Incoherent radar detection in compound-Gaussian clutter

The detection of incoherent pulse trains in compound-Gaussian disturbance with known spectral density is dealt with here. Two alternative approaches are investigated, The first, assuming perfect knowledge of the signal fluctuation law and implementing the Neyman-Pearson test on the observed waveform, turns out to be not applicable to the radar problem. The second, instead, relying on the generalized likelihood ratio optimization strategy, leads to a canonical detector, whose structure is independent of the clutter amplitude probability density function. Interestingly, this detector turns out to be constant false-alarm rate in the sense that threshold setting does not require any knowledge as to the clutter distribution, Moreover, since such a processor is not implementable in real situations, we also present an FFT-based (fast Fourier transform) suboptimum structure. Finally, we give closed-form formulas for the detection performance of both receivers, showing that both of them largely outperform the square-law detector, especially in the presence of very spiky clutter     

Vector subspace detection in compound-Gaussian clutter. Part I: survey and new results

Deals with the problem of detecting subspace random signals against correlated non-Gaussian clutter exploiting different degrees of knowledge on target and clutter statistical characteristics. The clutter process is modeled by the compound-Gaussian distribution. In the first part of the paper, the optimum Neyman-Pearson (NP) detector, the generalized likelihood ratio test (GLRT), and a constant false-alarm rate (CFAR) detector are sequentially derived both for the Gaussian and the compound-Gaussian scenarios. Different interpretations of the various detectors are provided to highlight the relationships and the differences among them. In particular, we show how the GLRT detector may be recast into an estimator-correlator form and into another form, namely a generalized whitening-matched filter (GWMF), which is the GLRT detector against Gaussian disturbance, compared with a data-dependent threshold. In the second part of this paper, the proposed detectors are tested against both simulated data and measured high resolution sea clutter data to investigate the dependence of their performance on the various clutter and signal parameters.     

Impulse feature extraction method for machinery fault detection using fusion sparse coding and online dictionary learning

Impulse components in vibration signals are important fault features of complex machines. Sparse coding(SC) algorithm has been introduced as an impulse feature extraction method, but it could not guarantee a satisfactory performance in processing vibration signals with heavy background noises. In this paper, a method based on fusion sparse coding(FSC) and online dictionary learning is proposed to extract impulses efficiently. Firstly, fusion scheme of different sparse coding algorithms is presented to ensure higher reconstruction accuracy. Then, an improved online dictionary learning method using FSC scheme is established to obtain redundant dictionary and it can capture specific features of training samples and reconstruct the sparse approximation of vibration signals. Simulation shows that this method has a good performance in solving sparse coefficients and training redundant dictionary compared with other methods. Lastly, the proposed method is further applied to processing aircraft engine rotor vibration signals. Compared with other feature extraction approaches, our method can extract impulse features accurately and efficiently from heavy noisy vibration signal, which has significant supports for machinery fault detection and diagnosis.     

基于压缩感知的信源个数与信号参数估计算法

信源个数与信号参数估计是盲信号处理的关键环节，对后续信号的侦察处理意义重大。针对当前盲信号信源个数与信源参数估计研究割裂的问题，提出了一种联合估计算法。通过分析信号的稀疏系数在不同测量矩阵相同稀疏字典下位置相同的特点，提高了信源个数和信号参数的估计精度，实现算法的自适应控制；通过数理分析确定了多级搜索策略的最优级次，大大降低了稀疏字典的原子数目。仿真结果表明：算法在一定信噪比下能够实现信源个数和信号参数的有效估计；信源个数和信号参数的估计精度随着压缩比的降低而逐渐提高，随着信噪比的提高而逐步增强；噪声对信源个数和信号参数估计精度的影响很大，尤其是低信噪比下；第2个信源载波频率和调频斜率的估计误差明显高于第1个信源参数的估计误差。     

