The localization of a sensor in wireless sensor network has now gained considerable attention. To study node localization based on received signal strength (RSS), a least squares optimization algorithm was proposed. Firstly, a linear model was established to calculate path loss. Then a recursive weighted least squares optimization method was presented for sensor node localization.Finally, we compared the algorithm proposed in this paper with the two other algorithms based on RSS. The simulation results showed that our proposed method has a better performance.
Most existing methods are difficult to detect low-altitude and fast-moving drones. A low-altitude unmanned aerial vehicle (UAV) target detection method based on an improved YOLOv3 network is proposed. While keeping the basic framework of the original model unchanged, the YOLOv3 model is improved. That is, multiscale prediction is added to enhance the detection ability of small-target objects. In addition, the two-axis Pan/Tilt/Zoom (PTZ) camera is controlled based on proportional integral derivative (PID), so that the target tends to the center of the field of view. It is more conducive to accurate detection. Finally, experiments are carried out using real UAV datasets. The results show that the mean average precision (mAP), AP50, and AP75 are 25.12%, 39.75%, and 26.03%, respectively, which are better than other methods. Also, the frame rate is 21 frames·s−1, which meets the performance requirements.
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