Start by clarifying the embedded constraints (MCU, memory, real-time deadlines) and the filter's state dimension, then propose a data-oriented design with fixed-size arrays and no dynamic allocation. Walk through the implementation in Rust, emphasizing zero-cost abstractions, predictable memory layout, and rigorous testing. Conclude by discussing trade-offs between numerical stability, performance, and code maintainability.
Pro tip: Mention that you would use const generics to parameterize the state dimension at compile time, enabling stack-allocated arrays and eliminating runtime bounds checks—a detail that shows deep Rust and embedded expertise.
Ask about the target MCU (e.g., Cortex-M), available memory, real-time deadlines, and the state vector dimension. Confirm whether floating-point hardware is available or if fixed-point arithmetic is needed.
Propose using fixed-size arrays (e.g., [f32; N]) and const generics to represent matrices and vectors, ensuring all data is stack-allocated and cache-friendly. Avoid heap allocation and dynamic dispatch.
Outline the predict and update steps using in-place operations and iterators to minimize allocations. Use Rust's ownership and borrowing to enforce safety without runtime overhead.
Discuss loop unrolling, SIMD (if available), and avoiding floating-point operations where possible. Consider using nalgebra or a custom linear algebra module tailored to the state size.
Describe unit tests with known inputs, property-based testing for numerical stability, and static analysis (clippy, miri). Mention documentation and modular design for maintainability.
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