A multi-shells prediction target reporting algorithm based on aerial three-dimensional trajectory estimation

Juan Yue, Jie Liu, Sili Gao
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Firstly, because the ground in the image cannot provide enough geometric prior knowledge, the detection deviation of the shell explosion point in the image is large, resulting in the target reporting result based on the double station positioning of the shell explosion point cannot meet the 1m accuracy requirement. Secondly, due to the interference of the mushroom cloud at the pre-sequence shell blast point, the explosion point of the group shell is difficult to be effectively detected, so the method does not support the continuous target reporting of multiple bullets. Based on this, this paper proposes a range prediction target reporting algorithm, which is based on the detection of the shell's aerial trajectory for double-station positioning, first locates the three-dimensional track points of the shell target when it moves in the air, then fits out the three-dimensional trajectory of the shell in the air, and finally predicts the three-dimensional position of the shell explosion point based on the bulls eye GPS information, and outputs the target reporting results. The algorithm avoids the detection of image explosion points, which can improve the positioning accuracy of explosion points, and can avoid the interference of mushroom clouds at the explosion points of pre-sequence shells and realize the automatic target reporting of continuous shells. In addition, in view of the problem of matching between group shell stations, this paper adopts the method of multi-target matching target based on track direction estimation. Firstly, it performs single-frame multi-target preliminary matching based on the elevation difference of dual-station direction finding rays and obtains all three-dimensional track points in the air of each shell target. Then, based on the two-dimensional histogram of the direction or the Mean Shift algorithm, the three-dimensional track direction of each shell target is estimated, and the true and false track points are checked based on the three-dimensional track direction of the target, and the false matching points are eliminated. Finally, the three-dimensional trajectory fitting and the position prediction of the explosion point of the shell target are carried out, and the target reporting results are output. Experiments verify that the positioning error of a single shell in this algorithm is 0.57m, and the positioning error of the traditional target reporting algorithm is 1.17m, indicating that even when the shape of the explosion point is regular, the positioning error of the predicted target can be doubled; For the one-shot two-bomb test that can detect the target, the positioning error of the algorithm in this paper is better than 1m, which meets the overall application requirements. In view of the target training of the Yellow Flag Sea test of ten bombs and fourteen bombs in a row, the algorithm fits the multi-target three-dimensional track parallel to each other and distributes it uniformly, which can intuitively and qualitatively judge the correctness of the matching target. In addition, from a quantitative point of view, the test found that the algorithm can not only eliminate the mismatch point, but also suppress the point with large error, thereby reducing the multi-track fit error and improving the multi-target reporting accuracy: the maximum fit error of the track < 0.5m, and the average fit error <0.3m. In summary, this paper proposes a multi-target prediction target reporting algorithm based on three-dimensional trajectory direction estimation, which avoids the detection of image shell explosion points, based on the image shell aerial track detection, conducts three-dimensional trajectory estimation of each shell target, predicts the three-dimensional position of each shell target explosion point, outputs high-precision target reporting information of each shell target, the target accuracy is better than 1m@1km, and supports a multi-bomb continuous target reporting, which can provide scientific and effective data support for training evaluation.","PeriodicalId":258680,"journal":{"name":"Earth and Space From Infrared to Terahertz (ESIT 2022)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Earth and Space From Infrared to Terahertz (ESIT 2022)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.2664646","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Through the passive observation and algorithm processing of the two-station infrared camera, the three-dimensional coordinates of the landing point of the shell are located, and the target is automatically reported throughout the day for the range training, which is an important way for training evaluation. The traditional photoelectric imaging target reporting method directly locates the double station based on the detection of the explosion point of the image shell, locates the three-dimensional position of the shell explosion point, and outputs the target reporting results. This method faces two difficulties. Firstly, because