Semi-definite programming for distributed tracking of dynamic objects by nonlinear sensor network

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Conference Proceeding
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2011, pp. 3532 - 3535
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This paper discusses dynamic state estimation for nonlinear measurement model through distributed multisensor network under power constraints. For this scenario, we propose an optimized power allocation strategy based on semidefinite programming, that achieves minimum mean-squared error for the estimate subject to constraints on total transmit power. System nonlinearity is handled effectively with the help of distributed unscented Kalman filtering and linear fractional transformation. Furthermore, advantage of using multiple sensors over a single independent sensor is established through simulation results for tracking a maneuvering target. © 2011 IEEE.
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