Statistical analysis of split spectrum processing for multiple target detection

This work provides a statistical analysis of the performance of split spectrum processing (SSP) for the detection of multiple targets using data consisting of simulated flaw signals added to experimentally obtained backscattered grain noise. The investigation is performed under two conditions: known a priori target spectral characteristics (i.e., center frequency and bandwidth) which, in turn, identifies the optimal spectral range for processing, and adaptively obtaining the processing frequencies using group delay moving entropy. The group delay moving entropy method was introduced to select the optimal frequency regions for SSP when detecting multiple targets. The effectiveness of this technique is statistically demonstrated in this paper. The performance is measured in terms of normalized signal-to-noise ratio (SNR) and probability of target detection. SSP with known target information yields a slightly higher probability of detection compared to SSP using group delay moving entropy, while both cases achieve comparable SNR enhancement. The SSP results were also compared with the corresponding bandpass filter outputs, which show superior performance for SSP for a wide range of simulation parameters.     

一种基于DOA分布信息熵加权的线谱目标检测方法

针对常规宽带能量检测方法对低信噪比线谱目标检测性能较差的不足,文章在分析目标线谱波达方向(Direction of Arrival,DOA)估计分布信息熵的基础上,提出一种基于DOA分布信息熵加权的线谱目标检测方法.通过仿真对比分析了该方法的检测性能,并利用海上实验数据验证了其有效性.结果表明,当目标方位较为稳定时,该...     

Dopplerlet变换推广及其在主动声纳中的应用

针对水下高速运动目标的主动探测问题,引入了Dopplerlet变换。根据运动目标回波的特点提出了主动Dopplerlet原子．并分析了基于主动Dopplerlet变换的自适应匹配塔形分解算法,克服了Dopplerlet变换在被动信号处理中声源频率变化引起误差增大的问题。将算法应用于高斯白噪声和高斯色噪声背景中运动目标的检测和速度距离的估计．取得了较好的结果。     

基于声呐历程累积图像的弱回波目标检测方法

针对浅海随机噪声与混响背景下蛙人等弱回波强度、慢速小目标的检测问题,提出一种基于声呐历程累积图像的目标检测方法。首先根据声呐图像时域、空域相关性,采用背景空时归一化处理技术,抑制声呐背景中的静态混响、突发性噪声等强回波干扰。声呐历程累积图像集成了多帧声呐图像的信息,目标回波亮点由于运动连续性形成亮线特征,利用该特征,采用Radon恒虚警率(Radon Constant False Alarm Rate,Radon-CFAR)检测声呐历程累积图像中的目标短时运动轨迹,能够检测到低信噪比的目标。分析了空时归一化处理和检测算法的性能,并通过海试数据验证了该算法的有效性,可以检测到低信噪比的蛙人目标回波。     

基于声呐历程累积图像的弱回波目标检测方法

针对浅海随机噪声与混响背景下蛙人等弱回波强度、慢速小目标的检测问题,提出一种基于声呐历程累积图像的目标检测方法.首先根据声呐图像时域、空域相关性,采用背景空时归一化处理技术,抑制声呐背景中的静态混响、突发性噪声等强回波干扰.声呐历程累积图像集成了多帧声呐图像的信息,目标回波亮点由于运动连续性形成亮线特征,利用该特征,采...     

Signal detection and noise suppression using a wavelet transform signal processor: application to ultrasonic flaw detection

The utilization of signal processing techniques in nondestructive testing, especially in ultrasonics, is widespread. Signal averaging, matched filtering, frequency spectrum analysis, neural nets, and autoregressive analysis have all been used to analyze ultrasonic signals. The Wavelet Transform (WT) is the most recent technique for processing signals with time-varying spectra. Interest in wavelets and their potential applications has resulted in an explosion of papers; some have called the wavelets the most significant mathematical event of the past decade. In this work, the Wavelet Transform is utilized to improve ultrasonic flaw detection in noisy signals as an alternative to the Split-Spectrum Processing (SSP) technique. In SSP, the frequency spectrum of the signal is split using overlapping Gaussian passband filters with different central frequencies and fixed absolute bandwidth. A similar approach is utilized in the WT, but in this case the relative bandwidth is constant, resulting in a filter bank with a self-adjusting window structure that can display the temporal variation of the signal's spectral components with varying resolutions. This property of the WT is extremely useful for detecting flaw echoes embedded in background noise. The detection of ultrasonic pulses using the wavelet transform is described and numerical results show good detection even for signal-to-noise ratios (SNR) of -15 dB. The improvement in detection was experimentally verified using steel samples with simulated flaws.     

