基于二维IFS-CFAR算法的海杂波微弱目标检测

一直以来,统计与分形理论是分别应用在目标检测中的;CFAR检测方法采用自适应门限代替固定门限,能根据被检测点的背景噪声、杂波和干扰的大小自适应地调整;文章首先介绍了CFAR恒虚警目标检测方法,给出2D-IFS二维迭代函数系统)预测误差算法;其次运用2D-IFS方法计算了雷达回波的预测误差,结合CA-CFAR统计学目标检测提出一种新的海杂波微弱目标检测方法;最后采用实测海杂波数据进行验证,仿真结果表明文章所提方法对低目标回波信杂比具有良好的检测能力。     

一种低速运动目标检测和识别的高阶累积量方法

强杂波背景下生命体运动低速目标的检测和识别是信号处理领域的难点.对采样数据的分析表明,生命体低速运动目标的回波信号可简化为正弦谐波过程,而墙体建筑物等固定物体杂波是高斯分布.因高斯分布杂波的高阶累积量为零,本文提出用高阶累积量方法抑制杂波,通过高阶谱分析和FFT积累,给出了目标检测和识别算法.实验结果表明,该方法构建的系统能穿透30 cm砖墙建筑物,探测出距离墙12 m远的人体低速运动目标信号.     

最大熵法在风廓线雷达谱分析中的应用研究

为了解现代谱分析技术在风廓线雷达中应用的可行性,通过采用实测的和模拟的风廓线雷达回波信号,对比研究了FFT法与最大熵法的谱分析效果。结果表明:(1)当回波信号比较强时,两者都可以得到较好的谱分析效果;但是当回波信号较弱时,最大熵法分析效果优于FFT法,最大熵法对地杂波具有较好的抑制能力。(2)最大熵谱比较光滑,表明最大熵法对随机白噪声也有一定的抑制作用。(3)最大熵法的递推阶数对谱分析结果有一定影响,最终预测误差准则确定的递推阶数一般偏小,采用15阶的递推阶数进行最大熵法分析取得了较好结果。由于风廓线雷达回波通常都较弱,因此研究结果可望用于改善信号处理效果。     

互相关自适应加权的医学超声成像算法研究

根据超声成像系统的超声回波信号互相关性,提出互相关自适应加权超声成像算法.该算法根据散射点回波信号之间的空间相关性设置加权系数,对不同位置的散射点进行自适应加权成像,从而降低了成像系统的旁瓣,抑制了相关性较差的噪声.通过Field II仿真的点目标和吸声斑目标处理结果表明该方法成像的横向和纵向分辨率高,成像速度快.相对于延时叠加(DAS)算法,该算法对散射点成像可提高对比度16 dB,对于吸声斑成像可提高对比度0.85 dB.最后采用完备数据集进行实验,结果表明该算法成像分辨率优于DAS算法,对比度提高了17 dB.     

机载极化阵列多输入多输出雷达极化空时自适应处理性能分析

为进一步提升机载多输入多输出(MIMO)雷达空时自适应处理(STAP)的杂波抑制与目标检测性能,本文提出基于极化阵列MIMO雷达的极化空时自适应处理(PSTAP)方法.首先,将新型的极化阵列应用于机载MIMO雷达,建立了机载极化阵列MIMO雷达极化空时自适应处理的信号模型.然后,基于分辨格思想,将杂波影响等效为与杂波自由度相关的独立杂波点源的形式,得到极化阵列MIMO雷达极化空时自适应处理协方差矩阵的等价表示.进而,结合上述等价协方差矩阵,对极化阵列MIMO雷达极化空时自适应处理的输出信杂噪比(SCNR)性能进行了推导分析,讨论了其中极化、空、时匹配系数的影响.理论分析表明,通过利用附加的极化域信息,极化阵列MIMO雷达极化空时自适应处理相比于传统MIMO-STAP能够有效提升杂波抑制性能,更有利于慢速运动目标检测,并且目标与杂波极化参数差别越大,输出SCNR的性能改善效果越明显.仿真结果验证了本文所提极化阵列MIMO雷达极化空时自适应处理方法的有效性与优越性.     

