In order to realise the fast detection of ships in synthetic aperture radar (SAR) images, a detection method combining visual saliency and a cascade convolutional neural network (CNN) is proposed. First, based on visual saliency, a multiscale spectral residual model is designed for realising the fast detection of ship candidate regions. Then, a cascaded CNN is designed, which consists of two convolution networks, namely, the front‐end shallow CNN, which is used to quickly exclude obvious non‐ship candidates and classify the ship candidates according to the ship orientation, and the back‐end deep CNN, which is used to detect high‐probability candidate regions with rotatable boundary boxes. The whole structure can realise the fast detection and precise positioning of ships with an arbitrary orientation. Finally, the authors conduct detailed experiments on the SAR ship image dataset. The experimental results show that the proposed method can effectively improve the detection accuracy of ships, ensuring the detection efficiency in SAR images.

Ground target three-dimensional positions measured from optical remote-sensing images taken by an unmanned aerial vehicle play an important role in related military and civil applications. The weakness of this system lies in its localization accuracy being unstable and its efficiency being low when using a single unmanned aerial vehicle. In this paper, a novel multi–unmanned aerial vehicle cooperative target localization measurement method is proposed to overcome these issues. In the target localization measurement stage, three or more unmanned aerial vehicles simultaneously observe the same ground target and acquire multiple remote-sensing images. According to the principle of perspective projection, the target point, its image point, and the camera’s optic center are collinear, and nonlinear observation equations are established. These equations are then converted to linear equations using a Taylor expansion. Robust weighted least-squares estimation is used to solve the equations with the objective function of minimizing the weighted square sum of re-projection errors from target points to multiple pairs of images, which can make the best use of the effective information and avoid interference from the observation data. An automatic calculation strategy using a weight matrix is designed, and the weight matrix and target-position coordinate value are updated in each iteration until the iteration stopping condition is satisfied. Compared with the stereo-image-pair cross-target localization method, the multi–unmanned aerial vehicle cooperative target localization method can use more observation information, which results in higher rendezvous accuracy and improved performance. Finally, the effectiveness and robustness of this method is verified by numerical simulation and flight testing. The results show that the proposed method can effectively improve the precision of the target’s localization and demonstrates great potential for providing more accurate target localization in engineering applications.

Optical axis pointing accuracy is an important index of airborne electro-optical (EO) platforms. In this work, we aim to correct the optical axis pointing angle of an airborne EO platform by digital compensation. First, a basic parameter model (BPM) of pointing error with clear physical significance is established by analyzing the physical structure and error source of the EO platform. Then, to suppress the nonlinear factors in the error, we propose an improved algorithm of a semi-parametric regression model based on the BPM. Numerical simulation analysis shows that the improved algorithm inherits the advantages of the BPM, such as fewer model parameters and clear physical significance, and can improve the correction effect. Finally, experimental results show that the mean square error of the azimuth angle is reduced from more than 110'' to less than 4'', and that of the elevation angle is reduced from more than 75'' to less than 3''. According to the results obtained, the proposed correction model can improve the optical axis pointing accuracy of an airborne EO platform quickly and effectively, which has significant application value.

One of the main methods for link quality prediction is to predict the physical layer parameters first, and then evaluate the link quality based on the mapping models between such parameters and packet reception ratio (PRR). However, existing methods often ignore the temporal correlations of physical layer parameter series and rarely consider the influence of link fluctuations, which lead to more errors under moderate and sudden changed links with larger fluctuations. In view of these problems, this paper proposes a more effective link quality prediction method RNN-LQI, which adopts Recurrent Neural Network (RNN) to predict the Link Quality Indicator (LQI) series, and then evaluates the link quality according to the fitting model of LQI and PRR. This method accurately mines the inner relationship among LQI series with the help of short-term memory characteristics of RNN and effectively deals with link fluctuations by taking advantage of the higher resolution of LQI in the transitional region. Compared with similar methods, RNN-LQI proves to be better under different link qualities. Especially under moderate and sudden changed links with larger fluctuations, the prediction error reduces at least by 14.51% and 13.37%, respectively. Therefore, the proposed method is more suitable for low power wireless links with more fluctuations.

Two‐unmanned‐aerial‐vehicle (UAV) cooperative target locating based on airborne visual tracking platform is a common UAV reconnaissance mode. In the process of two‐UAV cooperative target locating, the performance of target locating is highly dependent on UAV trajectory. In this Letter, a trajectory planning method for two‐UAV cooperative target locating is presented. Using the line‐of‐sight (LOS) targeting property of the airborne visual tracking platform, in combination with the azimuth angle and elevation angle of the measured LOS, a locating error model is established, which can reflect the correlation between the target locating error and LOS angle error. Then an objective function based on minimising the target locating error is built, which is used as an index of two‐UAV trajectory planning. This method can be applied in two‐UAV trajectory planning for cooperative target locating and the simulated results confirm that it works effectively.

Accurate state-of-charge (SOC) estimation is essential for maximizing the lifetime of battery-powered wireless sensor networks (WSNs). Lightweight estimation methods are widely used in WSNs due to their low measurement and computation requirements. However, accuracy of existing lightweight methods is not high, and their adaptability to different batteries and working conditions is relatively poor. This paper proposes a lightweight SOC estimation method, which applies Peukert’s Law to estimate the effective capacity of the battery and then calculates the SOC by subtracting the cumulative current consumption from the estimated capacity. In order to evaluate the proposed method comprehensively, different primary batteries and working conditions (constant current, constant resistance, and emulated duty-cycle loads) are employed. Experimental results show that the proposed method is superior to existing methods for different batteries and working conditions, which mainly benefits from the ability of Peukert’s Law to better model the rate-capacity effect of the batteries.