Target signature-constrained mixed pixel classification forhyperspectral imagery

Linear spectral mixture analysis has been widely used for subpixel detection and mixed pixel classification. When it is implemented as constrained LSMA, the constraints are generally imposed on abundance fractions in the mixture. In this paper, we consider an alternative approach, which imposes constraints on target signature vectors rather than target abundance fractions. The idea is to constrain directions of target signature vectors of interest in two different ways. One, referred to as linearly constrained minimum variance approach develops a linear filter to constrain these target signature vectors along preassigned directions using a set of specific filter gains while minimizing the filter output variance. Another, referred to as the linearly constrained discriminant analysis (LCDA), is derived from Fisher's linear discriminant analysis, but constrains the Fisher's discriminant vectors along predetermined directions to improve classification performance. Recently, Bowles et al. introduced another target signature-constrained approach, referred to as filter-vectors method, which requires a linear mixture model to implement constraints on target signature vectors. Interestingly, it turns out that the filter-vectors method can be considered as a special version of both linearly constrained minimum variance and linearly constrained discriminant analysis approaches     

FDA-MIMO雷达主瓣距离模糊杂波抑制方法

主瓣上存在的距离模糊杂波降低了真实目标的估计准确度,导致检测性能急剧下降,这对传统雷达提出了严峻挑战。基于频控阵（FDA）-多输入多输出（MIMO）雷达的距离角度二维波束指向,本文提出一种对抗主瓣距离模糊杂波的方法。该方法利用发射-接收空域鉴别模糊杂波次数,并根据已搜索到的目标的大致距离,利用多重信号分类算法（MUSIC）精确分辨多个目标及杂波位置,最后通过设计线性约束最小方差（LCMV）自适应滤波器,使杂波失配而被抑制。该方法有效地解决了主瓣距离模糊杂波的抑制问题,提升了复杂环境下的目标检测性能。仿真验证了提出方法的有效性。      

A novel track maintenance algorithm for PHD/CPHD filter

Probability hypothesis density (PHD) filter and cardinalized PHD (CPHD) filter have proved to be promising algorithms for tracking an unknown number of targets in real time. However, they do not provide the identities of the individual estimated targets, so the target tracks cannot be obtained. To solve this problem, we propose a new track maintenance algorithm based on the cross entropy (CE) technique. Firstly, the particle filter PHD (PF-PHD) algorithm is used to estimate the target states and the target number. Then, the results of the estimation are used as vertexes to construct a connectivity graph with associated weights, and the CE technique is employed as a global optimization scheme to calculate the optimal feasible associated events. Furthermore, due to the advantages of the CPHD filter and the Rao-Blackwellized particle filter (RBPF), we propose another track maintenance algorithm based on the CE technique, named the RBPF–CPHD tracker, which can further improve the track maintenance performance due to the more accurate state estimates and their number estimates. Simulation results show that the proposed algorithms can effectively achieve the track continuity, with stronger robustness and greater anti-jamming capability.     

Adaptive reduced-rank LCMV beamforming algorithms based on joint iterative optimisation of filters

A novel approach to linearly constrained minimum variance (LCMV) beamforming based on reduced-rank processing is proposed. The method is based on a constrained joint iterative optimisation of an adaptive projection matrix and a reduced-rank filter according to the minimum variance criterion. We derive LCMV expressions for the design of the projection matrix and the reduced-rank filter and present low-complexity adaptive algorithms for their efficient implementation. Simulations show that the proposed scheme outperforms the full-rank and existing reduced-rank methods with low complexity.     

