Spin-image surface matching based target recognition in laser radar range imagery

We explore the problem of in-plane rotation-invariance existing in the vertical detection of laser radar (Ladar) using the algorithm of spin-image surface matching. The method used to recognize the target in the range imagery of Ladar is time-consuming, owing to its complicated procedure, which violates the requirement of real-time target recognition in practical applications. To simplify the troublesome procedures, we improve the spin-image algorithm by introducing a statistical correlated coefficient into target recognition in range imagery of Ladar. The system performance is demonstrated on sixteen simulated noise range images with targets rotated through an arbitrary angle in plane. A high efficiency and an acceptable recognition rate obtained herein testify the validity of the improved algorithm for practical applications. The proposed algorithm not only solves the problem of in-plane rotation-invariance rationally, but also meets the real-time requirement. This paper ends with a comparison of the proposed method and the previous one.     

A surface matching approach to target recognition from forward-looking laser radar imagery

A modified spin-image surface matching system for out-of-plane rotation-invariant recognition in forward-looking laser radar (Ladar) range imagery has been described in the literature. In this paper, we experimentally address the relation between the training-set size used in the new method and recognition rate and present a method for selecting the training sample images. The modified system has attractive properties that include: rotated invariance, simplified training-set selection, predigestion from the 3-D range image to a set of 2-D images, tolerance for a significant amount of noise, no feature extraction. Each of these properties is investigated in the paper.     

Correlation recognition for range image of laser radar

The target recognition of laser radar becomes hot research in recent years, because laser radar can produce high space resolution and collect rich target information, such as range image, intensity image and Doppler image. In the vertical detection of laser radar, the problem of in-plane target rotation invariance is firstly solved. In the paper, a new support vector machine (SVM) correlation filter is presented, which simultaneously has the attractive attributes of SVM and common correlation filter. Exploiting the idea of margin of separation maximization, the design criterion is produced. The filter is synthetic by the multiple training images which are generated by rotating one image. The real range images of laser radar are used to finish the correlation experiments. The results show that the filter is not sensitive to the noise, the correlation peak is changed slightly for the different testing images, and the precision of location is high.This design way can be used in other recognition fields.     

Processing for laser radar range images

Imaging laser radar can give intensity and range images, which provide integrated 3-dimensional (3D)information about objects. However, dropouts and range anomalies exacerbate range images, which makes their background cluttered and target blurred. For background suppression, a new algorithm that combines intensity image and its mean is presented. By using this algorithm to process actual laser radar range images, most background noises are suppressed. According to range anomalies characteristics, multitemplate selection order mean filtering algorithm is presented and used for actual ladar range images where the distance between two targets is 77 m. This algorithm obtains the clear range image in which the interval of two objects is 75 m. The result shows that the processing algorithm is correct and effective.     

基于环向特征的快速旋转不变性匹配算法

景象匹配中经常会出现基准图与模板图之间存在任意角度旋转的情况,传统的结构化模板匹配算法是通过逐个比较基准图与模板图对应像素灰度值之间的相关性来进行匹配的,所以只能工作在小角度旋转的情况下,对任意角度旋转的景象匹配是无能为力的.利用圆的各向同性和投影特征提出了圆环投影匹配算法,并构造了环向特征投影不变量,用环向特征作匹配.实验结果表明,在同等条件下,该方法较圆投影算法具有更高的鲁棒性,且匹配速度可增快7倍,匹配概率可提高10%～20%.     

Polarimetric laser radar target classification

Imaging laser radar (ladar) systems have been developed for automatic target identification in surveillance systems. Ladar uses the range value at the target pixels to estimate the target's 3-D shape and identify the target. For targets in clutter and partially hidden targets, there are ambiguities in determining which pixels are on target that lead to uncertainties in determining the target's 3-D shape. An improvement is to use the polarization components of the reflected light. We describe the operation and preliminary evaluation of a polarization diverse imaging ladar system. Using a combination of intensity, range, and degree of polarization, we are better able to identify and distinguish the target from other objects of the same class.     

Image recognition of laser radar using linear SVM correlation filter

Through deducing the relationship between support vector machine (SVM) and correlation principle, the optimal hyperplane is proved as a correlation filter when the kernel function is the linear kernel. So a new correlation filter, named linear SVM correlation filter (LSCF), is proposed. The filter has not only shift-invariance, but also SVM properties. The real images of laser radar are used as experiment data, and LSCF is used to solve the in-plane rotation invariance. The results show that the filter can recognize the different rotated objects, and the correlation output is stable. The filter is insensitive to the noise and gray change, and has good discrimination ability. In the same design way, LSCF is also suitable to solve other problems of correlation distortion.     

