共查询到19条相似文献，搜索用时 156 毫秒
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论文首先研究高斯柯西卷积噪声模型和窄带混合高斯噪声模型用来对混响建模,前者主要用于仿真分析,而后者因其参数有明确物理意义且可通过样本序列估计得到,因此被用于海洋的工程实现.文章重点研究混合高斯噪声背景下确知信号的非高斯最佳检测器的构造,并对检测器进行了仿真.验证结果表明混合高斯噪声背景下确知信号的非高斯最佳检测器检测性能优于常规的匹配滤波器3～4.5 dB. 相似文献
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针对舰船地震波信号的非平稳性,将希尔伯特–黄变换(HHT)应用于舰船地震波信号处理中, 并基于 Hilbert 能量谱提出舰船地震波信号实时检测算法。首先,对实测地震波信号进行 HHT 分析,获得信号的时频谱、边际谱、瞬时能量谱以及 Hilbert 能量谱。然后,基于舰船地震波信号 Hilbert 能量谱的特点,选取 Hilbert 能量谱中特定频带内的能量作为检测特征量,利用滑动窗截取数据更新特征量和相应的自适应阈值。 最终,根据一定的检测规则,提出了舰船地震波信号的实时检测算法。实测数据和仿真数据表明：该检测算法能够实时地完成对目标信号的检测,具有较远的检测距离,且在信噪比为–10 dB 时,检测概率达到 100%。 相似文献
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舰船地震波是一种新型的舰船物理场,针对舰船地震波信号对舰船目标的检测问题,提出了一种工程性较强、运算量较小的实时恒虚警概率时频检测算法。 首先从理论上推导了算法的检测性能,该算法只涉及 FFT 变换和一些简单的判别运算,能够在 STM32 单片机上实时地运行该算法;进一步从仿真信号和实测数据 2 个方面对算法进行检验。 结果表明:该算法尽管运算量很小,但仍可有效进行远距离目标检测并设计了识别目标的决策算法,从而确定预警时刻与确认时刻。 相似文献
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为了能够在低信噪比的条件下提高目标的检测概率,降低目标的漏警率和虚警率,提出了一种基于信号特征的舰船轴频电场实时检测方法。 根据舰船的螺旋桨转速与轴频的对应关系确定了特征频段, 将特征频段内经过线谱、连续谱分离及线谱识别后的功率谱估计的最大值作为特征量。 根据环境变化,设置具有自适应功能的动态阈值,对信号进行实时检测,确定疑似目标点。 然后通过模量判断,线谱倍频判断和线谱稳定性判断,最终确认目标是否存在。 对仿真信号和实测信号进行了特征提取和检测,在不同海况和较低信噪比下实现了目标检测。 相似文献
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在高斯白噪声背景下,匹配滤波器作为线性调频信号的最优检测器,在水声信号处理中被广泛应用。 当发射信号为线性调频信号时,由水下目标径向速度引起的多普勒频移会造成回波和样本之间失配, 使匹配滤波器的检测性能下降,增加了目标速度估计的难度。 利用分数阶傅里叶变换对于线性调频信号的聚焦特性,提出了应用分数阶傅里叶变换的水下运动目标线性调频回波检测算法,完成对目标速度的估计, 推导目标运动速度与分数阶傅里叶变换阶数之间的关系,并对测量结果进行误差分析。 仿真测试表明,该算法可有效地估计混响背景下的目标径向速度,且具有良好的估计性能。 相似文献
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舰艇目标航行时往往向水中辐射大量线状谱结构的声波信号,线谱信号检测对舰艇目标的被动探测至关重要。实际水下环境噪声常表现为时域非平稳噪声,常规离散傅里叶变换类线谱检测方法时域积分时,不可避免会引入非平稳噪声背景中出现的短时强干扰噪声,使积分结果中线谱信噪比大幅降低,线谱检测性能急剧恶化。为解决上述问题,对分段离散傅里叶变换结果进行幅度归一化,补偿分段线谱信号间相位差,提出了一种非平稳噪声背景下的线谱相干检测方法。仿真结果表明,相比常规 DFT 类线谱检测方法,所提方法能够降低时域非平稳背景噪声起伏影响,具有更优的非平稳噪声背景下线谱检测性能,并能获得对线谱信号的相干处理增益。 相似文献
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水下火箭发动机工作时,尾部会产生高强度、宽频带背景噪声。在分析火箭发动机噪声产生机理和物理特性的基础上,主要研究了火箭发动机噪声背景下脉冲信号的检测流程和方法。采用自适应消噪和滤波,减弱背景噪声强度并提高了信噪比,建立了火箭发动机噪声背景下脉冲信号的检测方法,解决了强背景噪声脉冲信号难以检测的问题,为火箭发动机水下应用提供了新的思路。 相似文献
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分段拷贝相关器是高斯白噪声背景下,快速衰落水声信道中,对应最大似然比准则的最佳接收机,其输出服从χ2分布。推导出有信号时接收机输出端的概率密度函数和检测概率的解析表达式,计算机仿真结果验证了所获得结果的正确性。最后通过计算无信号时接收机输出的概率密度函数,在给定虚警概率的情况下,求出了接收机的检测门限。 相似文献
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基于器件模拟仿真,设计了一种1.5 μm波长InGaAsP-InP晶体管激光器材料外延结构. 其多量子阱有源区置于基区非对称波导中. 仿真结果显示该外延结构能够获得较好的光场限制和侧向电流限制. 对该材料MOCVD生长研究表明,基极重掺杂接触层中Zn2+扩散将导致量子阱严重退化. 通过对其扩散过程的模拟仿真,采用平均掺杂浓度为1×1018 cm-3的梯度掺杂,有效地抑制了Zn2+向量子阱区的扩散. 所获得的外延材料在 相似文献
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This research investigates whether passive detection of transient signals can be improved by replacing received signals with functionals of the received signals in correlation detectors. Specifically, this paper assesses the impact of using energy spectrum and autocorrelation functional inputs to the cross-correlation (energy), bicorrelation, and tricorrelation detectors. Test signals with differing autocorrelation and energy spectrum properties are used in computer simulations with independent Gaussian noise to evaluate detector performance. Detection results are presented for the case when only two channels of input data are available to form the correlations, as well as the case when three and four channels of input data are available to form the higher order correlations. In the former case, it is shown that detection performance can be improved for the narrow-band signals by using the energy spectrum and autocorrelation functional inputs rather than the original received signal. In the latter case, it is shown that detection performance can be improved by using the autocorrelation input for the broadband signal and the energy spectrum input for the narrow-band signals, and that the tricorrelation detector performs best for the signals tested 相似文献
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An operational passive sonar is required to detect signals from sources, which are subject to spatial and temporal coherence losses via modifications by the ocean environment. Furthermore, these signals are to be detected in the presence of frequency-dependent correlated noise fields. For a system which employs splitbeam cross-correlation processing, the spatial and spectral properties of the signal and noise are of significant import. Therefore, the exact probability density and cumulative distribution functions of the N-sampled correlator outputs of a splitbeam broadband passive sonar are derived for the case of Gaussian inputs which are described by arbitrary cross-spectral density matrices. The validity of approximating the exact probability density function (pdf) as a Gaussian distribution is investigated. The effect of signal coherence loss and noise correlation on the detection performance is considered and the associated processing loss is expressed as a degradation factor within the detection threshold equation 相似文献
