Adaptive detection in Gaussian interference with unknown covariance after reduction by invariance
IEEE Transactions on Signal Processing
Brief paper: Distributed fault detection for interconnected second-order systems
Automatica (Journal of IFAC)
CFAR detection of multidimensional signals: an invariant approach
IEEE Transactions on Signal Processing
Rao Test for Adaptive Detection in Gaussian Interference With Unknown Covariance Matrix
IEEE Transactions on Signal Processing - Part II
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Comparison of GLR and invariant detectors under structured clutter covariance
IEEE Transactions on Image Processing
Active actuator fault detection and diagnostics in HVAC systems
BuildSys '12 Proceedings of the Fourth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings
Distributed model-invariant detection of unknown inputs in networked systems
Proceedings of the 2nd ACM international conference on High confidence networked systems
Distributed model-invariant detection of unknown inputs in networked systems
Proceedings of the 2nd ACM international conference on High confidence networked systems
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This work considers hypothesis testing in networked systems under severe lack of prior knowledge. In previous work we derived a centralized Uniformly Most Powerful Invariant (UMPI) approach to testing unknown inputs in unknown Linear Time Invariant (LTI) networked dynamics subject to unknown Gaussian noise. The detector was also shown to have Constant False Alarm Rate (CFAR) properties. Nonetheless, in large-scale systems, centralized testing may be infeasible or undesirable. Thus, we develop a distributed testing version of our previous work that utilizes a statistic that is maximally invariant to the unknown parameters and the nonlocal/neighboring measurements. Similar to the centralized approach, the distributed test is shown to have CFAR properties and to have performance that asymptotically approaches that of the centralized test. Simulation results illustrate that the performance of the distributed approach suffers marginal performance degradation in comparison to the centralized approach. Insight to this phenomena is provided through a discussion.