This paper considers the problem of target and jamming recognition for the pulse Doppler radar fuze(PDRF).To solve the problem,the matched filter outputs of the PDRF under the action of target and jamming are analyzed...This paper considers the problem of target and jamming recognition for the pulse Doppler radar fuze(PDRF).To solve the problem,the matched filter outputs of the PDRF under the action of target and jamming are analyzed.Then,the frequency entropy and peak-to-peak ratio are extracted from the matched filter output of the PDRF,and the time-frequency joint feature is constructed.Based on the time-frequency joint feature,the naive Bayesian classifier(NBC)with minimal risk is established for target and jamming recognition.To improve the adaptability of the proposed method in complex environments,an online update process that adaptively modifies the classifier in the duration of the work of the PDRF is proposed.The experiments show that the PDRF can maintain high recognition accuracy when the signal-to-noise ratio(SNR)decreases and the jamming-to-signal ratio(JSR)increases.Moreover,the applicable analysis shows that he ONBCMR method has low computational complexity and can fully meet the real-time requirements of PDRF.展开更多
针对冲击脉冲超宽带雷达(Impulse Radio Ultra-Wideband Radar,IR-UWBR)在小样本条件及探测场景复杂等挑战下导致目标识别能力不足的问题,提出基于距离-多普勒图与自适应特征选择网络(Range-Doppler Map and Adaptive Feature Selection...针对冲击脉冲超宽带雷达(Impulse Radio Ultra-Wideband Radar,IR-UWBR)在小样本条件及探测场景复杂等挑战下导致目标识别能力不足的问题,提出基于距离-多普勒图与自适应特征选择网络(Range-Doppler Map and Adaptive Feature Selection Network,RDM-AFSN)的运动目标识别方法。在分析IR-UWBR在慢时间维接收回波信号规律的基础上,建立了IR-UWBR多普勒信息提取模型。同时,深入分析运动目标距离-多普勒图由于背景信息复杂、目标种类多导致图像空间特征差异大的特性,构建基于坐标软阈值去噪模块与空间自适应下采样层的RDM-AFSN目标识别模型。实验结果表明,所提模型能够有效提高小样本条件下对运动目标的分类能力,对不同场景下的同类目标均有较好的识别效果,与常用于地面目标识别的卷积-循环深度网络和图像编码深度网络相比,所提出的RDM-AFSN在识别准确率上分别提高了3.64%和7.53%。展开更多
基金supported by the National Natural Science Foundation of China(Grant No.61973037 and No.61673066).
文摘This paper considers the problem of target and jamming recognition for the pulse Doppler radar fuze(PDRF).To solve the problem,the matched filter outputs of the PDRF under the action of target and jamming are analyzed.Then,the frequency entropy and peak-to-peak ratio are extracted from the matched filter output of the PDRF,and the time-frequency joint feature is constructed.Based on the time-frequency joint feature,the naive Bayesian classifier(NBC)with minimal risk is established for target and jamming recognition.To improve the adaptability of the proposed method in complex environments,an online update process that adaptively modifies the classifier in the duration of the work of the PDRF is proposed.The experiments show that the PDRF can maintain high recognition accuracy when the signal-to-noise ratio(SNR)decreases and the jamming-to-signal ratio(JSR)increases.Moreover,the applicable analysis shows that he ONBCMR method has low computational complexity and can fully meet the real-time requirements of PDRF.
文摘针对冲击脉冲超宽带雷达(Impulse Radio Ultra-Wideband Radar,IR-UWBR)在小样本条件及探测场景复杂等挑战下导致目标识别能力不足的问题,提出基于距离-多普勒图与自适应特征选择网络(Range-Doppler Map and Adaptive Feature Selection Network,RDM-AFSN)的运动目标识别方法。在分析IR-UWBR在慢时间维接收回波信号规律的基础上,建立了IR-UWBR多普勒信息提取模型。同时,深入分析运动目标距离-多普勒图由于背景信息复杂、目标种类多导致图像空间特征差异大的特性,构建基于坐标软阈值去噪模块与空间自适应下采样层的RDM-AFSN目标识别模型。实验结果表明,所提模型能够有效提高小样本条件下对运动目标的分类能力,对不同场景下的同类目标均有较好的识别效果,与常用于地面目标识别的卷积-循环深度网络和图像编码深度网络相比,所提出的RDM-AFSN在识别准确率上分别提高了3.64%和7.53%。
