接收机工作特性曲线在红外成像系统检测性能评估中的应用

提出了一种红外弱小目标检测算法性能评估的方法。该方法通过假设的数学模型给出了评价指标的理论推导过程,得到了虚警概率与检测门限,检测概率与信噪比之间的关系,绘出了接收机工作特性（ROC）曲线。同时,给出了在目标与背景分布函数未知的情况下,利用红外图像序列绘出ROC曲线来评估算法的检测性能。利用该方法得到了四种典型的弱小目标检测算法的ROC曲线,比较了四种算法的检测性能。     

Energy detection of radio signals against the background of Nongaussian noise with unknown intensity

We find a distribution of the decision statistic of an adaptive energy detector with training. Expressions for the probabilities of false alarm and correct detection are obtained.     

Target detection algorithm in side-scan sonar image based on pixel importance value measurement

Side-scan sonar detection application always combines with unstable results.A two-stage novel pixel importance value measurement algorithm is proposed to stabilize the detection ability and false alarm probability simultaneously.In first stage of the algorithm,a new feature defined as pixel importance value(PIV) is proposed in terms of distances between the target pixel and each other pixels.PIV measurement of current pixel is defined as the weighted sum of all remaining segmented pixels.The wei...     

Detection of nuclear resonance signals: modification of the receiver operating characteristics using feedback

The performance of a nuclear resonance detection system can be quantified using binary detection theory. Within this framework, signal averaging increases the probability of a correct detection and decreases the probability of a false alarm by reducing the variance of the noise in the average signal. In conjunction with signal averaging, we propose another method based on feedback control concepts that further improves detection performance. By maximizing the nuclear resonance signal amplitude, feedback raises the probability of correct detection. Furthermore, information generated by the feedback algorithm can be used to reduce the probability of false alarm. We discuss the advantages afforded by feedback that cannot be obtained using signal averaging. As an example, we show how this method is applicable to the detection of explosives using nuclear quadrupole resonance.     

像素重要性测量特征下的侧扫声呐目标检测

研究了在较低信噪比下,在保证检测概率的前提下尽量降低虚警概率的目标检测,提出了一种针对特定目标的两阶段筛选算法.第一阶段中,首先使用阈值分割出有效点,并定义了一种新的像素重要性测量特征用于初步筛选目标。即通过有效像素点之间的距离来赋以高斯分布的权值,当前像素重要性的值定义为剩余有效点的距离加权和,具有较高的像素重要性值的聚集性强的区域内像素点会被定位出来。第二阶段,使用卷积神经网络分类器排除虚假目标.在实验中,使用近期无人潜器获得的海底数据,召回率与虚警概率分别达到90.39%与2.39%,证明了其相比声呐目标检测主流算法有更好的检测能力。      

噪声中的线谱检测及自动提取研究

线谱是水中目标检测与识别的重要特征,其自动提取对于水中无人探测系统至关重要。采用统计信号分析方法得出了线谱检测的接收机工作特性曲线,理论分析了线谱自动提取时一定虚警概率下的峰高门限,并提出一种自动门限以适应不同背景方差的变化。实测数据分析表明,本文方法在不同噪声背景下自动提取线谱的效果较好。     

Prediction of the performance of an algorithm for the detection of small targets in infrared images

Detection of small targets in infrared (IR) images is important in IR image processing. For the prediction of performance of a detection algorithm, it is necessary to calculate the probability of detection and probability of false alarm. A method is developed to calculate the probabilities in this paper. The detection is divided into two parts: the first part, which is called pre-detection, is to find out candidates for targets in a single frame of an image; and the second part is to localize the target in multiple frames of the image. Under some assumptions, the pre-detection probability, the false detection probability of single frame, detection probability and false alarm probability are derived. The algorithm for the detection of small target in IR image, which is used for the derivation of the probabilities, is contrast threshold detection based on background prediction, and a pipeline filter is used for multiframe image processing. The results show the relationship of the probabilities to the contrast of target to background, SNR, and contrast threshold.     

