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作 者:孙大军 卢明洋[1,2,3] 梅继丹 王少伯 焦宏浩[1,2,3] SUN Dajun;LU Mingyang;MEI Jidan;WANG Shaobo;JIAO Honghao(National Key Laboratory of Underwater Acoustic Technology,Harbin Engineering University,Harbin 150001,China;Key Laboratory of Marine Information Acquisition and Security(Harbin Engineering University),Ministry of Industry and Information Technology,Harbin 150001,China;College of Underwater Acoustic Engineering,Harbin Engineering University,Harbin 150001,China)
机构地区:[1]哈尔滨工程大学水声技术全国重点实验室,黑龙江哈尔滨150001 [2]海洋信息获取与安全工业和信息化部重点实验室(哈尔滨工程大学),黑龙江哈尔滨150001 [3]哈尔滨工程大学水声工程学院,黑龙江哈尔滨150001
出 处:《哈尔滨工程大学学报》2024年第11期2133-2142,共10页Journal of Harbin Engineering University
基 金:国家自然科学基金项目(61871144).
摘 要:针对传统目标运动分析算法受静止单平台可观测性限制的问题,本文提出了一种基于广义Radon变换的水下运动平台目标参数估计方法。在观测平台自身的运动参数已知的假设下,推导了目标方位与各运动参数的数学关系,在此基础上通过对平台声呐测得的目标时间方位历程进行广义Radon变换,依次得到目标的航向角、速度以及目标相对于探测平台的初距,最终实现目标运动轨迹的解算。利用矢量阵的仿真数据对方法的可行性进行了分析验证,结果表明:作为一种批处理类方法,该方法无需进行迭代运算,易于实现,在较低信噪比条件下仍具有很好的估计精度和鲁棒性。To overcome the constraints of traditional target motion analysis algorithms,which are limited by the observability of a stationary single platform,this paper proposes a target parameter estimation method based on the generalized Radon transform using an underwater mobile detection platform.First,the method derives the mathematical relationships between the target’s azimuth and various motion parameters,assuming that the platform’s motion parameters are known.Based on this,the generalized Radon transform is applied to the bearing-time records of the target as measured by the platform sonar.This process yields the target’s heading angle,velocity,and initial range relative to the target relative to the detection platform in turn.From these parameters,the target’s motion trajectory with respect to the detection equipment can be calculated.Simulated data based on a vector array verify the feasibility of the proposed method.The results suggest that this batch processing method does not require iterative calculations,making it easy to implement.Moreover,it offers high estimation accuracy and robustness even under low signal-to-noise ratio(SNR)conditions.
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