Strategic Objectives
• Deconstruct complex systems using recursive Bayesian logic.
• Implement industry-standard Kalman filters for linear tracking.
• Master Particle filters to navigate non-linear, multi-modal environments.
• Reduce sensor noise into actionable, high-precision data.
The Core Challenge
In a world of noisy sensors and unpredictable systems, traditional deterministic models fail to capture the truth of evolving states.
01
The Foundation of Inference
02
The Hidden State
03
Recursive Logic
04
The Markov Property
05
Modeling System Dynamics
06
The Measurement Model
07
Gaussian Noise
08
The Optimal Linear Filter
09
Predict and Correct
10
Handling Non-Linearity
11
The Unscented Transformation
12
Beyond Gaussianity
13
The Particle Filter
14
Importance Sampling
15
Resampling Strategies
16
Grid-Based Methods
17
Sensor Fusion
18
Outlier Rejection
19
The Information Filter
20
Smoothing and Batch Processing
21