CAJ | 학술논문

为进行强目标相关旁瓣干扰下的相邻弱目标检测,采用稀疏重建理论进行多目标方位估计.高信噪比情况下,由方位估计结果即可完成目标检测;对于低信噪比弱目标回波,为提高系统检测能力,结合方位估计结果,提出了两种检测算法,前者类似于传统CLEAN算法,从能量角度进行目标检测;后者则利用相关函数能量集中于主瓣的特点,通过计算将目标方位估计结果中非零元素置零前后匹配滤波峰值的差值,采用Page-test序贯检测器进行多目标检测.仿真和试验数据处理结果表明,相同检测概率下,第二种方法具有更加优良的弱目标检测性能.
위진행강목표상관방판간우하적상린약목표검측,채용희소중건이론진행다목표방위고계.고신조비정황하,유방위고계결과즉가완성목표검측;대우저신조비약목표회파,위제고계통검측능력,결합방위고계결과,제출료량충검측산법,전자유사우전통CLEAN산법,종능량각도진행목표검측;후자칙이용상관함수능량집중우주판적특점,통과계산장목표방위고계결과중비령원소치령전후필배려파봉치적차치,채용Page-test서관검측기진행다목표검측.방진화시험수거처리결과표명,상동검측개솔하,제이충방법구유경가우량적약목표검측성능.
High sidelobe peaks of auto-correlation function in the matching filter output incur interference to the detection of other adjacent targets. To achieve adjacent targets detection, sparse reconstruction theory was employed to obtain targets' position estimation. In the scenario with high SNR (signal to noise ratio), estimated results can be used directly for targets' detection; otherwise, i.e. in the scenario with low SNR, two detection algorithms are proposed. The first one, similar to CLEAN algorithm, utilizes the energy changes of matching filter output to determine the presence of targets, while the second one, considering the feature of auto-correlation function, combines main lobe change calculation and Page-test sequential detector to achieve multi-targets detection. Simulation and experimental data processing results demonstrate that the second algorithm achieved better weak targets' detection performance.