基于有效量测分集的联合概率数据互联算法

杂波环境下用联合概率数据互联算法(JPDA)跟踪多目标,其计算量将随跟踪目标数的增多和杂波密度的增大而呈指数增长,因此实时性不强;并且JPDA跟踪杂波环境中的近距离目标时,容易造成航迹合并。在充分考虑单个目标独立性的基础上,提出有效量测分集概念。将接收到的有效量测信号按照单个目标的关联划分,确定各目标跟踪波门内的候选量测信号,不考虑量测信号的重复关联。取得单个目标独立性之后,再运用单目标概率数据互联的方法估计目标状态。仿真实验表明,较传统JPDA实时性更强,能够分辨并跟踪近距离目标。     

基于FRFT的机动目标ISAR成像算法

研究目标机动性不太剧烈、散射点子回波序列多普勒变化满足一阶近似条件下的机动目标ISAR实时成像问题.提出了一种新的基于分数阶傅里叶变换(FRFT)的机动目标ISAR距离一瞬时多普勒成像算法,用FRFT代替传统的快速傅里叶变换(FFT)实现目标的方位向分辨,利用目标运动的先验信息缩小算法的搜索范围,提高运算效率.实测数据处理结果表明该算法较好地解决了机动目标成像问题.与已有的机动目标成像算法相比,该算法在保证成像质量的前提下,大大减小了运算量,可用于机动目标实时成像.     

Object detection using transformed signatures in multitemporalhyperspectral imagery

Changes in atmosphere, ground conditions, and sensor response between multitemporal airborne imaging sessions have limited the use of fixed target hyperspectral libraries in helping to identify targets in heterogeneous (cluttered) backgrounds. This hyperspectral target signature instability has resulted in using anomaly detection algorithms to detect targets in real time applications. The anomaly detection algorithms, however, have not detected targets at sufficiently low false alarm rates. This study examines mathematical transforms of target spectral signatures. Specifically this study uses statistical information regarding background clutter taken from one long-wave infrared (LWIR) hyperspectral (8-12 μm) airborne imagery flown on one day, to find the target spectral signature flown on another day (with significantly dissimilar weather conditions). The transforms use overlapping regions in the two data sets but without subpixel level registration. This work analyzes image cubes collected during the November 1998 Hyperspectral Day/Night Radiometry Assessment (HYDRA) data collect. The transformed signatures used in matched filter searches successfully find targets (even targets nearly covered) with low false alarm rates (<1 FA/kilometer²) and remained sensitive to targets using a reduced number of pixels in the overlap region. This work has demonstrated the transformation of target spectral signatures to search for candidate targets using multitemporal hyperspectral images without requiring accurate geo-registration     

Classification of landmine-like metal targets using widebandelectromagnetic induction

In their previous work, the authors have shown that the detectability of landmines can be improved dramatically by the careful application of signal detection theory to time-domain electromagnetic induction (EMI) data using a purely statistical approach. In this paper, classification of various metallic land-mine-like targets via signal detection theory is investigated using a prototype wideband frequency-domain EMI sensor. An algorithm that incorporates both a theoretical model of the response of such a sensor and the uncertainties regarding the target/sensor orientation is developed. This allows the algorithms to be trained without an extensive data collection. The performance of this approach is evaluated using both simulated and experimental data. The results show that this approach affords substantial classification performance gains over a standard approach, which utilizes the signature obtained when the sensor is centered over the target and located at the mean expected target/sensor distance, and thus ignores the uncertainties inherent in the problem. On the average, a 60% improvement is obtained     

Application of a Passive Polarimetric Infrared Sensor in Improved Detection and Classification of Targets

Automatic detection and recognition of targets by means of passive IR sensors suffer from limitations due to lack of sufficient contrast between the targets and their background, and among the facets of a target. In this paper the results of a suite of polarization-sensitive automatic target detection and recognition algorithms on sets of simulated and real polarimetric IR imagery are presented. A custom designed Polarimetric IR (PIR) imaging sensor is used for collecting real polarimetric target data-three of the four Stokes parameters under a variety of conditions. Then a set of novel algorithms are designed and tested that uses the target and background Stokes parameters for detection, segmentation and classification of targets. The empirical performance results are obtained in terms of the probabilities of detection, false alarm rate, segmentation accuracy, and recognition probabilities as functions of number of pixels on target, aspect and depression angles and under several background conditions (clutter densities) on the polarimetric and non-polarimetrirc data. These results show that a noticeable improvement over the non-polarimetric ATR can be achieved.     

