I develop professional, mathematically rigorous state estimation scripts in Python designed for robotics, signal processing, and navigation simulations.
What I Offer:
- Linear Filtering: Standard Kalman Filters (KF) optimized for linear system dynamics and basic sensor denoising.
- Nonlinear Estimation: Extended Kalman Filters (EKF) and Unscented Kalman Filters (UKF) to track highly nonlinear states without divergence.
- Advanced Particle Filters: Sequential Monte Carlo methods for non-Gaussian noise distributions and tracking complex environments.
- Sensor Fusion: Combining conflicting data arrays (e.g., IMU, GPS, encoders) into a single, highly accurate state vector.
- Telemetry Plots: Beautiful Matplotlib curves comparing noisy measurements vs. ground truth vs. filtered estimations.
Why This Gig?
- Explicit Math: Every script features clearly defined covariance matrices ($Q, R, P$) with thorough comments.
- Zero Black Boxes: 100% open, readable, PEP-8 compliant Python source code.
Message me before ordering with your system state equations and noise profiles!