Design and Evaluation of Stochastic Processes as Physical Radar Waveforms (Preprint)
Abstract
Recent advances in waveform generation and in computational power have enabled the design and implementation of new complex radarwaveforms. Still despite these advances, in a waveform agile mode where the radar transmits unique waveforms for every pulse or anonrepeating signal continuously, effective operation can be difficult due the waveform design requirements. In general, for radarwaveforms to be both useful and physically robust they must achieve good autocorrelation sidelobes, be spectrally contained, and possess aconstant amplitude envelope for high power operation. Meeting these design goals represents a tremendous computational overhead that can easily impede real-time operation and the overall effectiveness of the radar. This work addresses this concern in the context of random FMwaveforms (RFM) that have been demonstrated in recent years in both simulation and in experiments to achieve low autocorrelation sidelobes through the high dimensionality of coherent integration when operating in a waveform agile mode. However, while they are effective, the approaches to design these waveforms require optimization of each individual waveform, making them subject to costly computational requirements. This dissertation takes a different approach. Since RFM waveforms are meant to be noise like in the first place, the waveforms here are instantiated as the sample functions of an underlying stochastic process called a waveform generating function (WGF). This approach enables the convenient generation of spectrally contained RFM wave-forms for little more computational cost than pulling numbers from a random number generator (RNG).
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 01, 2021
- Accession Number
- AD1136873
Entities
People
- Charles Mohr