Volume 115, Issue 5, May 2004
Index of content:
- ACOUSTIC SIGNAL PROCESSING 
115(2004); http://dx.doi.org/10.1121/1.1699395View Description Hide Description
The performance of passive localization algorithms can become severely degraded when the target of interest is in the presence of interferers. In this paper, the eigencomponent association (ECA) method of adaptive interference suppression is presented for signals received on horizontal arrays. ECA uses an eigendecomposition to decompose the cross-spectral density matrix (CSDM) of the data and then beamforms each of the eigenvectors. Using an estimate of the target’s bearing, the target-to-interference power in each eigenvector at each CSDM update is computed to determine which are dominated by interference. Eigenvectors identified to contain low target-to-interference power are subtracted from the CSDM to suppress the interference. Using this approach, ECA is able to rapidly adapt to the hierarchical swapping of target and interference-related eigenvectors due to relative signal power fluctuations and target dynamics. Simulated data examples consisting of a target and two interferers are presented to demonstrate the effectiveness of ECA. These examples show ECA enabling accurate localization estimates in the presence of interferers, which without using the technique was not possible.
115(2004); http://dx.doi.org/10.1121/1.1701897View Description Hide Description
This paper examines and validates regularized inversion for array element localization (AEL) by quantitative comparison of inversion results to direct measurements of receiver positions for a full-scale AEL survey. Regularized AEL treats both receiver and source positions as unknown parameters in a ray-based inversion; prior information on source/receiver positions, inter-receiver spacing in depth, and/or a smooth array shape can be included, subject to statistically fitting the acoustic data. Uncertainties in the recovered receiver positions are estimated via Monte Carlo appraisal. To study this approach, a specially stabilized, two-dimensional receiver array and a series of impulsive sources (imploding glass light bulbs) were deployed from shore-fast (motionless) Arctic sea ice. Sources and recordings were not synchronized in time, so AEL inversions are based on relative arrival times. Receiver positions were measured to an uncertainty of ∼5 cm in each dimension [9 cm in three dimensions (3D)] using nonacoustic (optical) methods. Average AEL errors (difference between measured receiver positions and inversion results) of 13 cm in depth, 27 cm in the horizontal, and 30 cm in 3D, as well as good agreement between the measured errors and estimated AEL uncertainties validate the regularized approach and provide benchmarks for acoustic AEL. Receiver-position errors are quantitatively investigated as a function of the number of sources, source-position errors, and different regularizations.