基于功率复用的椭圆球面波函数非正交调制方法

围绕如何降低基于椭圆球面波函数的非正交调制（PSWF-NPSM）系统误码率，降低信号检测复杂度，引入调制信号功率复用思想，提出基于功率复用的椭圆球面波函数非正交调制（PD-PSWF-NPSM）方法。该方法对载波信号按子波带进行功率分配，增加了调制信号最小欧式距离，降低了系统误码率；在接收端，提出基于检测统计量的串行干扰相消信号检测方法，依据不同支路信号间功率差异，对信号进行分离、检测。理论与仿真分析表明，该方法在不降低系统频带利用率、调制信号功率谱、峰均功率比特性前提下，能够有效提高系统误码性能，降低调制信号检测复杂度；与原非正交调制方法相比，当误比特率（BER）为10^-5时，所提方法系统误码性能提升约1.7 dB。     

Unresolved Rayleigh target detection using monopulse measurements

When the returns from two or more targets interfere (i.e., the signals are not resolved in the frequency or time domains) in a monopulse radar system, the direction-of-arrival (DOA) estimate indicated by the monopulse ratio can wander far beyond the angular separation of the targets. Generalized maximum likelihood (GML) detection of the presence of unresolved Rayleigh targets is developed with probability density functions (pdfs) conditioned on the measured amplitude of the target echoes. The Neyman-Pearson detection algorithm uses both the in-phase and quadrature portions of the monopulse ratio and requires no a priori knowledge of the signal-to-noise ratio (SNR) or DOA of either target. Receiver operating characteristic (ROC) curves are given along with simulation results that illustrate the performance and application of the algorithm     

Optimum array detector for a weak signal in unknown noise

We study the design of constant false-alarm rate (CFAR) tests for detecting a rank-one signal in the presence of background Gaussian noise with unknown spatial covariance. We look at invariant tests, i.e., those tests whose performance is independent of the nuisance parameters, like the background noise covariance. Such tests are shown to have the desirable CFAR property. We characterize the class of all such tests by showing that any invariant decision statistic can be written as a function of two basic statistics which are in fact the adaptive matched filter (AMF) statistic and Kelly's generalized likelihood ratio statistic. Further, we establish an optimum test in the limit of low signal-to-noise ratio (SNR), the locally most powerful invariant (LMPI) test. We also derive the bound for the probability of detection of any invariant detector, at a fixed false-alarm rate, and compare the LMPI and the published detectors (Kelly and AMF) to it     

Waveform Design for Multistatic Radar Detection

We derive the optimal Neyman-Pearson (NP) detector and its performance, and then present a methodology for the design of the transmit signal for a multistatic radar receiver. The detector assumes a Swerling I extended target model as well as signal-dependent noise, i.e., clutter. It is shown that the NP detection performance does not immediately lead to an obvious signal design criterion so that as an alternative, a divergence criterion is proposed for signal design. A simple method for maximizing the divergence, termed the maximum marginal allocation algorithm, is presented and is guaranteed to find the global maximum. The overall approach is a generalization of previous work that determined the optimal detector and transmit signal for a monostatic radar.     

Vector subspace detection in compound-Gaussian clutter. Part II: performance analysis

For pt. I see ibid., vol. 38, no. 4, p. 1295 (2002). In this second part we deal with the problem of detecting subspace random signals against correlated non-Gaussian clutter modeled by the compound-Gaussian distribution. In the first part of the paper, we derived the optimum Neyman-Pearson (NP) detector, the generalized likelihood ratio test (GLRT), and a constant false-alarm rate (CFAR) detector; we also provided some interesting interpretations of them. In this second part, these detectors are tested against both simulated data and measured high resolution sea clutter data to investigate the dependence of their performance on the various clutter and signal parameters. Numerical examples concern a space-time adaptive processing (STAP) scenario and a ground-based surveillance radar system scenario.     

Detectors for Multiomial Input

The binary detection problem is considered. Under an arbitrary noise environment, the input sample space can be transformed into a multinomial vector. Based on observations of this vector, the Neyman-Pearson optimal detector is developed for a known signal. When the signal strength is unknown, the likelihood ratio principle is followed to obtain consistent tests which use the Pearson's chisquare statistic. The resulting detectors are compared to others in terms of asymptotic relative efficiency under some actual noise distributions.     

