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A statistical theory of signal coherence 总被引：1，自引：0，他引：1
A periodic signal can be perfectly predicted far into the future since it perfectly repeats every period. There is always some variation in the waveform over time for signals which are labeled as periodic but which are not truly deterministic. A formal definition is presented in this paper for such a varying periodic signal and the properties of such a class of signals are exploited. A measure called a signal coherence function of the amount of random variation in each Fourier component of the signal is defined and its statistical properties are developed. This signal coherence function is very different from the coherence function between two stationary signals. The method is applied to a digitized record of an acoustic signal generated by a boat in a bag in the Baltic Sea south of Stockholm, Sweden 相似文献
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Two qualitative results concerning statistical sonar signal processing and acoustic field matching are obtained. First, normal-mode field predictions are integrated with statistical signature analysis by constructing a boundary-value problem in the acoustic waveguide. From this construction it is found that the normal-mode filter is the unique acoustic preprocessor which does not confound deterministic waveguide correlation structure with stochastic source covariance structure. Second, the origin of deterministic, Gaussian, and non-Gaussian source signatures is investigated by associating physical parameters with the classical Lindeberg central limit conditions. From construction it is found that there are important objects that are not adequately represented either by infinitesimal points or by infinite surfaces. If receiver resolution is inadequate to resolve source complexity, these objects will exhibit a non-Gaussian acoustic signature via an entirely linear progression from internal excitation, to source radiation, through waveguide propagation, and finally to reception 相似文献
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This paper presents a method for tracking a distant moving target using only bearing measurements obtained from a tracking platform. The method is an improvement of the Hinich-Bloom passive tracking approach presented in [1]. The target is assumed to be moving at constant speed on a fixed heading, whereas the platform maneuvers during the measurement period. The direction cosines of the bearings are computed with respect to a rotation of the coordinate system that places 0° at the mean estimated target bearing. This is done to minimize the approximation bias due to the linearization of sine bearing as a function of inverse range and time. The coordinate system is rotated back to estimate the target coordinates. When the noise is Gaussian, the estimates of target range and heading are approximately maximum likelihood when the target's relative range is slowly varying during the observation period. In this case the mean square errors of the target parameter estimates are the smallest achievable within the order of the approximation. 相似文献
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