Optical circular sampling system for translation and rotation invariant pattern recognition

A simple but effective rotation invariant pattern recognition method based on optical circular sampling of the input pattern is described. The object is decomposed by a set of concentric rings. The distribution of energy between those rings is independent of the orientation of the object and characterizes it sufficiently to allow its classification. A computer generated hologram formed of concentric diffraction grating rings of suitable spatial frequencies may perform the sampling in parallel. In combination with a lens, each ring is then focused to a given position on a CCD detector where the intensity distribution may be found. Translation invariance is added to the system by sampling the intensity of the Fourier transform of the input object in combination with a liquid crystal light valve. A phototypesetter is used to write the hologram whose quality is demonstrated by optical experiments.     

相位补偿算法对提高太赫兹雷达距离像分辨率的研究

介绍了0.2 THz频率步进雷达系统以及获得一维距离像的方法,并利用0.2 THz雷达对角反射器进行距离像分辨率实验,分析了频率步进信号相位不一致对一维距离像以及分辨率的影响,提出了回波相位补偿的方法.经过相位补偿后,目标距离像分辨率和信噪比都显著提高,分辨率达到了厘米量级.仿真和实验结果表明,宽带太赫兹频率步进雷达经过相位补偿,可以对目标进行高分辨率成像,从而为太赫兹雷达二维和三维成像奠定了基础.     

基于光谱角时序不变性的红外目标识别

为了识别空间目标与气球诱饵,提出了基于光谱角时序不变性的红外目标识别方法。通过分析证实了空间目标与气球诱饵在温度时变特性上存在差异。为了反映目标测量功率温度时变本质,消除传感器与目标距离以及等效投影面积的时变耦合对测量功率的影响,提出了利用时序光谱角作为光谱向量的时序变化量度。该量度能够刻画光谱曲线的时序变化,反演物体温度时变特性,可以作为温度时变特性不同的目标和诱饵之间聚类识别的有效判据。仿真结果表明：该方法能够有效识别空间目标与气球诱饵;通过融合时序光谱特征,为空间目标识别提供了一种新的机制。     

激光雷达标准目标散射截面的研究

讨论了激光雷达散射截面的物理意义 ;给出了用双向反射分布函数来表示激光雷达散射截面的一般表达式 ;推出了在标准目标分别为点目标和扩展目标时激光雷达散射截面的解析表达式和数值表达式 ,并对入射光束在分别为高斯分布和均匀分布时的结果进行了分析     

Robust passive range estimation using the waveguide invariant

The waveguide invariant principle is used to estimate the range to a broadband acoustic source in a shallow-water waveguide using a single acoustic receiver towed along a path directly toward the acoustic source. A relationship between the signal processing parameters and the ocean-acoustic environmental parameters is used to increase the effective signal-to-noise ratio without requiring detailed knowledge of the environment. Heuristics are developed to estimate the minimum source bandwidth and minimum horizontal aperture required for range estimation. A range estimation algorithm is tested on experimental and simulated data for source ranges of 500-2200 m and frequencies from 350 to 700 Hz. The algorithm is accurate to within approximately 25% for the cases tested and requires only a minimal amount of a priori environmental knowledge.     

Ultra-high range resolution demonstration of a photonics-based microwave radar using a high-repetition-rate mode-locked fiber laser

We experimentally demonstrate the ultra-high range resolution of a photonics-based microwave radar using a high repetition rate actively mode-locked laser(AMLL). The transmitted signal and sampling clock in the radar originate from the same AMLL to achieve a large instantaneous bandwidth. A Ka band linearly frequency modulated signal with a bandwidth up to 8 GHz is successfully generated and processed with the electro-optical upconversion and direct photonic sampling. The minor lobe suppression(MLS) algorithm is adopted to enhance the dynamic range at a cost of the range resolution. Two-target discrimination with the MLS algorithm proves the range resolution reaches 2.8 cm. The AMLL-based microwave-photonics radar shows promising applications in high-resolution imaging radars having the features of high-frequency band and large bandwidth.     