An approach for the compensation of the background variability in fire detection systems

Background (BG) variability is an important issue in fire detection. Due to atmospheric situations in general a high correlation can be assumed between the BG infrared radiation for spatially near zones. This correlation can be even greater if similar forest and soil-surface conditions exist in these zones. In this paper we present an approach for fire detection in ground systems. This approach takes into account the temporal and spatial BG behavior. We show that if we use differences of sensor outputs of BG zones with highly correlated surface temperature and reflectance then the false alarm probability is reduced for a given threshold. We also show that for a fixed false alarm probability the use of differences between sensors instead of a single sensor provides higher fire detection probability.     

Probabilistic characteristics of signal detection by a spatial correlation filter

Results of theoretical and model studies of the probabilistic characteristics of signal detection by a spatial correlation filter are described. A mathematical model of signal processing in the spatial correlation filter is presented. Expressions for estimating the detection probability, missed detection probability, false alarm probability, and threshold level, as well as expressions for choosing the optimal filter parameters to obtain preset probability characteristics, are derived on the basis of this model. Results of computer simulation of the probabilistic characteristics of signal detection by spatial correlation filters with random arrays are shown to corroborate the theoretical findings obtained within the model.     

积累检测在光电设备中的应用分析

有些光电检测设备要求高的探测概率和低的虚警概率,使用单次检测不能达到要求。若采用积累榆测法则能满足系统要求。通过对积累检测在不同类型光电设备中的应用分析,建立了不同的数学模型,推导出各模型的探测概率和虚警概率的计算公式,并给出了相应的计算结果。计算结果表明：积累检测在主动探测设备中使用不连续的“K out of N”模型,能够有效提高探测概率,降低虚警概率。但在被动探测设备中,探测概率有所卜降,虚警概率会大大降低。     

中高分辨力遥感图像中飞机目标自动识别算法研究

提出了一种中高分辨力的航空航天遥感图像中飞机目标快速自动识别的新算法。在分割和分类过程中充分利用飞机目标的先验知识,提出了一种改进区域分割方法,并应用树分类器对飞机目标进行自动识别。所提出的改进区域分割方法较好地实现了区域分割中阈值的准确自动选取,克服了复杂背景图像中小目标的全局阈值自动分割的失效问题。采用二叉树分类器,通过提取简单的目标几何特征,分层进行种类识别,提高了识别速度,降低了漏检率和虚警率。运用该方法进行了实验。结果表明,识别率达到了100%。     

变分模态分量降噪后重构的水声弱信号检测

针对能量检测法在低信噪比下对非合作水声探测信号的检测性能显著下降的问题,提出了一种组合变分模态分解和小波变换降噪重构的信号检测方法。以信号分解出的各个本征模态函数的近似熵与互相关系数比值作为分量分类参数,将所得分量分为信号分量、含噪信号分量与噪声分量,然后利用第二代小波变换对含噪信号分量降噪后与信号分量组成重构信号,最后对重构信号进行检测。数值仿真结果表明该方法可以在无先验信息的情况下对CW和LFM信号自适应降噪,信噪比0 dB以下时CW信号重构后信噪比提升约12 dB,宽带LFM信号信噪比提升约8～9 dB,有效提升了低虚警概率下信号的检测概率。湖试结果表明,虚警概率为0.1时检测概率可提升至0.9以上,验证了该方法的有效性。     

Characteristics of the energy detection of unknown radio signals against the background of Likhter noise

We analyze operation of the energy detector in the case of a non-Gaussian noise background. Analytical expressions for the probabilities of correct detection and false alarm of the energy detector receiving unknown signals against the background of Likhter noise are obtained. Translated from Izvestiya Vysshikh Uchebnykh Zavedenii, Radiofizika, Vol. 51, No. 10, pp. 889–898, October 2008.     

