Index of content:
Volume 106, Issue 6, December 1999
- ACOUSTIC SIGNAL PROCESSING 
Passive wideband cross correlation with differential Doppler compensation using the continuous wavelet transform106(1999); http://dx.doi.org/10.1121/1.428197View Description Hide Description
An estimate of the time difference for the signal emitted by a stationary source to arrive at two spatially separated sensors is given by the time displacement that maximizes the cross-correlation function. For a fast moving source, however, this estimate is found to be in error because the time scales of the received signals are different for the two sensors. The correct time delay can be extracted by evaluating the continuous wavelet transform, which has the same functional form as the wideband cross-ambiguity function. When the signal-to-noise ratio is high, the coordinates of the ambiguity surface’s global maximum provide reliable estimates of both the differential time of arrival (or time delay) and the ratio of the time scales of the signals received by the two sensors. The continuous wavelet transform is computed using the one-step chirpz-transform method, the cross-wavelet transform method, and the two-step methods where multirate sampled replicas of the sensor waveforms are cross correlated, or else the sensor waveforms are interpolated using the discrete Fourier transform prior to cross correlation. The latter method is applied to real acoustic data recorded from an orthogonal configuration of three microphones during the low-altitude transit of a jet aircraft. The resulting time delay estimates are used to calculate the variation with time of the azimuth and elevation angles of the aircraft during the transit.
106(1999); http://dx.doi.org/10.1121/1.428198View Description Hide Description
Advanced array processing methods require accurate knowledge of the location of individual elements in a sensor array. Array element localization (AEL) methods are typically based on inverting acoustic travel-time measurements from a series of controlled sources at well-known positions to the sensors to be localized. An important issue in AEL is designing the configuration of source positions: a well-designed configuration can produce substantially better sensor localization than a poor configuration. In this paper, the effects of the source configuration and of errors in the data, source positions, and ocean sound speed are quantified using a sensor-position error measure based on the a posteriori uncertainty of a general formulation of the AEL inverse problem. Optimal AEL source configurations are determined by minimizing this error measure with respect to the source positions using an efficient hybrid optimization algorithm. This approach is highly flexible, and can be applied to any sensor configuration and combination of errors; it is also straightforward to apply constraints to the source positions, or to include the effects of data errors that vary with range. The ability to determine optimal source configurations as a function of the number of sources and of the errors in the data, source positions, and sound speed allows the effects of each of these factors to be examined quantitatively in a consistent manner. A modeling study considering these factors can guide in the design of AEL systems to meet specific objectives for sensor localization.