This paper considers sequential detection and classification of multiple transient signals from vector observations corrupted with additive noise and multiple types of structured interference. Sparse approximations of observations are found to facilitate computation of the likelihood of each signal model without relying on restrictive assumptions concerning the distribution of observations. Robustness to interference may be incorporated by virtue of the inherent separation capabilities of sparse coding. Each signal model is characterized by a Bayesian Network, which captures the temporal dependency structure among coefficients in successive sparse approximations under the associated hypothesis. Generalized likelihood ratios tests may then be used to perform signal detection and classification during quiescent periods, and quiescent detection whenever a signal is present. The results of applying the proposed method to a national park soundscape analysis problem demonstrate its practical utility for detecting and classifying real acoustical sources present in complex sonic environments.

This paper introduces a new framework for detecting, classifying, and estimating the signatures of multiple transient acoustical sources from a time-transform representation (TTR) of an audio waveform. A TTR is a vector observation sequence containing the coefficients of consecutive windows of data with respect to known sampled basis waveforms. A set of likelihood ratio tests is hierarchically applied to each time slice of a TTR to detect and classify signals in the presence of interference. Since the signatures of each acoustical event typically span several adjacent dependent observations, a Kalman filter is used to generate the parameters necessary for computing the likelihood values. The experimental results of applying the proposed method to a problem of detecting and classifying man-made and natural transient acoustical events in national park soundscape recordings attest to its effectiveness at performing the aforementioned tasks.

This paper introduces a method for in-situ underwater target classification, based on an image retrieval system, that can be implemented using a simple two-layer kernel-based network. This system incorporates a learning mechanism that captures new information for discriminating between objects in different classes or within the same class from a set of input-output pairs with associated confidence scores. A strategy to select the most informative patterns for optimal parameter adaptation during in-situ learning is also described. The system is then tested on a database of synthetically generated sonar images. The ability of the system to correctly classify images containing objects in different environmental and operating conditions than those used for original training, as well as its ability to incorporate new object types without perturbing the classification performance on other object types are demonstrated.

This paper presents a new method for multiaspect/ping classification of underwater objects using sonar data. This system uses decision feedback to form a likelihood ratio for making a high confidence final decision based not only upon the data of the current ping but also the final decisions made at several previous pings. The system is then applied to an underwater target classification problem. Test results on a buried object scanning sonar (BOSS) database collected for different objects and in different conditions show the promise of the proposed method for multi-aspect underwater target discrimination.

This paper uses the canonical correlation decomposition (CCD) framework to investigate the spatial correlation of sources captured using two spatially separated sensor arrays. The relationship between the canonical correlations of the observed signals and the spatial correlation coefficients of the source signals are first derived, including an analysis of the changes seen in this relationship under certain noise level and array geometry assumptions. Additionally, simulation results are presented that demonstrate the effects of different noise levels and array geometries on the canonical correlations for the case of two uniform linear sparse arrays.

In this paper, a new collaborative multi-aspect classification system (CMAC) is introduced. CMAC utilizes a group of collaborative decision-making agents capable of producing a high-confidence final decision based on features obtained over multiple aspects. This system is then applied to a buried underwater target classification problem. The results show that CMAC provides excellent multi-ping classification of mine-like objects while simultaneously reducing the number of false alarms compared to a multi-ping decision-level fusion classifier.