the ground in the image cannot provide enough geometric prior knowledge, the detection deviation of the shell explosion point in the image is large, resulting in the target reporting result based on the double station positioning of the shell explosion point cannot meet the 1m accuracy requirement. Secondly, due to the interference of the mushroom cloud at the pre-sequence shell blast point, the explosion point of the group shell is difficult to be effectively detected, so the method does not support the continuous target reporting of multiple bullets. Based on this, this paper proposes a range prediction target reporting algorithm, which is based on the detection of the shell's aerial trajectory for double-station positioning, first locates the three-dimensional track points of the shell target when it moves in the air, then fits out the three-dimensional trajectory of the shell in the air, and finally predicts the three-dimensional position of the shell explosion point based on the bulls eye GPS information, and outputs the target reporting results. The algorithm avoids the detection of image explosion points, which can improve the positioning accuracy of explosion points, and can avoid the interference of mushroom clouds at the explosion points of pre-sequence shells and realize the automatic target reporting of continuous shells. In addition, in view of the problem of matching between group shell stations, this paper adopts the method of multi-target matching target based on track direction estimation. Firstly, it performs single-frame multi-target preliminary matching based on the elevation difference of dual-station direction finding rays and obtains all three-dimensional track points in the air of each shell target. Then, based on the two-dimensional histogram of the direction or the Mean Shift algorithm, the three-dimensional track direction of each shell target is estimated, and the true and false track points are checked based on the three-dimensional track direction of the target, and the false matching points are eliminated. Finally, the three-dimensional trajectory fitting and the position prediction of the explosion point of the shell target are carried out, and the target reporting results are output. Experiments verify that the positioning error of a single shell in this algorithm is 0.57m, and the positioning error of the traditional target reporting algorithm is 1.17m, indicating that even when the shape of the explosion point is regular, the positioning error of the predicted target can be doubled; For the one-shot two-bomb test that can detect the target, the positioning error of the algorithm in this paper is better than 1m, which meets the overall application requirements. In view of the target training of the Yellow Flag Sea test of ten bombs and fourteen bombs in a row, the algorithm fits the multi-target three-dimensional track parallel to each other and distributes it uniformly, which can intuitively and qualitatively judge the correctness of the matching target. In addition, from a quantitative point of view, the test found that the algorithm can not only eliminate the mismatch point, but also suppress the point with large error, thereby reducing the multi-track fit error and improving the multi-target reporting accuracy: the maximum fit error of the track < 0.5m, and the average fit error <0.3m. In summary, this paper proposes a multi-target prediction target reporting algorithm based on three-dimensional trajectory direction estimation, which avoids the detection of image shell explosion points, based on the image shell aerial track detection, conducts three-dimensional trajectory estimation of each shell target, predicts the three-dimensional position of each shell target explosion point, outputs high-precision target reporting information of each shell target, the target accuracy is better than 1m@1km, and supports a multi-bomb continuous target reporting, which can provide scientific and effective data support for training evaluation.
一种基于空中三维弹道估计的多弹预测目标报告算法
通过双站红外摄像机的被动观测和算法处理,定位炮弹着陆点的三维坐标,全天自动上报目标进行靶场训练,是训练评估的重要途径。传统的光电成像目标报告方法是基于对图像炮弹爆炸点的检测,直接定位双站,定位炮弹爆炸点的三维位置,输出目标报告结果。这种方法面临两个困难。首先,由于图像中的地面无法提供足够的几何先验知识,导致图像中炮弹爆炸点的检测偏差较大,导致基于双站定位炮弹爆炸点的目标报告结果无法满足1m精度要求。其次,由于序前弹爆点处蘑菇云的干扰,难以有效探测到群弹的爆点,因此该方法不支持多发子弹的连续目标报告。在此基础上,本文提出了一种基于对炮弹空中轨迹检测进行双站定位的距离预测目标报告算法,首先定位炮弹目标在空中运动时的三维轨迹点,然后拟合炮弹在空中的三维轨迹,最后根据牛眼GPS信息预测炮弹爆炸点的三维位置。并输出目标报告结果。该算法避免了图像爆炸点的检测,提高了爆炸点的定位精度,避免了前序弹爆炸点蘑菇云的干扰,实现了连续弹的目标自动上报。此外,针对群弹站之间的匹配问题,本文采用了基于航迹方向估计的多目标匹配目标方法。首先,基于双站测向射线仰角差进行单帧多目标初步匹配,得到每个炮弹目标在空中的全部三维航迹点;然后,基于方向的二维直方图或Mean Shift算法估计每个炮弹目标的三维航迹方向,并根据目标的三维航迹方向检查真假航迹点,剔除假匹配点。最后对炮弹目标进行三维弹道拟合和爆炸点位置预测,并输出目标报告结果。实验验证,该算法中单个炮弹的定位误差为0.57m,传统目标报告算法的定位误差为1.17m,表明即使在爆炸点形状规则的情况下,预测目标的定位误差也可以翻倍;对于能探测到目标的一次双弹试验,本文算法的定位误差优于1m,满足总体应用要求。针对黄旗海试验10弹和14弹连续的目标训练,该算法拟合了彼此平行的多目标三维航迹并均匀分布,可以直观、定性地判断匹配目标的正确性。此外,从定量的角度测试发现,该算法不仅可以消除失配点,还可以抑制误差较大的点,从而降低多航迹拟合误差,提高多目标报告精度:航迹最大拟合误差< 0.5m,平均拟合误差<0.3m。综上所述,本文提出了一种基于三维弹道方向估计的多目标预测目标报告算法,避免了图像炮弹爆炸点的检测,在图像炮弹航迹检测的基础上,对每个炮弹目标进行三维弹道估计,预测每个炮弹目标爆炸点的三维位置,输出每个炮弹目标高精度目标报告信息;目标精度优于1m@1km,支持多弹连续目标报告,可为训练评估提供科学有效的数据支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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