Analysis of the Stability of the Body in a Standing Position When Shooting at a Stationary Target―A Randomized Controlled Trial

Postural stability of the body depends on many factors. One of them is physical activity. It is especially important in the case of sports or professional work, which combine mobility with the accuracy of a shot in a standing position. The smaller the body fatigue, the more accurate the shot. The aim of the study was the assessment of the impact of physical effort on the center of gravity deflection and length of the COP (center of pressure) path, as well as the reaction of ground forces in people who do not engage in systematic physical activity. The study group included 139 people (23.1 ± 5.2 yr; M: 46.8%; F: 53.2%). The test consisted of performing a static test twice, shooting at the target in a multimedia shooting range. Group X performed the Harvard test between the static tests. Group Y made no effort. The reaction parameters of the ground forces were assessed using the Zebris PDM-L Platform. In Group X performing the Harvard test, an increase in the average COP, VCOP, and 95% confidence ellipse area was noted. The path length and the average velocity of COP speed increased. There were no differences in Group Y (p > 0.05). Physical effort significantly affected the postural stability of the studied people, increasing the average parameters assessing balance when adopting static firing position.

A Convolutional Neural Network Combined with Attributed Scattering Centers for SAR ATR

It is very common to apply convolutional neural networks (CNNs) to synthetic aperture radar (SAR) automatic target recognition (ATR). However, most of the SAR ATR methods using CNN mainly use the image features of SAR images and make little use of the unique electromagnetic scattering characteristics of SAR images. For SAR images, attributed scattering centers (ASCs) reflect the electromagnetic scattering characteristics and the local structures of the target, which are useful for SAR ATR. Therefore, we propose a network to comprehensively use the image features and the features related to ASCs for improving the performance of SAR ATR. There are two branches in the proposed network, one extracts the more discriminative image features from the input SAR image; the other extracts physically meaningful features from the ASC schematic map that reflects the local structure of the target corresponding to each ASC. Finally, the high-level features obtained by the two branches are fused to recognize the target. The experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset prove the capability of the SAR ATR method proposed in this letter.

Impact angle guidance law to prevent the detection degradation of a seeker

An impact angle control guidance (IACG) law applicable to a homing missile equipped with a strapdown imaging seeker is investigated against a stationary target. Given an impact angle constraint, usually a detour is generated and the change of the look angle is inevitable. A rapid change of the look angle can cause a fast relative target motion in the seeker’s image plane, which can lead to a motion blur effect. The main contribution of the paper is that an IACG law is designed to minimize the look angle rate to prevent the loss of the target signal. Based on the variational approach, the optimal look angle rate that satisfies the impact angle constraint is derived, and the guidance law is designed to follow the optimal look angle rate. Using various weighting functions, a guidance law that has low sensitivity to the initial condition is also developed. Numerical simulation supports the performance of the guidance law. The result illustrates that the proposed guidance law reduces the rate of look angle in comparison with other IACG laws.

Simulation of Moving Target Indication Radar System Based on VisSim/Comm Application

Moving target indication (MTI) is mainly designed to detect moving targets and while unmoving targets are filtered out. It focuses on the technique of the modern stationary target indication (STI), by using directly the signal details to determine the reflecting object’s mechanical properties, after that it becomes easier to find moving or non-moving targets. This paper presents the simulation design of the MTI radar system. The main purpose of this design is to help students in understanding the radar system subject and help teachers to explain this subject in a simpler approach. Both students and teachers need to know how the signals inside the MTI radar processor are working and how they are generated and related to each other. This paper introduces a method of simulation of MTI radar signals including, A-scope radar display, transmitted and returned radar pulses with constant and multiple PRF, n-delay line cancellers.

Combination of ELM and BEMD for Target Recognition of SAR Images

For the problem of target recognition of synthetic aperture radar (SAR) images, a method based on the combination of bidimensional empirical mode decomposition (BEMD) and extreme learning machine (ELM) is proposed. BEMD performs feature extraction for SAR images, producing multi-layer bidimensional intrinsic mode functions (BIMF). These BIMFs covey the discrimination of the original target while effectively eliminating the noises. ELM conducts the classification of each BIMF with high efficiency and robustness. Finally, the decisions from different BIMFs are fused using a linear weighting strategy to reach a reliable decision on the target label. The proposed method compensates the relatively low adaptivity of ELM to noise corruption by BEMD feature extraction. Moreover, the multi-layer BIMFs provide more discriminative information for correct decision. Hence, the overall recognition performance can be improved. As an efficient recognition algorithm, the proposed method can be used in an embedded system for wide applications. Experiments are designed and implemented on the moving and stationary target acquisition and recognition (MSTAR) dataset. The proposed method is tested under both the standard operating condition (SOC) and extended operating conditions (EOCs). The results reflect its effectiveness and robustness via quantitative comparisons.

Region Matching of SAR Images Using Blocks for Target Recognition

A synthetic aperture radar (SAR) target recognition method based on image blocking and matching is proposed. The test SAR image is first separated into four blocks, which are analyzed and matched separately. For each block, the monogenic signal is employed to describe its time-frequency distribution and local details with a feature vector. The sparse representation-based classification (SRC) is used to classify the four monogenic feature vectors and produce the reconstruction error vectors. Afterwards, a random weight matrix with a rich set of weight vectors is used to linearly fuse the feature vectors and all the results are analyzed in a statistical way. Finally, a decision value is designed based on the statistical analysis to determine the target label. The proposed method is tested on the moving and stationary target acquisition and recognition (MSTAR) dataset and the results confirm the validity of the proposed method.

An Integrated Counterfactual Sample Generation and Filtering Approach for SAR Automatic Target Recognition with a Small Sample Set

Although automatic target recognition (ATR) models based on data-driven algorithms have achieved excellent performance in recent years, the synthetic aperture radar (SAR) ATR model often suffered from performance degradation when it encountered a small sample set. In this paper, an integrated counterfactual sample generation and filtering approach is proposed to alleviate the negative influence of a small sample set. The proposed method consists of a generation component and a filtering component. First, the proposed generation component utilizes the overfitting characteristics of generative adversarial networks (GANs), which ensures the generation of counterfactual target samples. Second, the proposed filtering component is built by learning different recognition functions. In the proposed filtering component, multiple SVMs trained by different SAR target sample sets provide pseudo-labels to the other SVMs to improve the recognition rate. Then, the proposed approach improves the performance of the recognition model dynamically while it continuously generates counterfactual target samples. At the same time, counterfactual target samples that are beneficial to the ATR model are also filtered. Moreover, ablation experiments demonstrate the effectiveness of the various components of the proposed method. Experimental results based on the Moving and Stationary Target Acquisition and Recognition (MSTAR) and OpenSARship dataset also show the advantages of the proposed approach. Even though the size of the constructed training set was 14.5% of the original training set, the recognition performance of the ATR model reached 91.27% with the proposed approach.

Target Recognition of Synthetic Aperture Radar Images by Updated Classifiers

For the problem of reliable decision in synthetic aperture radar (SAR) target recognition, a method based on updated classifiers is proposed. The convolutional neural network (CNN) and support vector machine (SVM) are used as basic classifiers to classify samples with unknown target labels. The two decisions are fused and the reliability of the fused decision is evaluated. The classified test samples with high reliabilities are added to the original training samples to update the classifiers. The updated classifiers have stronger classification abilities and the fused result of the two classifiers can obtain a more reliable decision. The proposed method is tested and verified based on the moving and stationary target acquisition and recognition (MSTAR) dataset. The experimental results verify the effectiveness and robustness of the proposed method.