An Angle Recognition Algorithm for Tracking Moving Targets Using WiFi Signals with Adaptive Spatiotemporal Clustering

An angle estimation algorithm for tracking indoor moving targets with WiFi is proposed. First, phase calibration and static path elimination are proposed and performed on the collected channel state information signals from different antennas. Then, the angle of arrival information is obtained with the joint estimation algorithm of the angle of arrival (AOA) and time of flight (TOF). To deal with the multipath effects, we adopt the DBscan spatiotemporal clustering algorithm with adaptive parameters. In addition, the time-continuous angle of arrival information is obtained by interpolating and supplementing points to extract the dynamic signal paths better. Finally, the least-squares method is used for linear fitting to obtain the final angle information of a moving target. Experiments are conducted with the tracking data set presented with Tsinghua’s Widar 2.0. The results show that the average angle estimation error with the proposed algorithm is smaller than Widar2.0. The average angle error is about 7.18° in the classroom environment, 3.62° in the corridor environment, and 12.16° in the office environment; they are smaller than the errors of the existing system.

GMT-WGAN: An Adversarial Sample Expansion Method for Ground Moving Targets Classification

In the field of target classification, detecting a ground moving target that is easily covered in clutter has been a challenge. In addition, traditional feature extraction techniques and classification methods usually rely on strong subjective factors and prior knowledge, which affect their generalization capacity. Most existing deep-learning-based methods suffer from insufficient feature learning due to the lack of data samples, which makes it difficult for the training process to converge to a steady-state. To overcome these limitations, this paper proposes a Wasserstein generative adversarial network (WGAN) sample enhancement method for ground moving target classification (GMT-WGAN). First, the micro-Doppler characteristics of ground moving targets are analyzed. Next, a WGAN is constructed to generate effective time–frequency images of ground moving targets and thereby enrich the sample database used to train the classification network. Then, image quality evaluation indexes are introduced to evaluate the generated spectrogram samples, with an aim to verify the distribution similarity of generated and real samples. Afterward, by feeding augmented samples to the deep convolutional neural networks with good generalization capacity, the classification performance of the GMT-WGAN is improved. Finally, experiments conducted on different datasets validate the effectiveness and robustness of the proposed method.

Impact Angle Control Guidance to Intercept Moving Targets by Virtual Target Technique

To solve the problem regarding the impact angle of the missile, this paper proposes a novel guidance law, which can control the missile to hit the target at the desired angle. The key of the guidance law is selecting a moving point on the collision line as the virtual target, and the tactical requirements can be fulfilled by the missile directly pursuing the virtual target. The Lyapunov stable theory is used to prove the convergence of the proposed guidance law. The guidance command is generated by a PID controller to make the missile towards the virtual target. The proposed guidance law makes the lateral acceleration of the missile converge to zero, which leads the angle of attack to zero, and it theoretically guarantees the flight path angle equals the attitude angle. Numerical simulations demonstrate this impact angle control guidance law is very accurate and robust. Regardless of whether the initial heading error is large or small, the missile which employs the proposed guidance law can always hit the target from the preset direction and the guidance process is smooth.

Enhanced Pre-STAP Beamforming for Range Ambiguous Clutter Separation with Vertical FDA Radar

Medium pulse repetition frequency (MPRF) is an important mode in airborne radar system. Since MPRF mode brings both Doppler and range ambiguities, it causes difficulty for the airborne radar to suppress ground or sea clutter. In recent years, it has been pointed out that the frequency diverse array (FDA) radar is capable of separating the range ambiguous clutter, which is helpful for the airborne radar in detecting weak moving targets originally buried in ambiguous clutter. To further improve the ambiguous clutter separation performance, an enhanced pre-STAP beamforming for range ambiguous clutter suppression is proposed for the vertical FDA planar array in this paper. With consideration of range dependence of the vertical spatial frequency, a series of pre-STAP beamformers are designed using a priori knowledge of platform and radar parameters. The notches of the beamformers are aligned with the ambiguous clutter to extract echoes from desired range region while suppressing clutter from ambiguous range regions. The notches can be widened by using covariance matrix tapering technique and the proposed method can improve the performance of range ambiguous clutter separation with limited degrees-of-freedom (DOFs). Simulation examples show the effectiveness of the proposed method.

MODERN DREAMS. PLANNING FIREPLACES

An analysis of the current pace of development of hypersonic technologies for the means of launching spacecraft into Earth orbit, as well as for unmanned weapons systems, which embody both impact properties and reconnaissance functions. In order to better understand the strategic importance of technologies based on direct-flow jet engines, fragmentary coverage of the impressive path of achievements of aerospace technology, given some of the highlights of the history of its formation. The presented article analyzes the current problem - increasing the reliability, reliability and accuracy of the definition and classification of moving targets by autonomous means of aircraft on combat duty. The results of the research cover the features of the starting positions and functional action on the combat duty of aircraft of different classes and bases and are able to serve as a reliable scientific basis for improving military equipment on hypersonic technologies

Real-Time Analysis of Athletes’ Physical Condition in Training Based on Video Monitoring Technology of Optical Flow Equation

This article is dedicated to the research of video motion segmentation algorithms based on optical flow equations. First, some mainstream segmentation algorithms are studied, and on this basis, a segmentation algorithm for spectral clustering analysis of athletes’ physical condition in training is proposed. After that, through the analysis of the existing methods, compared with some algorithms that only process a single frame in the video, this article analyzes the continuous multiple frames in the video and extracts the continuous multiple frames of the sampling points through the Lucas-Kanade optical flow method. We densely sampled feature points contain as much motion information as possible in the video and then express this motion information through trajectory description and finally achieve segmentation of moving targets through clustering of motion trajectories. At the same time, the basic concepts of image segmentation and video motion target segmentation are described, and the division standards of different video motion segmentation algorithms and their respective advantages and disadvantages are analyzed. The experiment determines the initial template by comparing the gray-scale variance of the image, uses the characteristic optical flow to estimate the search area of the initial template in the next frame, reduces the matching time, judges the template similarity according to the Hausdorff distance, and uses the adaptive weighted template update method for the templates with large deviations. The simulation results show that the algorithm can achieve long-term stable tracking of moving targets in the mine, and it can also achieve continuous tracking of partially occluded moving targets.