# Accession Number:

## ADA422439

# Title:

## Covariance Matrix Filtering for Adaptive Beamforming with Moving Interference

# Descriptive Note:

## Conference paper

# Corporate Author:

## JOHNS HOPKINS UNIV LAUREL MD APPLIED PHYSICS LAB

# Personal Author(s):

# Report Date:

## 2001-03-14

# Pagination or Media Count:

## 7.0

# Abstract:

An approach is developed for adaptive beamforming for mobile sonars operating in an environment with moving interference from surface shipping. It is assumed that the sound source of each ship is drawn from an ensemble of Gaussian random noise, but each ship moves at constant speed along a deterministic course. An analytic expression for the ensemble mean covariance is obtained. In practice the location, course, speed, mean noise level, and transmission loss of each interferer are not known with sufficient precision to use the modeled ensemble mean as a basis for adaptive beamforming. The problem is thus to accurately estimate the ensemble mean based on data samples. The analytic ensemble mean is not stationary, and thus is not well estimated by the sample mean. The ensemble of covariance samples consists of rapidly varying random terms associated with the emitted noise and more slowly oscillating deterministic terms associated with the source and receiver motion. The non-stationary ensemble covariance mean can be estimated by filtering out the rapidly varying noise while retaining the slow oscillatory terms. Performance of the filters can be visualized and assessed in the epoch frequency domain, the Fourier transform of the covariance samples. In this domain, higher bearing rates show up at higher frequencies. The traditional sample mean estimator retains only the zero frequency bin corresponding to stationary interference. Techniques that can identify and include the appropriate non-zero frequency contributions are better non-stationary estimators than the sample mean. Several such techniques are offered and compared. Simulations are invaluable in evaluating the filter performance, since the ensemble mean can be precisely calculated analytically in the simulation and compared directly with the sample estimates. Simulations of adaptive beamformers using covariance filtering will be shown to yield improved robustness to shipping motion. 2 figures, 5 refs.

# Descriptors:

- *SHIP NOISE
- *SHIP MOTION
- *MATHEMATICAL FILTERS
- *BEAM FORMING
- *INTERFERENCE
- *ADAPTIVE FILTERS
- *PASSIVE SONAR
- COMPUTERIZED SIMULATION
- SYMPOSIA
- WHITE NOISE
- GAUSSIAN NOISE
- ACOUSTIC DETECTION
- ESTIMATES
- SOUND TRANSMISSION
- SAMPLING
- COVARIANCE
- SURFACE EFFECT SHIPS
- FREQUENCY DOMAIN
- BANDPASS FILTERS
- UNDERWATER SOUND
- SONAR RECEIVERS
- FOURIER ANALYSIS
- TRANSMISSION LOSS
- LOW PASS FILTERS
- SONAR ARRAYS
- LOW FREQUENCIES

# Subject Categories:

- Numerical Mathematics
- Marine Engineering
- Acoustic Detection and Detectors
- Acoustics