The mathematical model and fusion algorithm for multisensor data fusion are presented, and applied to integrate the decisions obtained by multiple sonars in a distributed detection system. Assuming that all the sonar...The mathematical model and fusion algorithm for multisensor data fusion are presented, and applied to integrate the decisions obtained by multiple sonars in a distributed detection system. Assuming that all the sonars and the fusion system operate at the same false alarm probability, the expression for the detection probability of the fusion system is obtained. Computer simulations reveals that the detection probability and detection range of the fusion system are significantly improved compared to the original distributed detection system.展开更多
现有的检测方法相对误差较大,为此,本文研究了基于现场可编程门阵列(FPGA)的微弱声呐信号自动检测方法。首先,自动提取微弱声呐信号特征,运用该方法放大输入信号,以提升对微弱信号的检测准确性;其次,运用滤波器带宽有效消除噪声,快速捕...现有的检测方法相对误差较大,为此,本文研究了基于现场可编程门阵列(FPGA)的微弱声呐信号自动检测方法。首先,自动提取微弱声呐信号特征,运用该方法放大输入信号,以提升对微弱信号的检测准确性;其次,运用滤波器带宽有效消除噪声,快速捕捉关键信号并进行时序分析,通过采集完整信号实现快速检测;最后,当声呐接近目标的滤波输出时,在浅海环境下运用该方法减小检测误差。结果表明,当衰减值为25 d B时,实验组的相对误差为2%,能够准确检测微弱信号,减小检测的误差,实现检测方法的较好应用。展开更多
混响是主动声纳检测的主要背景干扰,由于它是一种非平稳的有色噪声,使得工作在白噪声条件下的检测器性能受到极大限制。在混响背景下实现目标回波检测,常采用自回归(AR)模型对宽带回波预白化处理,但在强混响背景条件下,白化后直接进行...混响是主动声纳检测的主要背景干扰,由于它是一种非平稳的有色噪声,使得工作在白噪声条件下的检测器性能受到极大限制。在混响背景下实现目标回波检测,常采用自回归(AR)模型对宽带回波预白化处理,但在强混响背景条件下,白化后直接进行匹配滤波检测的结果不甚理想。针对此问题,在AR模型预白化基础上,提出一种改进方法,对白化后信号先进行二分奇异值分解(SVD)处理,有效去除大部分混响干扰,然后再作匹配检测。仿真实验分析表明,相比于仅白化后的匹配滤波检测,该方法可提高信混比约3 d B,匹配检测效果得到了明显改善。展开更多
基金National Doctorate Discipline FoundationNational Defense Key Laboratory Foundation of China.
文摘The mathematical model and fusion algorithm for multisensor data fusion are presented, and applied to integrate the decisions obtained by multiple sonars in a distributed detection system. Assuming that all the sonars and the fusion system operate at the same false alarm probability, the expression for the detection probability of the fusion system is obtained. Computer simulations reveals that the detection probability and detection range of the fusion system are significantly improved compared to the original distributed detection system.
文摘现有的检测方法相对误差较大,为此,本文研究了基于现场可编程门阵列(FPGA)的微弱声呐信号自动检测方法。首先,自动提取微弱声呐信号特征,运用该方法放大输入信号,以提升对微弱信号的检测准确性;其次,运用滤波器带宽有效消除噪声,快速捕捉关键信号并进行时序分析,通过采集完整信号实现快速检测;最后,当声呐接近目标的滤波输出时,在浅海环境下运用该方法减小检测误差。结果表明,当衰减值为25 d B时,实验组的相对误差为2%,能够准确检测微弱信号,减小检测的误差,实现检测方法的较好应用。
文摘混响是主动声纳检测的主要背景干扰,由于它是一种非平稳的有色噪声,使得工作在白噪声条件下的检测器性能受到极大限制。在混响背景下实现目标回波检测,常采用自回归(AR)模型对宽带回波预白化处理,但在强混响背景条件下,白化后直接进行匹配滤波检测的结果不甚理想。针对此问题,在AR模型预白化基础上,提出一种改进方法,对白化后信号先进行二分奇异值分解(SVD)处理,有效去除大部分混响干扰,然后再作匹配检测。仿真实验分析表明,相比于仅白化后的匹配滤波检测,该方法可提高信混比约3 d B,匹配检测效果得到了明显改善。