Radar detection and preclassification based on multiple hypothesis

This work presents a single-scan-processing approach to the problem of detecting and preclassifying a radar target that may belong to different target classes. The proposed method is based on a hybrid of the maximum a posteriori (MAP) and Neyman-Pearson (NP) criteria and guarantees the desired constant false alarm rate (CFAR) behavior. The targets are modeled as subspace random signals having zero mean and given covariance matrix. Different target classes are discriminated based on their different signal subspaces, which are specified by their corresponding projection matrices. Performance is investigated by means of numerical analysis and Monte Carlo simulation in terms of probability of false alarm, detection and classification; the extra signal-to-noise power ratio (SNR) necessary to classify once target detection has occurred is also derived.     

基于提前停止判决的编码软信息辅助载波同步

 针对目前编码辅助载波同步算法中复杂度较高、延时大的问题,提出了引入辅助停止判决机制的编码辅助载波同步算法。在现有的编码辅助载波同步结构基础上,该算法能对环路信噪比(SNR)进行实时判定,在环路SNR满足限定条件后提前停止编码辅助载波同步迭代,而不影响译码性能。采用新的相位估计方式估计含相位噪声的载波相位,提升了该条件下的环路信噪比。仿真采用码率为1/2的低密度奇偶校验(LDPC)码作为编码方式,结果表明:在误码率为10^-5时,该算法减少了约50%的编码辅助载波同步迭代次数;在含相位噪声的信号条件下,与理想解调译码相比,性能损失不超过0.15 dB。     

分布式接收宽带多目标信号盲检测迭代处理方法

随着软硬件技术的飞速发展和宽带接收机的广泛使用,频谱检测向着高瞬时带宽的方向发展,传统基于信道化处理的频谱检测方法存在搜索速度慢、处理效率低下的问题。文章提出了 1种新的分布式接收宽带多目标信号盲检测迭代处理方法,在无须预先知道信号数目及信号频谱位置的情况下,能够实现特定虚警概率多信号盲检测,具备较高的灵活性和稳健性。首先,在对信号特征进行分析的基础上,通过构造线性模型,将分布式接收多目标信号检测转化为线性模型求解问题进行处理;然后,基于贝叶斯多参数联合求解模型,在对未知参数先验分布进行合理假设的基础上,推导了各未知参数变分分布及信号检测门限的解析表达式,采用变分分布软信息迭代的方式实现多传感器信号、多参数联合估计,并利用每次迭代参数估计结果,对信号检测门限进行更新,通过置零操作实现预设虚警概率下的多信号盲检测;最后,通过仿真实验对所提方法性能进行了分析,并与相关方法进行了对比。仿真结果表明,所提方法能够有效利用多路接收信号信息,实现宽带未知多目标信号的盲检测,有效提升短数据下的算法处理效能,与现有方法相比,在接收单元数目较多以及信噪比较低时具有明显优势。     

基于Kalman滤波的高频CW电报信号自动识别

针对强噪声背景下高频CW电报信号检测算法性能严重下降、误码率较高的问题,文章提出一种基于卡尔曼滤波的高频CW电报信号同步检测识别算法。利用自同步法对CW电报信号实现位同步,进而利用卡尔曼滤波针对时变干扰噪声设置自适应阈值,对信号能量进行软判决,实现CW电报信号的自适应跟踪检测,提取有效信号进行识别。通过短波信道仿真软件和实际短波通信测试表明,该算法能够在强噪声背景下有效检测识别CW电报信号,且算法可由迭代实现。     

基于稀疏表示的多雷达信号二维融合处理

多雷达信号融合通过对多视角和多频带雷达信号进行相干融合,可以提高图像的距离和方位向分辨率.为了克服基于谱估计的多雷达信号融合方法稳健性严重依赖于散射点个数估计精度和二维极点配对精度的问题,在深入研究逆合成孔径雷达(ISAR)信号的基础上,构造了多雷达信号二维融合的线性表示模型,将融合处理转化为一个信号表示问题;充分挖掘...     

