Hardware complexity for Extrinsic Fabry-Perot Interferometer sensor processing

A number of Extrinsic Fabry-Perot Interferometer processing techniques have been demonstrated for use to extract gauge-length measurements from optical detector output signals. These include: (1) an artificial Neural Network method, (2) a direct phase synthesid method, and (3) an iterative search method. For applications where the processing is to be performed with low-power hardware, co-located with the sensor, the hardware implementation architecture and complexity become critical for a practical solution. In this paper, implementation complexity tradeoffs and comparisons are given for various implementation architectures for each method with respect to each gauge-length estimate. Our research considers complexity as measured in terms of the number of hardware-resident arithmetic operators, the total number of arithmetic operations performed, and the data memory size. It is shown that accurate gauge-length estimates are achievable with implementation architectures suitable for applications including low-power implementations and scalable implementations.