Adaptive sequential refinement: A tractable approach for ambiguity function shaping in cognitive radar

Ambiguity function shaping continues to be one of the most challenging open problems in cognitive radar. Analytically, a complex quartic function should be optimized as a function of the radar waveform code. Practical considerations further require that the waveform be constant modulus, which exacer...

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Bibliographic Details
Main Authors: Aldayel, Omar, Guo, Tiantong, Monga, Vishal, Rangaswamy, Muralidhar
Format: Conference Proceeding
Language:English
Subjects:
Cognitive radar
constant modulus
Cost function
Doppler effect
Indexes
Interference
Radar signal
radar waveform optimization
successive algorithm
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Summary:Ambiguity function shaping continues to be one of the most challenging open problems in cognitive radar. Analytically, a complex quartic function should be optimized as a function of the radar waveform code. Practical considerations further require that the waveform be constant modulus, which exacerbates the issue and leads to a hard non-convex problem. We develop a new approach called Adaptive Sequential Refinement (ASR) to suppress the clutter returns for a desired range-Doppler, i.e. ambiguity function response. ASR solves the aforementioned optimization problem in a unique iterative manner such that the formulation is updated depending on the iteration index. We establish formally that: 1.) the problem in each step of the iteration has a closed form solution, and 2.) monotonic decrease of the cost function until convergence is guaranteed. Experimental validation shows that ASR produces a radar waveform with higher Signal to Interference Ratio (SIR) and superior ambiguity function shaping than state of the art alternatives even as its computational burden is orders of magnitude lower.
ISSN:2576-2303
DOI:10.1109/ACSSC.2017.8335406