在恒虚警检测下的多假目标干扰效果分析

针对多假目标干扰对恒虚警(CFAR)检测雷达的干扰效果评估问题,根据CFAR检测原理,分析了多假目标干扰下雷达CFAR检测门限和不同间隔的多假目标的干扰效果,得到了在一定密度和功率的多假目标干扰下,部分雷达将出现无任何信号输出的自压制状态的结论,最后通过仿真进行了验证,研究可以用于指导多假目标干扰的设计。     

A real-time method for autonomous passive acoustic detection-classification of humpback whales

This paper describes a method for real-time, autonomous, joint detection-classification of humpback whale vocalizations. The approach adapts the spectrogram correlation method used by Mellinger and Clark [J. Acoust. Soc. Am. 107, 3518-3529 (2000)] for bowhead whale endnote detection to the humpback whale problem. The objective is the implementation of a system to determine the presence or absence of humpback whales with passive acoustic methods and to perform this classification with low false alarm rate in real time. Multiple correlation kernels are used due to the diversity of humpback song. The approach also takes advantage of the fact that humpbacks tend to vocalize repeatedly for extended periods of time, and identification is declared only when multiple song units are detected within a fixed time interval. Humpback whale vocalizations from Alaska, Hawaii, and Stellwagen Bank were used to train the algorithm. It was then tested on independent data obtained off Kaena Point, Hawaii in February and March of 2009. Results show that the algorithm successfully classified humpback whales autonomously in real time, with a measured probability of correct classification in excess of 74% and a measured probability of false alarm below 1%.     

红外多波段点目标检测RX算法性能分析

给出了多波段条件下用于点目标检测的RX算法模型,以接收机工作特性曲线ROC以及恒虚警率条件下的检测概率为性能评价手段,分析了探测器像元数、光谱波段数以及广义信噪比对算法性能的影响,并重点分析了波段数与广义信噪比需要满足的匹配关系,据此给出了用于点目标检测的波段参量优化选择方法.最后应用该方法对飞机尾焰进行了最优化探测波段优化选择,证明了该方法的有效性.     

基于光谱角累加的高光谱图像异常检测算法

针对传统方法中用作背景的像素中存在干扰像素的问题,提出一种基于光谱角累加的高光谱图像异常检测算法。通过计算测试区域待测像素光谱向量与其他光谱向量之间的夹角,并将其累加,得到图像中每个像素的异常程度;然后使用波段选择预处理方法进一步提高检测性能。HyMap高光谱数据验证表明,在虚警概率设为0.008时,检测概率达到0.73,即在提高异常检测可靠度的同时,降低了虚警概率。     

基于支持向量描述的自适应高光谱异常检测算法

提出了一种应用于高光谱异常检测的自适应支持向量数据描述方法.根据高光谱数据和局部异常检测模型的特点,通过局部背景分波段二阶分布统计,分析了核参量与局部背景总体标准差的变化关系,构造了随检测背景变化的局部检测核参量,使得检测算法针对不同背景分布自适应地调整检测核参量.克服了传统支持向量描述算法由于采用固定核参量带来的复杂背景下检测性能下降的问题.通过模拟数据和真实高光谱数据的测试检验,接收机特性曲线表明该算法相对于传统固定核参量支持向量数据描述方法,在相同虚警概率下检测概率提高了10%.     

扫描型红外预警系统中弱小目标的检测算法

提出一种基于面阵探测器的全方位红外预警系统的运动弱小目标检测算法.该算法主要基于灰度形态学滤波和目标概率分布函数匹配两种技术.灰度形态学的高帽变换可以有效地检测出红外图像中的特定目标,但没有考虑目标强度分布特征,检测结果的虚警率较高.本文采用了均值偏移算法常用的概率分布函数匹配技术,对检测出的小目标进行二次确认.试验结果表明,该方法能够有效地抑制背景杂波干扰,降低虚警率.     

Energy Detection of Narrow-Band Radio Signals against the Background of Noise of Unknown Intensity

We find the decision-statistic distribution of an adaptive trained energy detector. Expressions for the probabilities of false alarm and true detection are obtained.