Narayan Kovvali

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Probability hypothesis density (PHD) filtering, implemented using particle filters, is a Bayesian technique used to non-linearly track multiple objects. In this paper, we propose a new approach based on PHD particle filters (PHD-PF) to automatically track the number of magnetoencephalography (MEG) neural dipole sources and their unknown states. In(More)
The effective and reliable detection of explosive compounds in complex environments is an important problem in many environment and security-related applications. This paper develops an explosive detection approach based on multi-modal sensing and sensor data fusion. A least-squares feature extraction technique is designed to isolate explosive signatures in(More)
As metallic aircraft components are subject to a variety of in-service loading conditions, predicting their fatigue life has become a critical challenge. To address the failure mode mitigation of aircraft components and at the same time reduce the life cycle costs of aerospace systems, a reliable prognostics framework is essential. In this paper a hybrid(More)
In this paper, we consider the tracking of a radar target with unknown range and range rate at low signal-to-noise ratio (SNR). For this nonlinear estimation problem, the Cramér-Rao lower bound (CRLB) provides a bound on an unbiased estimator's mean-squared error (MSE). However, there exists a threshold SNR at which the es-timator variance deviates from the(More)