pith:3Z7FE5CV
Knapsack-based Online Sensor Selection for Vehicle State Estimation
A deficiency-weighted greedy algorithm solves the knapsack problem to pick a low-cost sensor subset that keeps Extended Kalman Filter estimation errors inside chance constraints in real time.
arxiv:2605.16801 v1 · 2026-05-16 · eess.SY · cs.SY
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Claims
The deficiency-weighted greedy algorithm provides an approximate yet efficient solution to the multidimensional minimum knapsack problem that selects sensors while satisfying the chance-constrained error bounds derived from the EKF covariance.
The EKF covariance matrix accurately represents the true probabilistic error bounds under the chosen sensor subset, and the chance constraints remain valid when the selected sensors change at each time step.
A deficiency-weighted greedy algorithm solves a multidimensional minimum knapsack problem to select external sensors online while satisfying EKF-derived chance constraints on state estimation error.
References
Receipt and verification
| First computed | 2026-05-20T00:03:22.954439Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
de7e527455086a5ae22e78ae07a1556c999f9489a1e2fe67598a0a7de9b2e796
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/3Z7FE5CVBBVFVYROPCXAPIKVNS \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: de7e527455086a5ae22e78ae07a1556c999f9489a1e2fe67598a0a7de9b2e796
Canonical record JSON
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