Time-varying waveform selection and configuration for agile sensors in tracking applications

We present an algorithm for dynamic waveform selection and configuration for agile sensors in a target tracking application. The method selects and configures generalized frequency-modulated (FM) waveforms with time-varying signatures to minimize the predicted mean squared tracking error. We derive the Cramer-Rao lower bound (CRLB) for these signals and use the CRLB in conjunction with the unscented transform to compute the predicted mean square error. The method is computationally feasible and applicable to nonlinear scenarios as demonstrated in our simulations.

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