pith:5PXOJLYS
Power-Aware Cognitive Radar Multi-target Tracking Under Unknown Disturbances
Independent POMCP trees per target enable adaptive power allocation that raises low-SNR detection from 0.6 to nearly 0.9 in cognitive massive MIMO radar.
arxiv:2507.17506 v4 · 2025-07-23 · eess.SP · cs.LG
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Claims
Results confirm that the proposed POMCP method improves the detection probability for low-SNR targets from 0.6 to nearly 0.9, and yields more accurate tracking of the weakest target than a non-adaptive orthogonal waveform or a cognitive uniform-power POMCP baseline.
The framework assumes that independent POMCP trees can produce sufficiently accurate state predictions under unknown disturbances to drive a constrained optimization that reliably reallocates power without violating total-energy or interference limits.
POMCP-based adaptive waveform design in cognitive massive MIMO radar raises low-SNR target detection probability from 0.6 to nearly 0.9 and improves weakest-target tracking over non-adaptive and uniform-power baselines.
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Receipt and verification
| First computed | 2026-06-03T01:05:44.421069Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
ebeee4af12c4e1eced284a3480eeda72eed54ece693ff5aa11ce48c0b1cc63e0
Aliases
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/5PXOJLYSYTQ6Z3JIJI2IB3W2OL \
| 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: ebeee4af12c4e1eced284a3480eeda72eed54ece693ff5aa11ce48c0b1cc63e0
Canonical record JSON
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