雷达自适应周期MTD 跟踪目标设计应用

提出了一种改进型的动目标检测(MTD)跟踪目标设计,该方法可根据目标速度实时计算信号周期,保持目标回波始终位于非关断滤波器内,通过参数设计控制目标回波的频谱远离关断滤波器,保证目标回波信号始终被检测到,MTD 处理方法又可实时测量目标的速度,形成闭环的自适应周期MTD 跟踪。该方法在某型雷达上得到应用,性能良好。     

Infrared Target Extraction Using Weighted Information Entropy and Adaptive Opening Filter

In infrared (IR) images, near targets have a transient distribution at the boundary region, as opposed to a steady one at the inner region. Based on this fact, this paper proposes a novel IR target extraction method that uses both a weighted information entropy (WIE) and an adaptive opening filter to extract near finely shaped targets in IR images. Firstly, the boundary region of a target is detected using a local variance WIE of an original image. Next, a coarse target region is estimated via a labeling process used on the boundary region of the target. From the estimated coarse target region, a fine target shape is extracted by means of an opening filter having an adaptive structure element. The size of the structure element is decided in accordance with the width information of the target boundary and mean WIE values of windows of varying size. Our experimental results show that the proposed method obtains a better extraction performance than existing algorithms.     

Constrained Detection for Spatial-Multiplexing Multiple-Input–Multiple-Output Systems

A family of detectors that exploit signal constraints is developed for maximum-likelihood detection for multiple-input-multiple-output (MIMO) systems. Real constrained detectors and decision-feedback detectors are proposed for real constellations by forcing the relaxed solution to be real. A generalized minimum mean square error (GMMSE) and constrained least squares MIMO detectors are also developed for unitary and nonunitary signal constellations. Using these constrained detectors, we propose a new ordering scheme to achieve a tradeoff between interference suppression and noise enhancement. Moreover, to mitigate the inherent error propagation, the decision-feedback MIMO detectors are integrated with signal constraints. The simulation results show that our combined detector achieves a significant performance gain over vertical Bell Laboratories layered space-time (V-BLAST) detection.     

基于概率假设密度滤波和动力学方程约束的空间群目标数量和位置分辨

空间目标具有射程远、速度快等特点,为了有效解决密集性高、可分性差的高速空间目标群饱和攻击问题,实现非合作空间群目标数量和位置的尽早分辨,该文基于随机有限集(RFS)理论和动力学方程约束研究了空间“团状”目标数量和位置分辨问题,提出目标监测早期解决大量距离靠近、运动特征差异不明显的高速空间群目标数量和位置估计的相关算法,该算法利用概率假设密度(PHD)滤波器能够解决未知时变环境下目标个数与状态估计的特点,将高斯混合PHD (GM-PHD)滤波和空间目标动力学方程相结合,在解决不可分辨空间群目标数量和位置估计问题的同时,充分利用空间目标动力学方程对群内目标状态进行实时调整,提高空间目标位置状态估计精度,解决不可分辨空间目标群边跟踪边分辨问题,相关算法可为空间群目标数量和群内特殊价值个体目标位置尽快分辨、连续稳定跟踪和可靠动向预报等提供数据基础。     

空间瞬变光辐射目标定位方法

基于瞬变光辐射目标的时空特性,提出了一种基于模板的光辐射目标检测与定位方法.该方法采用加权平均法进行背景估计,并利用Robinson滤波器对残差图像进行二次滤波,有效降低了交叉投影法生成的模板数量.结合瞬变光辐射目标在空间上的局部特性和时间上的瞬时特性,对备选模板进行了多级假设检验.最后利用模板提取了目标区域,求得了目...     