基于空域稀疏性的宽带DOA估计

利用宽带阵列接收信号的空域稀疏性,将宽带信号的波达方向(DOA)估计转化为一个稀疏信号重构的问题,提出了一种新的宽带信号DOA估计算法。该算法将宽带信号分解为多个子带信号,联合利用多个子带信号的空域稀疏性进行重构。它是对用于稀疏重构的标准的稀疏贝叶斯学习算法的推广,可适用于多冗余字典的信号模型。另外,通过对多快拍的阵列接收信号进行奇异值分解(SVD),提取信号子空间作为算法的输入数据,可以在有效减少运算复杂度的同时,提高对噪声的稳健性。与传统的宽带阵列DOA估计方法相比,该算法能够用于低信噪比、快拍有限和信源相关性较高的场合,同时算法的性能对信源个数的估计值不太敏感。仿真实验表明,该算法相对现有的基于子空间类的方法,具有更好的DOA估计性能。     

Rate-constrained target detection

Given little or no a priori information, a real world detection system has the task of allocating limited resources. Often these resources are themselves detection systems that operate at a slower rate, for example a tracker following a radar. Detection systems that are described by serial multiple decision points with each decision device assumed to be more reliable but slower than the device preceding it are considered. It is shown that because of the rate constraint the Neyman-Pearson criterion is suboptimum. An optimum rate-constraint test is developed which has the same likelihood ratio form as the Neyman-Pearson test. A strategy for controlling multiple-point detection sequences is developed that depends only on local information and is shown to be optimum under fairly broad conditions. The strategy can be implemented in practical systems, since it depends on the hit rate which is both controllable and observable. This approach to decision-making has applications in many fields and shows a promise as both an analysis and design tool     

基于改进圆域分割方法的混沌微弱信号检测

针对Melnikov方法和Lyapunov指数法在确定Duffing振子混沌阈值时计算繁琐、耗时较长的问题,提出基于改进圆域分割的相图识别方法。以圆域内相点比例为指标,利用其在系统相变时急剧变化这一特性进行微弱信号的检测。改进圆域分割方法的圆域半径取值更加灵活,使混沌阈值的确定更加方便。仿真实验表明,信噪比为-60 d B时检测正确率达89%,验证了该方法的有效性。     

Numerical Results on the Convergence of Relative Efficiencies

This paper considers the detection of a known constant signal in an additive non-Gaussian noise under the assumptions of discrete time and statistically independent noise samples. The objective is to determine how large sample size must be before the easily computed asymptotic relative efficiency becomes a valid measure of performance. The exact small-sample error probabilities are calculated for a Neyman-Pearson optimal nonlinear detector consisting of a zeromemory nonlinearity followed by summation and threshold comparison. "Large-tailed" noise having a double exponential distribution is used as an example. The exact distribution of the test statistics for a linear detector and for the Neyman-Pearson optimal detector are calculated. Then the relative efficiency of the Neyman-Pearson optimal detector, as compared to a linear detector, is computed in order to study the rate of approach of the relative efficiency to its asymptotic value.     

基于FRFT的雷达信号chirp基稀疏特征提取 及分选

 特征分析是雷达信号分选识别的基础,利用稀疏分解思想对新体制雷达信号进行特征提取是一个新的研究方向。本文以分数阶Fourier变换的核函数作为稀疏分解的chirp基函数,将具有相近特征参数的chirp基函数构成基函数族用于稀疏分量提取,推导了在分数阶Fourier域基于匹配跟踪的chirp基函数族稀疏分解公式,然后利用chirp基稀疏分量的调频率和初始频率构成特征参数序列,将雷达信号脉冲分成5大类进行分选和识别,仿真分析验证了推导结果的有效性。结果表明对于具有线性或曲线时频特征的雷达信号在信噪比为-3 dB,采样频率为500 MHz,观测时间为2 μs,调频率不超过100 MHz/μs时,仍然具有95%的正确分选概率。