基于投影寻踪的超声彩色血流成像杂波抑制方法

为抑制超声彩色血流成像(CFI)中杂波信号对血流速度估计的影响,提出一种基于投影寻踪(PP)的杂波抑制方法。根据回波信号的时域特性,采用投影寻踪法提取主元以降低主元对非血流区域的高强度杂波的敏感度;然后在特征向量空间去除估计得到的杂波成分,从而获得血流信号。将本方法用于计算机仿真信号和人体实测信号。结果表明:与传统的高通滤波器法相比,本方法能更好地保持流速剖面的完整性和血流轮廓;与特征向量法相比,在近似的流速完整度下本方法在非血流区域产生的误差较少,血管内外能量比高出特征向量法约5dB,能够有效地提高彩色血流成像的质量。      

基于超声RF信号熵分析的声空化时空监测方法

高强度聚焦超声(HIFU)治疗过程中剧烈的空化效应可能损伤靶区周围健康组织,因此,亟需开发可对生物组织内部声空化效应进行高精度时空定量监测的新型技术手段,方能确保临床安全和有效.相对于传统的商用超声灰度值信号,超声射频(RF)信号可以更好地保留声波散射信号更多的细节信息.而信息熵作为非基于数学函数模型的统计参数,可以表征由声空化效应引发的组织内部散射体无序度演变状态.因此,本文提出了一种基于超声RF信号熵分析的声空化实时监测成像系统,在此基础上实时评估HIFU引发的超声空化区域时空演化行为.首先,通过改制后的B超系统获取凝胶生物仿体内部由HIFU引发的空化泡群产生的散射回波原始RF信号,利用二维均值滤波方法抑制HIFU强声束对声空化监测成像回波信号的干扰后,通过数据标准化处理扩展RF信号的动态变化范围,再基于滑动窗信息熵分析重建熵值图像,经过二值化处理后即可实现对HIFU作用下组织内部声空化区域的时空监测.实验结果表明,相比于传统B超灰度成像法,基于RF信号熵分析的声空化监测成像算法可以更灵敏且精确地确定空化发生的起始时间和空间位置,有助于更好地保障HIFU临床治疗的安全性和有效性.本...     

逆合成孔径成像激光雷达数据采样技术

逆合成孔径成像激光雷达能够实现对运动目标的高分辨实时成像,但激光信号的极大带宽和目标回波信号的微弱性给雷达回波数据的接收和处理带来了较大困难.针对这一问题,提出了基于光外差探测手段和压缩感知理论相结合的信号采样方法,首先通过光外差探测降低回波信号的有效带宽,再结合压缩感知理论实现对信号的稀疏化采样和重构.仿真结果证明了运用本文所提出的采样方法,在使用远低于奈奎斯特定理所规定的采样率时,仍然能够实现对目标的高质量成像.     

逆合成孔径成像激光雷达数据采样技术

逆合成孔径成像激光雷达能够实现对运动目标的高分辨实时成像,但激光信号的极大带宽和目标回波信号的微弱性给雷达回波数据的接收和处理带来了较大困难.针对这一问题,提出了基于光外差探测手段和压缩感知理论相结合的信号采样方法,首先通过光外差探测降低回波信号的有效带宽,再结合压缩感知理论实现对信号的稀疏化采样和重构.仿真结果证明了运用本文所提出的采样方法,在使用远低于奈奎斯特定理所规定的采样率时,仍然能够实现对目标的高质量成像.     

采用混响杂波比估计的有源声呐波形杂波抑制性能评价方法

浅海混响中由礁石等强散射体引起的杂波会导致有源声呐虚警、检测性能降低,合理的波形设计可有效抑制此类杂波。虽然使用多普勒敏感波形或减小波形多普勒容限已被证明能够有效抑制杂波,然而只有对当前波形的杂波抑制性能进行量化评价,才能判断当前波形的杂波抑制效果优劣,进而帮助决策是否需调整发射波形,而目前尚缺乏波形杂波抑制性能评价方法的提出。首先通过建模阐述了波形抑制杂波的原理,然后针对波形抑制杂波的性能评价问题,提出了采用混合分布统计模型对混响进行拟合及杂波比估计的方法,最后,通过湖试实验验证了该方法能够有效评价波形的杂波抑制性能。提出的方法利用混响统计特征的参数估计来反映和评价当前波形的性能,对波形体制选择和参数设计具有指导意义。      

