Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The reiterative super-resolution (RISR) algorithm was previously developed to enable adaptive beamforming with as few as one time snapshot, is robust to temporally correlated signals, and accounts for array calibration errors. Here a gain-constrained version (denoted GC-RISR) is derived follod by a partially-constrained version (PC-RISR). It is shown that an interesting trait of the latter is spatial super-resolution at SNR values lor than is typical for adaptive beamforming techniques as a trade-off for requiring more iterations to converge. The PCRISR formulation is controlled by a selectable parameter that serves a role similar to that of an adaptive step-size which balances beten convergence speed and accuracy.
['Erik Hornberger', 'Shannon D. Blunt', 'Thomas Higgins']
I am writing a research paper on a idea Partially constrained adaptive beamforming for super-resolution at Low SNR
[{"from": "human", "value": "I am writing a research paper on a idea Partially constrained adaptive beamforming for super-resolution at Low SNR"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The reiterative super-resolution (RISR) algorithm was previously developed to enable adaptive beamforming with as few as one time snapshot, is robust to temporally correlated signals, and accounts for array calibration errors. Here a gain-constrained version (denoted GC-RISR) is derived follod by a partially-constrained version (PC-RISR). It is shown that an interesting trait of the latter is spatial super-resolution at SNR values lor than is typical for adaptive beamforming techniques as a trade-off for requiring more iterations to converge. The PCRISR formulation is controlled by a selectable parameter that serves a role similar to that of an adaptive step-size which balances beten convergence speed and accuracy."}]