Ship detection of synthetic aperture radar (SAR) images is one of the research hotspots in the field of marine surveillance. Fusing salient features to detection network can effectively improve the precision of ship detection. However, how to effectively fuse the salient features of SAR images is still a difficult task. In this paper, to improve the ship detection precision, we design a novel one-stage ship detection network to fuse salient features and deep convolutional neural network (CNN) features. Firstly, a saliency map extraction algorithm is proposed. The algorithm is applied to generate saliency map by using multi-scale pyramid features and frequency domain features. Secondly, the backbone of the ship detection network contains a two-stream network. The upper-stream network uses the original SAR image as input to extract multi-scale deep CNN features. The lower-stream network uses the corresponding saliency map as input to acquire multi-scale salient features. Thirdly, for integrating the salient features to deep CNN features, a novel salient feature fusion method is designed. Finally, an improved bi-directional feature pyramid network is applied to the ship detection network for reducing the computational complexity and network parameters. The proposed methods are evaluated on the public ship detection dataset and the experimental results shows that it can make a significant improvement in the precision of SAR image ship detection. INDEX TERMS Ship detection, synthetic aperture radar images, feature fusion, saliency map, deep convolutional neural network.
A new model is presented to resolve cycle slips detection for triple-frequency observations of BeiDou navigation satellite system (BDS) in this paper when pseudorange observations are missing or insufficiently accurate under harsh or special situations. Based on the first-order time-difference geometryfree (GF) pseudorange-phase combination model, the new cycle slips detection and correction method based on the triple-frequency carrier phase and Doppler observations is proposed. With analyses on the two common sampling intervals (30 s and 1 s), it can be concluded that the optimal combination coefficients of the proposed model relate to sampling intervals. Combinations [4,-2,-3], [-1,-5,6], and [-3,6,-2] are selected to detect and correct cycle slips for 30 s sampling interval, while combinations [0,-1,1], [1,0,-1], and [-3,2,2] are selected for 1 s sampling interval. The validity of the phase-Doppler combination model under the static condition and the steady ionosphere with 30 s sampling interval and 1 s sampling interval is verified by two static experiments. Results show that the phase-Doppler combination model can achieve the same performance as the pseudorange-phase combination model. All the small, insensitive, and large cycle slips added to the three types of BDS satellites which separately belong to Geostationary Earth Orbit (GEO), Inclined Geosynchronous Orbit (IGSO), and Medium Earth Orbit (MEO) are detected and corrected successfully by the proposed model.
The Shannon-Nyquist sampling theorem that is based on narrowband interference (NBI) detection and parameter estimation methods in direct sequence spread spectrum (DSSS) communication is limited by the high sampling rate. Compressive sensing (CS) is adopted to address the problem. But it will change the signal nature, which leads to the unavailability of Shannon-Nyquist sampling theorem-based interference detection and parameter estimation methods. According to the different posterior probability distribution features of NBI, DSSS signal, and noise in compressed domain, a posterior probability model of whether NBI exists in the received signal is constructed by using the compressed measurements. The posterior probability that whether NBI exists in the received signal is employed as the feature parameters of NBI detection and parameter estimation. With the feature parameters detected, NBI detection can be achieved and the edge location of NBI components can be located. The relationship between the edge location of NBI components and the edge frequency of NBI is constructed, which will contribute to estimate the NBI edge frequency. The numerical simulation results demonstrate that the proposed method can effectively achieve NBI detection and parameter estimation in the compressed domain and it performs significantly better than the other existing methods.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
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