moving along a rectilinear fight path and by assuming a signal model of cooperative targets imaging. A bulk image is then obtained by parametric compensation of the linear and quadratic phase terms, which is performed by means of estimating the translational motion parameters through the fractional Fourier transform. Next, the number of targets in the bulk image is estimated by clustering number estimation, and the segmented images from the bulk image are separated by the normalized cuts. Finally, well-focused images are obtained by refined parametric compensation of the residual quadratic and cubic phase terms, which is carried out by estimating the parameters through maximizing the image contrast. Simulation results demonstrate the effectiveness of the proposed method.