Hyperspectral subpixel target detection using the linear mixingmodel

Relative to multispectral sensing, hyperspectral sensing can increase the detectability of pixel and subpixel size targets by exploiting finer detail in the spectral signatures of targets and natural backgrounds. Over the past several years, different algorithms for the detection of full-pixel or subpixel targets with known spectral signature have been developed. The authors take a closer and more in-depth look at the class of subpixel target detection algorithms that explore the linear mixing model (LMM) to characterize the targets and the interfering background. Sensor noise is modeled as a Gaussian random vector with uncorrelated components of equal variance. The paper makes three key contributions. First, it provides a complete and self-contained theoretical derivation of a subpixel target detector using the generalized likelihood ratio test (GLRT) approach and the LMM. Some other widely used algorithms are obtained as byproducts. The performance of the resulting detector, under the postulated model, is discussed in great detail to illustrate the effects of the various operational factors. Second, it introduces a systematic approach to investigate how well the adopted model characterizes the data, and how robust the detection algorithm is to model-data mismatches. Finally, it compares the derived algorithms with regard to two desirable properties: capacity to operate in constant false alarm rate mode and ability to increase the separation between target and background     

利用灰度和纹理特征的SAR图像分类研究

多类别多特征量情况下的合成孔径雷达(SAR)图像的目标分类是一个难以解决的问题.从灰度和纹理模型出发,提出了综合利用灰度和纹理特征的目标分类方法.均值和方差是灰度模型中重要的特征统计量,而能量、熵、对比度、局部相似性和相关性是纹理模型中重要的特征统计量.灰度和纹理特征能确切地描述SAR图像中的目标.通过构造特征向量,定义向量之间的距离,并按照最小距离方法进行目标分类.以一定大小的窗口读入样本,提高了算法的运行速度和抗噪能力.理论上,窗口越大,特征向量值越接近真实值.窗口越小,边缘的分类精度越高.实验表明该方法较好地处理了多类别多特征量情况下的SAR图像分类问题,分类结果是有效的,这为SAR图像目标分类提供了一条简单可行的途径.     

基于动态交替投影的失配滤波器快速设计算法

雷达脉冲压缩处理中,通常需要使用匹配滤波器以获得最优的输出信噪比。然而,对于常用雷达波形,其匹配滤波器输出距离旁瓣较高,易产生目标遮蔽效应,而通过优化设计失配滤波器可以在牺牲一定输出信噪比的条件下有效抑制距离维旁瓣。另外,在实际应用中一般需要对系统失真进行周期性标校,进而根据经过系统失真后的雷达波形进行失配滤波器设计,所以需要高效率的失配滤波器设计算法。然而,现有的失配滤波器设计算法通常涉及矩阵求逆等复杂计算过程,其计算复杂度高且不利于硬件实现。本文提出一种基于动态交替投影的失配滤波器快速设计算法,可实现在最小峰值旁瓣值、积分旁瓣值、加权积分旁瓣值等准则下的距离维旁瓣抑制,且信噪比损失、主瓣展宽可控。本算法适用于多种脉冲压缩波形,算法过程仅需用到简单计算及快速傅里叶变换,适用于硬件的高效率设计与实现。      

Signal Processing for Passive Radar Using OFDM Waveforms

Passive radar is a concept where illuminators of opportunity are used in a multistatic radar setup. New digital signals, like Digital Audio/Video Broadcast (DAB/DVB), are excellent candidates for this scheme, as they are widely available, can be easily decoded to acquire the noise-free signal, and employ orthogonal frequency division multiplex (OFDM). Multicarrier transmission schemes like OFDM use block channel equalization in the frequency domain, efficiently implemented as a fast Fourier transform, and these channel estimates can directly be used to identify targets based on Fourier analysis across subsequent blocks. In this paper, we derive the exact matched filter formulation for passive radar using OFDM waveforms. We then show that the current approach using Fourier analysis across block channel estimates is equivalent to the matched filter, based on a piecewise constant assumption on the Doppler-induced phase rotation in the time domain. We next present high-resolution algorithms based on the same assumption: first we implement MUSIC as a 2-D spectral estimator using spatial smoothing; then we use the new concept of compressed sensing to identify targets. We compare the new algorithms and the current approach using numerical simulation and experimental data recorded from a DAB network in Germany.