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Abstract - SSAP3 |
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SSAP3.1 |
Processing of Experimental Seismic Array Data Using 2-D Wideband Interpolated Root-MUSIC D. Sidorovich, A. Gershman, J. Bohme (Ruhr University of Bochum, Germany) 2-D elementspace and beamspace extensions of Friedlander's wideband interpolated root-MUSIC are applied to azimuth-velocity source location using real seismic data from the GERESS array (Germany). We demonstrate that 2-D interpolated root-MUSIC is able to estimate the parameters of a typical seismic source with a good accuracy. The use of interpolated root-MUSIC and its beamspace modification is motivated by the significant reduction of processing time allowing on-line implementation. |
| SSAP3.2 |
Nonstationary Array Signal Detection Using Time-Frequency and Time-Scale Representations A. Rao, D. Jones (University of Illinois, Urbana-Champaign, USA) Quadratic time-frequency representations (TFRs) and time-scale representations (TSRs) have been shown to be very useful for detecting nonstationary signals in the presence of nonstationary noise. The theory developed thus far is only for the single observation case; however, in many situations involving signal detection, there are advantages in using an array of receiving sensors. Sensor arrays allow for target or source localization and can provide a largegain in the SNR. We show that time-frequency and time-scale representations provide a natural structure for the detection of a large class of nonstationary signals in the presence of nonstationary noise using an array of sensors. That is, time-frequency and time-scale provide a detection structure that is both optimal and allows for efficient implementation. In developing the TFR/TSR-based optimal quadratic array processor, we consider several types of array environments including those with full, partial, and no coherence. |
| SSAP3.3 |
The Source Number Estimation Based on Gerschgorin Radii O. Caspary, P. Nus, T. Cecchin (CRAN-CNRS, France) The GDE criterion is based on the estimation of the Gerschgorin disks’ radii where the disks are separated in two distinct sets : one associated to the signal sources, the other related to the noise. We aim at modifying that criterion into a new one called SGDE by using the sum of the disks’ radii. Besides, the SGDE criterion is modified with a simple deflation on the sum of the Gerschgorin radii to obtain a better estimation with sources of different power. We also suggest applying a deflation method to the covariance matrix before using the criteria based on the Gerschgorin radii. The transformed Gerschgorin radii can be connected to the Least-Squares through the transformed cross-correlation vector. So, two new criteria are put forward on the same principle as the SGDE criterion. These criteria can be applied in many situations : coloured or white noise, sources of different power. |
| SSAP3.4 |
Array Processing in Non-Gaussian Noise with the EM Algorithm R. Kozick (Bucknell University, USA); B. Sadler (Army Research Lab, USA); R. Blum (Lehigh University, USA) A central problem in sensor array processing is the localization of multiple sources and the reception of the signals emitted by those sources.Many approaches have been studied for this problem when the additive noise in the sensor array data is modeled with a Gaussian distribution. However, the schemes designed for Gaussian noise typically perform very poorly when the noise is non-Gaussian. An algorithm is presented in this paper for array processing in non-Gaussian noise. The algorithm is based on modeling the noise with a Gaussian mixture distribution. The expectation-maximization (EM) algorithm is then used to derive an iterative processing structure that estimates the source locations, estimates the source waveforms, and adapts the processing to match the characteristics of the noise. Simulation examples are presented to illustrate the performance of the algorithm. |
| SSAP3.5 |
Multistage Cancellation of Terrain Scattered Jamming and Conventional Clutter D. Rabideau (MIT Lincoln Lab, USA) This paper addresses the problem of adaptively canceling both conventional clutter and terrain-scattered jamming (TSJ) in airborne radar systems. Existing algorithms for this type of interference adapt first in space/fast-time to cancel the TSJ, then in space/slow-time to cancel the conventional clutter. Unfortunately, the rapid weight updating required to cancel the nonstationary TSJ will modulate the clutter and targets, making the cancellation of conventional clutter extremely difficult and reducing the accuracy of the reported target locations. This paper proposes a multi-stage beamformer that prevents modulated clutter from degrading cancellation performance. The processor is formulated and its properties are described. The application of this beamformer to site-specific simulated data sets is used to illustrate its performance. |
| SSAP3.6 |
Analysis of an Adaptive Detection Algorithm for Non-Homogeneous Environments C. Richmond (MIT Lincoln Lab, USA) The adaptive matched filter (AMF) detector is known to be highly vulnerable to jammers and clutter discretes on which it has not trained. A vulnerability often leading to impractical false alarm rates in non-homogeneous environments. Sequentially following the AMF test with the adaptive cosine estimator (ACE) detector was proposed as a method of regulating the AMF's high false alarm rate. The overall detection algorithm, called the adaptive sidelobe blanker (ASB), is two dimensional and has exhibited significant potential in experimental settings of inhomogeneous environments. The goal of this paper is to theoretically examine the potential of this algorithm for application in non-homogeneous environments. |
| SSAP3.7 |
Error Reduction of Range Estimates in Multipath Environments M. Richman, T. Parks (Cornell University, USA); C. Kimball (Schlumberger, USA) A reduction in error is obtained for estimates of range computed in a multipath environment. A good estimation technique for this problem typically involves exploiting the multipath interference, that is, analyzing each received signal reflection to improve the estimate. Under certain conditions, however, the errors in multipath estimates can be quite large. We compute the Cram\'{e}r-Rao bounds for an example satisfying these conditions to demonstrate this phenomenon. We propose a modification to the original signal model which better represents the received signals. While the modified model does introduce a bias error, we provide a suitable estimate of the bias error so that a complete error anaylsis of the modified model is possible. The total error (noise error plus bias error) for the modified model is computed for an example and compares favorably with the error obtained via the original signal model. |
| SSAP3.8 |
Design of Chemical Sensor Arrays for Monitoring Disposal Sites on the Ocean Floor A. Jeremic, A. Nehorai (University of Illinois, Chicago, USA) We develop detection methods for automatic environmental monitoring of disposal sites on the deep ocean floor using chemical sensor arrays. Such sites have been proposed for the relocation of dredge materials from harbors and shipping channels; the monitoring is used to detect possible release of pollutants at the site. We model the underwater transport of the pollutants as a diffusion process, and obtain a measurement model by exploiting the spatial and temporal evolution of the associated concentration distribution. The detection problem is defined by a one side hypothesis test for the case of multiple sources. We derive two detectors, the generalized likelihood ratio (GLR) test and the mean detector, and determine their performance in terms of the probabilities of false alarm and detection. The results are applied to the design of chemical sensor arrays satisfying criteria specified in terms of these probabilities, and to optimally select numbers of sensors and time samples. Numerical examples are used to demonstrate the applicability of our results. |
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