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631.
The detection of sparse signals against background noise is considered. Detecting signals of such kind is difficult since only a small portion of the signal carries information. Prior knowledge is usually assumed to ease detection. In this paper, we consider the general unknown and arbitrary sparse signal detection problem when no prior knowledge is available. Under a Neyman-Pearson hypothesis-testing framework, a new detection scheme is proposed by combining a generalized likelihood ratio test (GLRT)-like test statistic and convex programming methods which directly exploit sparsity in an underdetermined system of linear equations. We characterize large sample behavior of the proposed method by analyzing its asymptotic performance. Specifically, we give the condition for the Chernoff-consistent detection which shows that the proposed method is very sensitive to the 2 norm energy of the sparse signals. Both the false alarm rate and the miss rate tend to zero at vanishing signal-to-noise ratio (SNR), as long as the signal energy grows at least logarithmically with the problem dimension. Next we give a large deviation analysis to characterize the error exponent for the Neyman-Pearson detection. We derive the oracle error exponent assuming signal knowledge. Then we explicitly derive the error exponent of the proposed scheme and compare it with the oracle exponent. We complement our study with numerical experiments, showing that the proposed method performs in the vicinity of the likelihood ratio test (LRT) method in the finite sample scenario and the error probability degrades exponentially with the number of observations. 相似文献
632.
633.
Radar equipment of stealth platforms such as aircraft have adopted the newest modern technology to design the signal waveforms. One of the important and effective methods is the hybrid waveform called spread spectrum stretch (S-cubed) which combines linear frequency modulation (LFM) and discrete phase code. In order to investigate the function of enemy’s stealth radar equipment, the interception algorithm of S-cubed is needed. In this paper, a novel detection and parameter estimation approach for the reconnaissance S-cubed radar signal is presented. First, the generalized time-frequency representation of Zhao, Atlas, and Marks (ZAM-GTFR) and Hough transforms (HT) are applied to detecting the signal, and then the initial frequency and modulation slope of LFM are estimated from the ZAM-GTFR. On the basis of LFM information, the reconstructing signal is generated. Finally, the code rate of discrete phase code is extracted from the negative peaks of the ZAM-GTFR. Simulation results show that the proposed algorithm has higher estimation accuracy when the signal to noise ratio (SNR) is above 3 dB. 相似文献
634.
635.
针对镍基定向凝固高温合金疲劳寿命的晶体取向相关性,及定向凝固合金涡轮叶片使用过程中的复杂载荷问题,基于循环损伤累积(CDA)方法,引入方向函数修正,并综合考虑应力应变水平、应变比、保载时间及保载形式,建立了ω修正的CDA寿命预测方法。采用定向凝固高温合金DZ125的试验结果进行验证,预测结果与试验数据相比基本落在3倍分散带以内,显示出本文方法较好的适应性。 相似文献
636.
基于高压涡轮叶片寿命损耗的航空发动机功率控制 总被引:2,自引:2,他引:2
提出了基于高压涡轮(HPT)叶片寿命损耗计算的功率控制策略.通过飞机和发动机模型在不同环境条件下进行飞行任务仿真,得到推力需求及HPT叶片温度等参数,采用逆向工程方法进行HPT叶片寿命损耗计算.结果表明:在满足推力需求的同时,采用降低HPT叶片温度的控制策略能明显减少在不同环境条件下HPT叶片寿命损耗.通过不断调整发动机高压涡轮环境温度使之工作在推力需求基线附近,达到了有效延长发动机寿命的目的,验证了高压涡轮叶片寿命损耗计算方法简单可行.表明基于HPT叶片寿命损耗的发动机功率控制降低发动机寿命周期成本的有效性. 相似文献
637.
基于T-S模糊模型的航空发动机非线性分布式控制系统故障诊断 总被引:4,自引:1,他引:4
针对航空发动机非线性分布式控制系统的故障诊断问题,首先提出了一种基于飞行包线划分的航空发动机非线性Takagi-Sugeno(T-S)建模方法,建立了具有网络诱导时延的航空发动机非线性分布式控制系统模型,然后将该系统视为离散切换系统,为其建立了具有时延补偿功能的故障观测器,给出了使得观测器误差系统渐近稳定的充分条件.故障检测仿真时间为20s,当第12s时,设定系统发生幅值为0.0025的阶跃型突变故障,仿真结果表明:12s之前,故障观测器保持渐近稳定,当第12s时,残差迅速增大并超过所设定阈值,从而检测到故障的发生. 相似文献
638.
为深入分析胶-螺混合连接结构强度及影响因素,利用ABAQUS平台建立胶-螺混合连接三维渐进损伤模型,采用界面层损伤判据及退化方法来模拟胶层的损伤破坏。首先,将胶-螺混合连接结构强度分别与胶接结构、螺接结构强度进行对比,详细阐释胶-螺混合连接结构与胶接结构承载机理及胶接区应力分布形式;然后,分析讨论钉头形式及宽径比对混合连接结构强度和损伤形式的影响。结果表明:胶-螺混合连接结构强度优于胶接结构和螺接结构,并得到了可有效提高结构强度的钉头形式及最佳宽径比。 相似文献
639.
Novel approach of crater detection by crater candidate region selection and matrix-pattern-oriented least squares support vector machine 总被引:1,自引:0,他引:1
Impacted craters are commonly found on the surface of planets, satellites, asteroids and other solar system bodies. In order to speed up the rate of constructing the database of craters, it is important to develop crater detection algorithms. This paper presents a novel approach to automatically detect craters on planetary surfaces. The approach contains two parts: crater candidate region selection and crater detection. In the first part, crater candidate region selection is achieved by Kanade-Lucas-Tomasi (KLT) detector. Matrix-pattern-oriented least squares support vector machine (MatLSSVM), as the matrixization version of least square support vector machine (SVM), inherits the advantages of least squares support vector machine (LSSVM), reduces storage space greatly and reserves spatial redundancies within each image matrix compared with general LSSVM. The second part of the approach employs MatLSSVM to design classifier for crater detection. Experimental results on the dataset which comprises 160 preprocessed image patches from Google Mars demonstrate that the accuracy rate of crater detection can be up to 88%. In addition, the outstanding feature of the approach introduced in this paper is that it takes resized crater candidate region as input pattern directly to finish crater detection. The results of the last experiment demonstrate that MatLSSVM-based classifier can detect crater regions effectively on the basis of KLT-based crater candidate region selection. 相似文献
640.