Moving target detection using super‐resolution algorithms with an ultra wideband radar

We present subspace based detection algorithms for detecting moving targets applied to the data collected by an ultra wideband radar system. Combining the results of time reversal MUSIC algorithm and delay estimation MUSIC methods resulted in successful localization of targets. We have investigated methods to resolve the ambiguities in target detection and we performed experiments using two targets to test the effectiveness of the algorithms. © 2010 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 20, 237–244, 2010     

汽车防撞雷达多目标识别测量算法研究

针对目前汽车防撞系统中常用的调频连续波测距雷达对多目标识别的不足,基于频移键控雷达的联合作用,提出一种新型的测量方法,该算法采用一种类梯形调频波形,利用调制频率的周期性变化特征分别对单目标和多目标实现有效检测,使雷达不仅可测量单目标在不同运动状态下的距离和速度,同时也可识别多目标,并对其完成测距与测速。利用Matlab对所提出的算法进行了仿真与验证,表明该算法不仅能准确捕捉所探测目标,并且在测量中有着较高的精度,适用于毫米波汽车防撞雷达在行驶中的目标测量。     

Ultrasonic nondestructive evaluation of highly scattering materialsusing adaptive filtering and detection

Adaptive filtering and detection has been applied to the problem of detecting ultrasonic echo signals from test targets where the wanted signals are masked by coherent scattering from grain boundaries present in highly scattering materials. The filter is based on the normalized least mean square (LMS) error algorithm, and can be operated with either an independent reference signal or by using the delayed input signal as the reference. Tests made on a collection of 64 ultrasonic A-scans using the same processing parameters show that an up to 10 dB improvement in signal-to-noise ratio can typically be obtained. A cell-averaging constant false alarm rate (CFAR) detector is used to detect the signals automatically. The performance of the method is compared to that of split spectrum processing, both with and without polarity thresholding     

扩频信号的声光谱相关检测

传统检测扩频信号的声光技术,是将扩频信号建模为随机平稳过程,而扩频信号是被伪随机码调制的周期信号,应建模为循环平稳随机过程。基于声光技术与循环谱相关技术,提出了扩频信号的声光谱相关检测方法。建立了基于声光时间积分的声光谱相关检测模型,分析了当干扰瞄准扩频接收机时,扩频信号在循环频率α=1/Tc的循环谱,给出了循环谱峰的检测公式TcRs1/。仿真实验结果表明,与传统的声光技术检测扩频信号的功率谱相比,在光电检测阵列上检测的循环谱峰,谱峰旁瓣几乎为零,并可多获得约3dB的增益,表明声光谱相关检测方法具有更优异的检测和抗干扰性能。     

空间结构三维风时程模拟及其小波分析

基于谐波叠加法和快速傅里叶变换提出改进方法,用于大跨空间结构的三维相关风速时程模拟。风速功率谱被分解为频率谱函数和相干函数;研究表明在空间三个方向上模拟风速功率谱皆收敛于目标风谱。小波分析在时域和频域上同时具有良好局部化特性。使用小波技术对模拟的风速时程进行时频分析,利用离散小波变换分解和重构风速时程,用小波系数描述风速信号特性。采用最大熵值法减少风速时程分析在时频域上的信息损失并提高效率。计算显示,改进方法速度快、精度高,弥补了传统手段模拟空间结构三维风速时程耗时冗长、精度低的缺陷,小波分析在风速时程分析中具有理想的可靠性和保真性。     

基于运动平台的海底混响建模与仿真

混响是主动声呐目标检测的主要干扰,混响的建模与仿真对研究水下信号处理具有重要的意义。以海底混响为研究对象,综合考虑发射信号的波形、海底散射体散射特性以及运动平台带来的多普勒频移等影响因素,提出了一种基于运动平台的海底混响仿真方法。该方法同时结合了散射原理与网络模型,具有明确的物理意义,且其仿真实现方法简单高效。最后,通过分析混响仿真信号的瞬时值、包络的概率分布、频谱特性以及时间相关性,验证了该方法对海底混响仿真的有效性。     

被动声纳方位估计的宽带盲源分离算法

针对实际被动声纳信号宽带非平稳且统计特性无法预知的特点,由宽带卷积混合模型,建立了融合时间延迟结构与非参数化特性的代价函数,通过核密度技术同时估计目标源的概率密度函数和解混矩阵,并对估计的最优解混矩阵与目标源信号求取每个频点内方位能量谱,最后累加所有子带构成宽带方位能量谱。宽带仿真结果与实际海试表明本文方法在方位分辨率和估计精度方面接近最小方差无失真响应（Minimum variance distortionless response, MVDR）和多重信号分类（Multiple signal classification, MUSIC）算法,在弱目标检测方面具有一定优势。     

一种有效的机载合成孔径雷达动目标成像方法

为实现机载合成孔径雷达(Synthetic Aperture Radar,简称SAR)实际回波数据中的运动目标聚焦成像,本文在用前置滤波法检测出运动目标的基础上,提出了利用目标子图像匹配方法来估计动目标的方位向速度,得到相应的运动目标聚焦参考函数,然后对运动目标进行聚焦成像,并给出了利用该方法得到的运动目标聚焦图像．成像结果表明,目标子图像匹配方法对动目标成像是有效的,易于工程实现,有实际应用价值．     

