Phased array system trade studies
- Python
- NumPy
- SciPy
- pandas
- pydantic
Problem
A phased-array design has to satisfy more than a clean beam pattern. Power, thermal, link budget, radar detection, RF-chain noise, cost, and reliability all trade against each other, and a design that looks good on a polar plot can still miss its system requirements.
Approach
phased-array-systems is a requirements-driven Python framework that sits on top of the
pattern model. It treats requirements as first-class
objects, with pass/fail and margins, samples a design space with a Latin-hypercube DOE, and
batch-evaluates designs against communications link budgets, the monostatic radar equation, and an
RF cascade (Friis noise figure, IIP3, dynamic range). It then filters feasible designs, extracts and
ranks Pareto frontiers, and models reliability (T/R module MTBF, graceful degradation) and
cost/power.
Result
The framework turns a single phased-array design into a trade space you can defend: feasible against infeasible designs, Pareto frontiers of cost against EIRP or detection range, and traceable margins. It pairs with the Phased Array Antenna Model, and I wrote up how the two fit together in a blog post. The code is on GitHub.