基于自适应空间处理输出噪声轨迹分析的噪声源检测与分类的可能性

G. S. Malyshkin
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引用次数: 0

摘要

为了对目标进行检测和分类,利用水声观测过程中检测到的源轨迹,其中包含目标的测量参数信息,即目标的分类属性。通过对这些属性的分析,可以对观察对象的类别做出决定,例如,是水面源还是水下源。由于被测物体的参数是利用其能量特性,观测轨迹的参数(方位、方位变化速度和其他可能的轨迹参数)。在这种情况下,分类决策的正确性和速度取决于所使用的分类符号的数量和质量,这既取决于被观察对象的参数,也取决于声音从源传播到观察设备的特征。对于信号检测和分辨率,我们进一步考虑了快速投影自适应算法,在[1],[4]中考虑了将其应用于原位实验信号检测问题。这类算法的目标是在真实海洋环境中的多波束传播和散射模型条件下,提供高概率的源轨迹参数检测和精确测量[5]-[8]。本工作是[1],[4]工作的延续,旨在确保实验场数据不仅用于检测,而且用于观测源的分类。本研究的主题是水声噪声测向全尺寸实验的结果,在前面的文献[1]、[4]中有详细描述。实验采用L = 56根垂直雏菊链(每根10根)的天线,水平间距等。天线安装在靠近航道的海岸楔的沿海海洋区域200米深处。平面天线单元受到观测区域内不可控水面舰船和一个水下源信号的影响。采用采样天线元数据的奇异分解构造自适应算法。修改奇异分解的初始结果允许创建算法,为在构建测向地形时提取观测到的单独成分(例如,最弱的)信号提供优先条件。对此,除非自适应测向救济外,提出了测向救济的三种变体,每一种变体都解决了观测信号单个变种的选择和分类的部分一般问题:—初始化,对应于输入样本信号的能量,其中最弱信号的分量被放大(概述算法);它使用一种算法来检测微弱和分散的信号;-测向地形,突出信号的相干成分。在2小时40分钟的时间内,对30多个源的轨迹进行了分析,提高了观测对象参数探测的可靠性和测量精度。基于方位起伏度的不同变体对信号源轨迹进行联合分析,可以改善微弱信号的检测条件,并利用表面目标轨迹波动区宽度的分类属性对具有强散射成分的信号进行分类决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On the possibility of detection and classification of noise sources based on analysis of their trajectories at the output of adaptive spatial processing
To detect and classify objects, the source trajectories detected in the process of hydroacoustic observation are used, which contain information about the measured parameters of objects, which are their classification attributes. The analysis of these attributes allows to make a decision about the class of the observed object, for example, a surface or underwater source. As the measured object parameters are used their energy characteristics, parameters of the observed trajectory (bearing, speed of change in bearing and other possible trajectory parameters). In this case, the correctness and speed of the decision on classification depends on the quantity and quality of used classification signs, which are determined by both parameters of the observed object, and the features of sound propagation from the source to the observation device.For signal detection and resolution, we further consider fast projective adaptive algorithms, the use of which, as applied to the problems of in-situ experimental signal detection was considered in [1], [4]. The goal of this class of algorithms is to provide a high probability of detection and accurate measurement of the parameters of the source trajectories under the conditions of a model of multi-beam propagation and scattering in the real ocean environment [5]-[8]. The proposed work is a continuation of the work [1], [4], and aims to ensure the use of experimental field data not only for detection, but also for the classification of the observed sources.The subject of the study are the results of the full-scale experiment of hydroacoustic noise direction finding, given earlier and described in detail in [1], [4]. For the experiment was used antenna of L = 56 vertical daisy chains (of 10 elements each), equidistantly spaced horizontally. The antenna was installed at a depth of 200 meters in the coastal marine zone of the coastal wedge near the shipping lanes. The elements of the flat antenna were affected by signals from surface ships moving ncontrollably in the observation area and one underwater source.A singular decomposition of sampled antenna element data was used to construct adaptive algorithms. Modification of the initial results of singular decomposition allows to create algorithms that provide priority conditions for extraction of separate components of the observed (e. g., weakest) signals when constructing direction finding terrain.In this regard, in addition to the non-adaptive direction finding relief, it is proposed to form three variants of direction finding relief, each of which solves part of the general problem of selection and classification of individual varieties of observed signals:– Initial, corresponding to the energy of the signals of the input sample with amplified components of the weakest signals (overview algorithm);– direction finding relief, which uses an algorithm to detect weak and scattered signals;– direction finding terrain, which highlights the coherent components of signals.Analysis of the trajectories of more than 30 sources in the episode of two hours and forty minutes has been carried out, which increased the reliability of the detection and measurement accuracy of the parameters of the observed objects. Joint analysis of the trajectories of sources based on different variants of the bearing relief allowed to improve the conditions for the detection of weak signals and to make classification decisions using the classification attribute of the width of the area of fluctuations of the surface target trajectory for signals with a strong scattered component.
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