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This paper presents an evaluation of second, third, and fourth-order moments for the passive detection of transient signals in both simulated Gaussian noise and measured noise. The measured noise was recorded by a vertical array located near the San Diego, CA, harbor and is dominated at low frequencies by ship-generated noise. The detectors assume neither noise nor signal stationarity and can use single or multiple channels of data. Simulation results indicate that the fourth-order moment detector often performs better than the energy detector in the correlated measured noise, with increasing channel contributions to the moment function, resulting in increased gain. The results in simulated Gaussian noise likewise favor the fourth-order moment detector, at least for the signals with significant fourth-order moments, but the ability of the higher order detector to discriminate against correlated noise is evident. Analysis over a 30-min segment of the measured noise with selected signals demonstrates that fourth-order detection gains can be reliably expected as the noise statistics change. 相似文献
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Several detection statistics are compared in the frequency domain based on the asymptotic probability of detection (APD) criterion. They include second-order, fourth-order, normalized fourth-order, and kurtosis estimates. The results show that for randomly occurring signals which can be characterized as non-Gaussian, the fourth-order, normalized fourth-order, and kurtosis estimates can have higher asymptotic probability of detection levels compared with second-order estimates. But only for the normalized fourth-order and kurtosis estimates do the results seem significant. Moreover, if a second-order estimate of the noise is available to normalize a fourth-order estimate of signal and noise, the resultant normalized fourth-order estimate has higher asymptotic probability of detection levels even for Gaussian signals. This result holds only when there is a significant positive covariance between the numerator and the normalizing noise sample in the denominator. On the other hand, if an independent noise sample is used to normalize a second-order or fourth-order estimate, the overall performance based on the asymptotic probability of detection will be degraded compared with the unnormalized second-order or fourth-order estimates, respectively. 相似文献
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Power spectral density estimation is often employed as a method for signal detection. For signals which occur randomly, a frequency domain kurtosis estimate supplements the power spectral density estimate and, in some cases, can be employed to detect their presence. This has been verified from experiments with real data of randomly occurring signals. In order to better understand the detection of randomly occurring signals, sinusoidal and narrow-band Gaussian signals are considered, which when modeled to represent a fading or multipath environment, are received as non-Ganssian in terms of a frequency domain kurtosis estimate. Several fading and muitipath propagation probability density distributions of practical interest are considered, including Rayleigh and log-normal. The model is generalized to handle transient and frequency modulated signals by taking into account the probability of the signal being in a specific frequency range over the total data interval. It is shown that this model produces kurtosis values consistent with real data measurements. The ability of the power spectral density estimate and the frequency domain kurtosis estimate to detect randomly occurring signals, generated from the model, is compared using the deflection criterion. It is shown, for the cases considered, that over a large range of conditions, the power spectral density estimate is a better statistic based on the deflection criterion. However, there is a small range of conditions over which it appears that the frequency domain kurtosis estimate has an advantage. The real data that initiated this analytical investigation are also presented. 相似文献
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Signal detection is a critical stage in the implementation of any effective communications system. The underwater acoustic environment, particularly in the presence of underwater vehicles, presents significant challenges to reliable detection without excessive false alarms. While there is often sufficient signal-to-noise ratio with respect to stationary broad-band background noise to permit reliable operation, the presence of strong event-like interference signals such as narrow-band signals and impulsive broad-band signals complicates the detection problem significantly. Frequency-hopped signals interleaved with quiescent bands are proposed as the basis of a robust detection system. These signals also make robust detection possible in a multi-access communications system. Two new detection algorithms that exploit the particular structure of these frequency-hopped signals are developed and their performance is analyzed. This analysis uses a modification of the doubly noncentral F-distribution that has not been used previously for such analysis. This distribution makes possible the direct calculation of probabilities of detection and false-alarm under interference and signal scenarios that cannot be analyzed using the better known noncentral F-distribution. With this analysis, the two developed detectors are shown to offer superior performance to that of either the CFAR detector or the binary data sequence detector. Experimental data confirms the theoretically derived results 相似文献