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#1003Open强哥 Posted 2026/08/02

$100 prize: explain the EKF Jacobian properly — every partial derived, nothing skipped. Published MIT, in your name

EKF状态估计教学
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Every EKF tutorial I've read derives the prediction and update steps carefully, then
reaches the Jacobian and says "taking the partial derivatives, we get" — followed by a
matrix that appears out of nowhere.

I want the version that doesn't skip. Every partial derivative derived, one step at a
time, with the geometric reason it looks the way it does.

Ideally I can also drag the noise parameters in the browser and watch the covariance
ellipse respond — but the derivation matters more than the demo.

Ship runnable work as a zip; the platform embeds it in a sandbox so anyone can try it live.

Why not just have an LLM write it

I tried. It produces something that looks right and skips exactly the steps I'm stuck on.
I want it from someone who was stuck there too.

Prize and license

  • $100 (¥720) prize, escrowed up front, paid as soon as it's judged.
  • The winning entry is published under the MIT license, in your name. I don't resell it,
    there's no paywall, anyone can read it for free.
  • Copyright stays with you. Put it on your blog, your GitHub, your CV.

This isn't me buying the rights. It's me paying for the thing to exist.

How it's judged

Against the acceptance criteria below, item by item. The criteria were fixed before anyone
entered and will not move. The result and the reasoning are posted publicly — who won,
and which criterion decided it.

Rules

  • No cap on entries. Anyone can enter; entering just registers you and takes nothing
    away from anyone else.
  • Deliver any time before the deadline. Nothing is taken back for working slowly.
  • Reading costs nothing and needs no account. Entering needs an email code — there is no
    signup step.
  • If nothing is good enough, nobody wins and everyone keeps their work, free to publish
    anywhere.

Acceptance criteriaJudged item by item in a dispute. Meet what’s written and payment is owed.

  1. 1.A complete derivation for every entry of the Jacobian — no skipped steps
  2. 2.An interactive demo with adjustable noise parameters
  3. 3.A worked numeric example that matches the simulation
  4. 4.You agree the winning entry ships MIT-licensed under your name (copyright stays yours)

Deliveries0 open to all, no cap

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Discussion0

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