A Hands-On Guide to kalbee: Your First Kalman Filter (and Beyond)

Everything you need to go from pip install to a working multi-object tracker, one runnable snippet at a time. kalbee is a Python library for state estimation — the art of recovering a clean signal (position, velocity, temperature, whatever you're measuring) from noisy sensor data. This guide walks through it from the ground up. Every code block runs as-is; copy them into a file and follow along. Install pip install kalbee The only runtime dependencies are NumPy and SciPy. Optional extras add object-detection ( pip install "kalbee[yolo]" ) and plotting ( pip install "kalbee[viz]" ) support. The one idea you need: predict and update Every filter in kalbee works the same way. You alternate between two steps: predict() — advance the state forward in time using a motion model ("where do I think the object is now?"). update(z) — correct that prediction with a new measurement z ("what does the sensor actually say?"). The filter tracks two things: the state x (your best estimate) and the covariance P (how uncertain that estimate is). You read them back via kf.x and kf.P . Your first filter Let's track an object moving at roughly constant velocity, measuring only its (noisy) position. Instead of hand-building matrices, we use kalbee's ready-made models : import numpy as np from kalbee import KalmanFilter , rmse from kalbee.models import constant_velocity , position_measurement_model dt = 1.0 # Motion model: state is [position, velocity] F , Q = constant_velocity ( dt = dt , process_var = 0.01 , n_dims = 1 ) # Measurement model: we observe position only, with noise variance 4.0 H , R = position_measurement_model ( order = 1 , n_dims = 1 , measurement_var = 4.0 ) # Simulate a noisy trajectory rng = np . random . default_rng ( 0 ) pos , vel = 0.0 , 1.0 truths , measurements = [], [] for _ in range ( 50 ): pos += vel * dt truths . append ( pos ) measurements . append ( pos + rng . standard_normal () * 2.0 ) # std 2.0 -> var 4.0 # Create the filter: start at zero with high uncert