Browsing by Subject "Source Localization"

This thesis addresses the problem of source localization and time-varying spatial spectrum estimation with maneuverable arrays. Two applications, each having different environmental assumptions and array geometries, are considered: 1) passive broadband source localization with a rigid 2-sensor array in a shallow water, multipath environment and 2) time-varying spatial spectrum estimation with a large, flexible towed array. Although both applications differ, the processing scheme associated with each is designed to exploit array maneuverability for improved localization and detection performance.

In the first application considered, passive broadband source localization is accomplished via time delay estimation (TDE). Conventional TDE methods, such as the generalized cross-correlation (GCC) method, make the assumption of a direct-path signal model and thus suffer localization performance loss in shallow water, multipath environments. Correlated multipath returns can result in spurious peaks in GCC outputs resulting in large bearing estimate errors. A new algorithm that exploits array maneuverability is presented here. The multiple orientation geometric averaging (MOGA) technique geometrically averages cross-correlation outputs to obtain a multipath-robust TDE. A broadband multipath simulation is presented and results indicate that the MOGA effectively suppresses correlated multipath returns in the TDE.

The second application addresses the problem of field directionality mapping (FDM) or spatial spectrum estimation in dynamic environments with a maneuverable towed acoustic array. Array processing algorithms for towed arrays are typically designed assuming the array is straight, and are thus degraded during tow ship maneuvers. In this thesis, maneuvering the array is treated as a feature allowing for left and right disambiguation as well as improved resolution towards endfire. The Cramer Rao lower bound is used to motivate the improvement in source localization which can be theoretically achieved by exploiting array maneuverability. Two methods for estimating time-varying field directionality with a maneuvering array are presented: 1) maximum likelihood estimation solved using the expectation maximization (EM) algorithm and 2) a non-negative least squares (NNLS) approach. The NNLS method is designed to compute the field directionality from beamformed power outputs, while the ML algorithm uses raw sensor data. A multi-source simulation is used to illustrate both the proposed algorithms' ability to suppress ambiguous towed-array backlobes and resolve closely spaced interferers near endfire which pose challenges for conventional beamforming approaches especially during array maneuvers. Receiver operating characteristics (ROCs) are presented to evaluate the algorithms' detection performance versus SNR. Results indicate that both FDM algorithms offer the potential to provide superior detection performance in the presence of noise and interfering backlobes when compared to conventional beamforming with a maneuverable array.

In this thesis, we develop methods for addressing the deficiencies of array processing with linear thinned arrays. Our methods are designed for array systems mounted on moving platforms and exploit synthetic aperture processing techniques. In particular, we use array motion to decrease the sidelobe levels and increase the degrees of freedom available from thinned array systems. In this work, we consider two application areas 1) passive SONAR and 2) ultrasound imaging.

Synthetic aperture processing is a methodology for exploiting array motion and has been successfully used in practice to increase array resolution. By spatially sampling along the path of the array virtual sensors can be realized and coherently fused to the existing array. The novel contribution of this work is our application of synthetic aperture processing. Here our goal is not to increase array resolution, instead we propose to use the synthetic aperture process to expand the spatial covariance and spatial frequency sensing capabilities of thinned array system.

In the passive sensing case, we use a class of thinned arrays know as co-prime linear sensor arrays for source localization. The class of co-prime arrays provides roughly half the aperture worth of spatial covariances and with modest array motion can be extended to the full aperture of the array. The amount of motion required to produce a full set of spatial covariances is shown to be a function of the co-prime array parameters and is only a fraction of the total aperture of the array. The full set of spatial covariances can be used to form a spatial covariance matrix with dimension equal to that of a uniform array. With a spatial covariance matrix in hand one can perform signal processing tasks as if the array were fully populated. Three methods for spatial covariance matrix estimation are compared in different source localization scenarios. In the work presented here, we demonstrate the benefits of our approach for achieving reduced sidelobe levels and extending the source localization capabilities above the limits of the static co-prime array.

In the active sensing case, we develop a framework for incorporating motion using thinned arrays for ultrasound imaging. In this setting, array motion is used to augment the spatial frequency sensing capabilities of the thinned array system. Here we develop an augmentation strategy based on using quarter-wavelength array translations to fill-in missing spatial frequencies not measured by the static thinned array. The quarter-wavelength translation enables the thinned array system to sample missing spatial frequencies and increase the redundancy of other spatial frequencies sampled by the array. We compare the level of redundancy in sampling the spatial frequencies achieved by the thinned arrays post translation to different levels of sample redundancy derived from pruning the transmit/receive events of a uniform array. In this manner, we are able to examine how the level of spatial frequency redundancy afforded by different thinned arrays compare over the full redundancy range of the uniform array. While artificially pruning the uniform array does not necessarily create realizable arrays, it provides the means to compare image quality at different spatial frequency redundancy levels. In this work, we are able to conclude that images formed from thinned arrays using the translated synthetic aperture process are capable of approximating images formed from the corresponding uniform array. In particular, the systems considered in this work have approximately one-third of the active sensors when compared to the uniform array.

In both application areas, the use of thinned arrays offers a reduction in the cost to deploy and maintain a given array system. The feature that makes it possible to overcome the spatial sampling deficiencies of thinned array systems is motion and it is at the core of the performance gains in these applications.