A Simplified Approach to Understanding the Kalman Filter Technique

ArXiv ID: ssrn-715301 “View on arXiv”

Authors: Unknown

Abstract

No abstract found

Keywords: No abstract available, Unknown

Complexity vs Empirical Score

• Math Complexity: 6.0/10
• Empirical Rigor: 2.0/10
• Quadrant: Lab Rats
• Why: The paper presents a full derivation of the Kalman Filter algorithm with several mathematical formulas and a section on Maximum Likelihood Estimation, indicating high math complexity. However, the focus is on an Excel tutorial for classroom education, with no backtests, datasets, or statistical metrics, resulting in low empirical rigor.

  flowchart TD
    A["Research Goal: Simplify Kalman Filter Understanding"] --> B["Data/Inputs: System & Measurement Models"]
    B --> C["Methodology: State & Covariance Prediction"]
    C --> D["Computational: Kalman Gain Calculation"]
    D --> E["Methodology: State & Covariance Update"]
    E --> F["Key Findings: Optimal State Estimation Achieved"]