Fast Optimization Algorithm for Evanescent-Mode Cavity Tuner Optimization and Timing Reduction in Software-Defined Radar Implementation

Closing the Loop on Cognitive Radar for Spectrum Sharing

“…The Clutter Modulation performance metric evaluates the phenomenology (Section IV) that arises when sub-CPI waveform adaptations occur, which causes pulse compression sidelobe and mainlobe The CR framework in Fig. 1 also encompasses the use of a high-power tunable matching network [43] to optimize the transmitter front-end power amplifier in terms of power added efficiency (PAE), spectral containment, or output power, the latter of which has been shown to increase the radar's maximum detection range. The goal here is to continually retune this matching network to maintain the optimal RF front-end configuration for the MCR system while the particular DSA parameters are varied.…”

“…This joint time/accuracy consideration for radar poses a trade-off between a highly accurate (and possibly complex) CR technique and its corresponding latency, which is further exacerbated when multiple CR techniques, multiple objectives, and/or other radar signal processing algorithms can be employed. Finally, as adaptive hardware components are used in future CR designs to expand flexibility for transmitted waveforms, the trade-off between hardware tuning speed and fidelity must also be examined [43].…”

“…Finally, tuning of the RF front-end to maximize output power is not currently fast enough to adapt dynamically on CR time-scales. Consequently, the algorithm that controls transmit circuit tuning must adhere to a set of known configurations based on expected CR conditions (see [43]) and therefore presently has no impact on the RT. That said, the prospect of increased tuning speeds may change this arrangement in the future.…”