Estimation of sea clutter inherent Doppler spectrum from shipborne S-band radar sea echo

Measurement of shipborne radar sea echo is instrumental in collecting the sea clutter data in open sea areas. However,the ship movement would introduce an extra Doppler component into the spectrum of the sea clutter, so the sea clutter inherent spectrum must be estimated prior to investigating the sea clutter Doppler characteristics from the shipborne radar sea echo. In this paper we show some results about a shipborne sea clutter measurement experiment that was conducted in the South China Sea in a period between 2017 and 2018; abundant clutter data have been collected by using a shipborne S-band clutter measurement radar. To obtain the sea clutter inherent Doppler spectrum from these data, an estimation method, based on the mapping relationship between the shipborne clutter spectrum and the inherent clutter spectrum, is proposed. This method is validated by shipborne clutter data sets under the same measuring conditions except for the ship speed. Using this method, the characteristics of the Doppler spectrum lineshapes in the South China Sea are calculated and analyzed according to different sea states, wave directions, and radar resolutions, which can be instrumental in designing the radar target detection algorithms.     

Suppression of Sea-Ice Clutter Observed by a Millimeter Wave Radar Using a New Log-Weibull/CFAR System

Sea-ice clutter was measured using a millimeter wave radar with frequency of 34.860GHz, beamwidth of 0.25° and pulse length of 30ns. To determine the sea-ice clutter amplitude statistics, we investigated the log-normal, Weibull, log-Weibull and K-distributions using the Akaike Information Criterion (AIC), which is more rigorous fit of the distribution to the data than the least-squares method. It is shown that the amplitude of sea-ice clutter obeys the log-Weibull distribution with shape parameters of 2.36 to 2.93 for both entire data and data within the beam width of an antenna. We propose a new log-Weibull/CFAR system uses a modified cell-averaging LOG/CFAR system. It is found that this log-Weibull/CFAR procedure is effective for suppression of sea-ice clutter and detection of target.     

Sea Clutter Suppression and Target Detection Algorithm of Marine Radar Image Sequence Based on Spatio-Temporal Domain Joint Filtering

In marine radar target detection, sea clutter will cause a large number of missed alarms and false alarms, which will affect the accuracy of target detection. In order to suppress sea clutter effectively, a sea clutter suppression and target detection algorithm of marine radar image sequence based on spatio-temporal domain joint filtering is proposed in this paper. The proposed method is to add a sea clutter suppression link before detecting the target. Firstly, the marine radar image sequence is transformed into three-dimensional frequency wavenumber domain by three-dimensional fast Fourier transform (3D-FFT), and then the three-dimensional image spectrum is obtained. According to the fact that the sea clutter spectrum obtained from the image spectrum satisfies the dispersion relation of linear wave theory in the three-dimensional frequency wavenumber domain, a sea clutter model is established. Then, through the established sea clutter model, a spatio-temporal domain joint sea clutter suppressor is designed to filter the image spectrum. After that, the filtered image spectrum is transformed by three-dimensional inverse fast Fourier transform (3D-IFFT) to obtain the image sequence in which sea clutter is suppressed. Finally, target detection is carried out for sea clutter suppressed image sequence. The method is validated by using the real data of X-band marine radar. Compared with the classical Empirical mode decomposition (EMD) method, the improvement of signal-to-noise ratio (SNR) is more obvious, and SNR can be increased by 15.3 db at most. In addition, compared with target detection on original images directly, the proposed method has excellent detection rate and can increase detection rates by at least 8%.     

利用正弦调频信号的宽带速度敏感特性抑制混响

有源声呐探测水中低速目标时,混响与目标回波的频率接近,目标回波与混响分离困难.根据运动目标与产生混响的散射体在速度域的差异,利用正弦调频信号(SFM)的速度敏感特性实现目标回波与混响的有效分离.推导了 SFM信号宽带混响抑制能力与速度分辨模糊之间的约束关系,建立了波形优化设计的搜索模型.通过多普勒滤波处理将数据从频率域...     