Speckle photography fringe analysis by the Walsh transform

Two-dimensional Walsh spectral analysis is presented as a new method of numerically processing the Young's fringes diffraction pattern from a double-exposure speckle photograph. The Walsh spectrum of the fringes is more complex than the Fourier spectrum but can be interpreted reliably by further cross correlation of the fringes with square waves. Compared with Fourier spectral analysis, the new technique gives results of almost identical accuracy but with substantially reduced computational effort. The ideas presented have relevance to the general problem of detecting and accurately determining the frequency components of a 2-D sinusoid in the presence of noise.     

光电图像序列运动弱目标实时检测算法

针对光电探测图像序列中的运动弱小目标实时检测问题,提出了一种基于时空域融合滤波的弱目标检测算法。算法在空域上利用形态学Tophat滤波抑制背景增强目标,在时域上通过改进的帧间差分方法增强运动目标,两者融合后经自适应门限分割与航迹关联确认目标。实际录取数据分析结果表明,算法全面考虑运动弱小目标在时域与空域方面的特性,能更有效地从复杂背景中检测低信噪比运动弱小目标,减小了虚警率,抗噪声干扰能力强。     

一种基于色彩处理的被动声呐宽带目标检测显示方法

针对被动声呐传统宽带能量检测方式难以解决强干扰存在时的弱目标检测问题,着眼于目标、干扰及背景噪声频谱特征差异,提出了一种划分子带,利用颜色合成理论进行宽带融合检测的显示方法。针对弱目标存在稳定线谱或在部分频点能量相对较强的情况,可有效提升对弱目标的检测能力,并增强不同目标方位历程的区分度。     

A novel application of a microaccelerometer for target classification

This paper presents a novel method of target classification by means of a microaccelerometer. Its principle is that the seismic signals from moving vehicle targets are detected by a microaccelerometer, and targets are automatically recognized by the advanced signal processing method. The detection system based on the microaccelerometer is small in size, light in weight, has low power consumption and low cost, and can work under severe circumstances for many different applications, such as battlefield surveillance, traffic monitoring, etc. In order to extract features of seismic signals stimulated by different vehicle targets and to recognize targets, seismic properties of typical vehicle targets are researched in this paper. A technique of artificial neural networks (ANNs) is applied to the recognition of seismic signals for vehicle targets. An improved back propagation (BP) algorithm and ANN architecture have been presented to improve learning speed and avoid local minimum points in error curve. The improved BP algorithm has been used for classification and recognition of seismic signals of vehicle targets in the outdoor environment. Through experiments, it can be proven that target seismic properties acquired are correct, ANN is effective to solve the problem of classification and recognition of moving vehicle targets, and the microaccelerometer can be used in vehicle target recognition.     

Automated target detection system for hyperspectral imaging sensors

Over the past several years, hyperspectral sensor technology has evolved to the point where real-time processing for operational applications is achievable. Algorithms supporting such sensors must be fully automated and robust. Our approach, for target detection applications, is to select signatures from a target reflectance library database and project them to the at-sensor and collection-specific radiance domain using the weather forecast or radiosonde data. This enables platform-based detection immediately following data acquisition without the need for further atmospheric compensation. One advantage of this method for reflective hyperspectral sensors is the ability to predict the radiance signatures of targets under multiple illumination conditions. A three-phase approach is implemented, where the library generation and data acquisition phases provide the necessary input for the automated detection phase. In addition to employing the target detector itself, this final phase includes a series of automated filters, adaptive thresholding, and confidence assignments to extract the optimal information from the detection scores for each spectral class. Our prototype software is applied to 50 reflective hyperspectral datacubes to measure detection performance over a range of targets, backgrounds, and environmental conditions.     

基于时域波束信号高阶谱的目标检测技术

为解决常规时域波束形成技术抗噪声能力弱、对弱目标检测能力差的问题,利用高斯噪声的高阶累积量(三阶及三阶以上)为零、非高斯信号的高阶累积量不为零这一性质,对常规时域波束形成后输出的波束信号进行后置处理。首先,对常规时域波束形成后输出的各预成波束信号,分别求其四阶累积量切片谱值;然后,再对各四阶累积量切片谱值分别进行能量累加,得到空间谱图;最后,通过对空间谱在时间上的累积,得到方位历程图。用仿真和海试数据对算法进行了验证:在低信噪比情况下,常规算法不能有效检测到弱目标时,经后置处理后可以有效检测到弱目标。结果表明,与常规时域波束形成算法相比,波束形成后再进行切片谱后置处理的算法增强了对噪声的抑制能力,提高了对弱目标的检测能力。