单水听器波导不变量被动测距

本文主要讨论利用声场干涉现象由单水听器被动测距的可行性及测距性能,为此提出了单水听器波导不变量被动测距方法,通过提取LOFAR图上干涉条纹、使用简正波模型计算波导不变量、频域相关法估计相对速度,最后依据干涉条纹方程得到声源距离估计量。数值仿真和海上实验结果验证了单水听器被动测距的可行性,并具备一定测距性能,在3 dB信噪比环境中,对于7 km处的运动声源,平均测距误差小于5%。本文方法具有设备简单、易于推广至阵列信号处理等特点,为声纳信号处理环境宽容性的提高及环境适配声纳的设计开拓了思路。     

Reduction of range walk error in direct detection laser radar using a Geiger mode avalanche photodiode

In this paper a new technique to improve the accuracy of direct detection time-of-flight (TOF) laser radar using a Geiger mode avalanche photodiode (APD) by reducing range walk error is presented. Assuming that the creation of primary electrons in the APD is Poisson-distributed, a mathematical model for the detection probability of each time bin in a TOF histogram is derived. It is shown that the range walk error depends on the energy of the laser-return pulse at the Geiger mode APD with the mathematical model regarded as the TOF histogram of multiple laser pulses in a single-hit case. The method which reduces the range walk error with the center of mass detection in the TOF histogram is then proposed, and the experiment for its proof is carried out.     

Binary phase only filters for rotation and scale invariant pattern recognition with the joint transform correlator

The joint transform correlator (JTC) is one of the two main optical image processing architecture which provides a highly effective way of comparing images in a wide range of applications. Traditionally, an optical correlator is used to compare an unknown input scene with a pre-captured reference image library, to detect if the reference occurs within the input. Strength of the correlation signal decreases rapidly as the input object rotates or varies in scale relative to the reference object. The aim of this paper is to overcome the intolerance of the JTC to rotation and scale changes in the target image. Many JTC systems are constructed with the use of ferroelectric liquid crystal (FLC) spatial light modulators (SLMs) as they provide fast two-dimensional binary modulation of coherent light. Due to the binary nature of the FLC SLMs used in the JTC systems, any image addressed to the device need to have some form of thresholding. Carefully thresholding the grey scale input plane and the joint power spectrum (JPS) has significant effect on the quality of correlation peaks and zero order (DC) noise. A new thresholding technique to binarise the JPS has been developed and implemented optically. This algorithm selectively enhances the desirable fringes in the JPS which provide correlation peaks of higher intensity. Zero order noise is further reduced when compared to existing thresholding techniques.Keeping in mind the architecture of the JTC and limitations of FLC SLMs, a new technique to design rotation and scale invariant binary phase only filters for the JTC architecture is presented. Filers design with this technique have limited dynamic range, higher discriminability among target and non-target objects, and convenience for implementation on FLC SLMs. Simulation and experiments shows excellent results of various rotation and scale invariant filters designed with this technique. A rotation invariant filter is needed for various machine vision applications of the JTC. By fixing the distance between camera and input object, the scale sensitivity of the correlator can be avoided. In contrast to the industrial machine vision applications, scale factor is very important factor for the applications of the JTC systems in defence and security. A security system using a scale invariant JTC will be able to detect a target object well in advance and will provide more time to take a decision.     

基于卷积神经网络的雷达目标航迹识别研究

现代战争中雷达信号日趋复杂,如何快速准确地从种类繁多、数据量庞大的雷达检测数据中,获取目标航迹的类别信息,为战场指挥提供准确有效的信息是当前急需解决的难题。传统基于人的经验认知的雷达目标航迹识别方法已经无法有效应对瞬息万变的战场和海量数据。根据实际雷达数据特点,提出了使用对数的雷达航迹预处理方法,并构建了基于卷积神经网络的深度学习模型,实现了对雷达对抗中的目标航迹的识别与检测。基于模拟生成的雷达目标航迹数据对提出的数据预处理方法和构建的模型进行测试;实验表明,所提出的方法能很好地实现对目标航迹的检测与识别。     

一种改进的块匹配运动目标识别算法

鉴于传统的块匹配法在图像特征提取、匹配以及搜索等方面有诸多不足,复杂的边缘提取处理与基于灰度的匹配准则都使得系统的处理速度与精度降低,为了使识别处理更加快速精确,提出了一种改进的块匹配识别算法。通过拉普拉斯算子对图像特征进行增强,提高图像的特征信息;实验表明,针对320像素240像素大小的图像,改进算法的特征增强计算时间在Matlab中平均约为0005 4 s。