Adaptive-on-transmit using information theoretic measures

Herr, D. B.; Raju, P. S.; Stiles, J. M.

doi:10.1049/icp.2022.2299

Cited by 1 publication

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“…This function could take the form of a complex basis expansion model as in Ref. [31] or another given waveform model. The function f ( t ; α ) maps some N dimensional discrete parameterisation of the waveform to a continuous‐time signal.…”

Section: Mfi Optimisationmentioning

confidence: 99%

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Information theoretic waveform design with applications to adaptive‐on‐transmit radar

Herr,

Raju,

Stiles

2023

IET Radar Sonar & Navi

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The marginal Fisher information (MFI) metric is used to design waveforms for the sake of informationally optimal adaptive‐on‐transmit radar operation. A framework for MFI waveform design is developed and the Polyphase‐Coded FM (PCFM) waveform model is utilised to produce a constant‐modulus, spectrally contained signal amenable to transmission with high‐power amplifiers. The efficacy of the MFI waveform design and minimum mean square error (MMSE) estimation is experimentally demonstrated and extended into an adaptive and dynamic sensing paradigm. The radar transmit waveform is optimised to maximise the Fisher information with respect to the range profile. Upon observing new information from radar echoes, the iterative MMSE (iMMSE) estimator then minimises the estimation error variance according to prior observations. Sequential information maximisation (via waveform design) and error minimisation (via iMMSE) tends towards the Cramér–Rao lower bound (CRLB) with additional measurements improving radar resolution and accuracy. These concepts maximise the information extracted by a radar operating in a congested spectrum where the available bandwidth is limited.

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Section: Mfi Optimisationmentioning

confidence: 99%

“…The transmit signals are composed of MFI codes designed using a strictly band‐limited phase‐code waveform model as in Ref. [31]. Each signal has a bandwidth of 50 MHz and pulse duration of 500 ns yielding a time‐bandwidth products of 25.…”

Section: Optimal Estimatorsmentioning

confidence: 99%