An adaptive clutter rejection method based on AR model in color flow imaging

The ultrasound Doppler signal scattered from blood is heavily corrupted by the clutter signal reflected from slowly moving muscular tissue. The Doppler frequency shift of blood flow and clutter in different parts of human body greatly changes. Therefore, if a fixed wall filter is selected, the optimal filtering effect can not be attained. An adaptive clutter rejection method is proposed in this paper, which consists of a weak clutter rejector and a 2-order AR estimator. The clutter and blood power thresholds were preliminary defined in the weak clutter rejector. The echo power is compared with the two pre-defined thresholds, and the result was used to select an appropriate wall filter. The output of the weak clutter rejector is estimated by a 2-order AR estimator and two poles are acquired. The low frequency pole denotes the clutter signal and the high frequency pole denotes the blood signal. Before the AR estimation, a static signal is added to avoid producing split spectral peaks. It is illustrated in the simulation that the proposed method can detect the slower blood flow with smaller variance compared with the traditional wall filtering method.     

复杂场景下基于压缩感知的目标电磁散射与成像

研究了处于复杂场景下目标的逆合成孔径雷达(ISAR)成像问题。首先,建立了目标与复杂环境的电磁散射模型,采用计算电磁学的方法仿真得到了目标的雷达回波数据,进而充分考虑了背景噪声对雷达成像质量的影响。研究发现,目标所处的复杂背景会降低ISAR对目标的成像质量。其次,为减小仿真雷达回波数据所需的计算量,提出采用基于压缩感知(CS)的方法来对该场景进行成像,从而极大降低电磁仿真的计算点数。通过实验发现,在CS成像中,采用数据点使用率为0.4时所得到的成像质量可达到采用转台成像质量的效果。因此,采用基于CS的成像方法,可极大降低目标与场景的电磁散射计算复杂度,使得处于真实复杂场景下的目标电磁仿真和ISAR成像研究切实可行。     

红外搜索系统中弱小目标检测算法研究

 复杂背景下低信噪比弱小目标的检测是红外搜索系统中的重点和难点,为解决红外搜索系统中杂波干扰多、目标信噪比低等问题,提出一种模板匹配滤波的目标检测方法。该算法在预测背景的同时,通过对图像背景灰度值进行动态的阈值处理,自适应地进行背景抑制。当背景包含较多复杂因素时,采用模板匹配滤波的目标检测方法,消除背景抑制后的残留杂波,实现弱小目标的提取。试验结果表明：当场景较复杂且图像信噪比较低时,使用该算法处理后可使图像信噪比达到4 dB以上,从而提高了弱小目标的检测概率。     

A novel clutter rejection scheme in color flow imaging

In ultrasound color flow imaging systems, it is important to suppress the clutter signals originated from stationary and slowly moving tissue sufficiently. Without sufficient clutter rejection, the estimation of the blood flow velocity will be inaccurate, and the imaging quality will be poor. In this paper we present a new clutter rejection scheme involving first down-mixing the clutter signals to zero frequency and then cancelling the stationary echo by subtracting the signals from two consecutive pulses. This stationary echo cancelling scheme is easy to be realized with small computational power, and is adaptive to the clutter signals. Theoretical analysis and simulation using RF data prove that it's an efficient and practical clutter rejection scheme for color flow imaging.     

混沌海杂波背景下的微弱信号检测混合算法

基于经验模态分解理论, 提出了一种基于粒子群算法的支持向量机预测方法. 采用总体平均经验模式分解法将混沌信号分解为若干固有模态函数和趋势分量, 将复杂的非线性信号转化为具有不同尺度特征的平稳分量. 利用粒子群算法对支持向量机的惩罚系数和核函数进行优化, 结合支持向量机建立混沌序列的单步预测模型. 从预测误差中检测淹没在混沌背景中的微弱信号(包括瞬态信号和周期信号). 对Lorenz系统和实测IPIX雷达数据进行仿真实验, 结果表明, 该方法能够有效地从混沌背景噪声中检测出微弱目标信号, Lorenz系统得到的均方根误差0.000000339 (-102.8225 dB时)比传统支持向量机方法的均方根误差0.049 (-54.60 dB时)降低了5个数量级, 从海杂波中检测出具有谐波特性的微弱信号, 表明预测模型具有更低的门限和误差.