Multipath exploitation radar (MER) integrates the prior environment to make use of the extra target information encoded in multipath signals, which is capable of localization for single target with a wide-beam antenna. Nevertheless, as the number of targets in the urban scene increases, the association between the target and its corresponding multipath’s time-of- arrival (ToA) faces the problem of combination explosion. Moreover, accounting for the measurement and extraction error attached with the ToAs, there may be multiple combinations with similar probability, which leads to a significant accuracy decline or even wrong location results. To solve the problem, this paper proposes a novel algorithm that jointly uses MER and time-frequency (TF) features for multi-target localization especially for pedestrians. Through the TF analysis of the micro-Doppler feature, the pedestrian characteristics such as pace and phase of periodic action can be obtained. Based on these characteristics, the multipath’s ToAs in multi-target scenario can be associated with each different target, hence the aforementioned multi-target location problem can be transferred to a series of single-target localization problems. The impacts of target number on localization accuracy are analyzed in detail. The effectiveness of the proposed method is validated through the simulation experiments. The results indicate that, compared with the traditional methods, the proposed method has higher localization accuracy in multi-target scene.
Compared with traditional sparsity-driven methods, inverse synthetic aperture radar (ISAR) image enhancement method based on convolutional neural networks (CNNs) have outstanding performance in recent research, which improved the resolution of reconstructed image significantly with higher imaging efficiency. However, recently developed ISAR image enhancement methods based on neural networks are only effective in the same scenarios where the training data was generated. Additionally, all these method adopted the mean-squared error as the loss function, causing the reconstructed ISAR image to lose high-frequency information and fail to capture appropriate details. To address these limitations, a single ISAR image enhancement framework based on a modified super-resolution convolutional neural network (SRCNN) is proposed in this paper. The ISAR image enhancement processing framework were improved to minimize the influence of the fixed imaging model. A combined loss function, composed of the structural similarity (SSIM) loss and the L1 loss functions, was adopted in the proposed framework to retain the high-frequency information and the luminance information of the ISAR image, while improving the resolution. Through quantitative analysis of experimental results by using different quality evaluation indicators, it demonstrated that compared with extant methods, the proposed framework provides reconstructed ISAR images with higher resolution and definition over a range of different scenarios.
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