Pith Number

pith:J6JLORGK

pith:2026:J6JLORGKZCGPZK37NESLW4TONF

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Augmented Set-membership Affine Projection Algorithm and Its Performance Analysis

Chen Wang, Haiquan Zhao, Wenjing Luo, Xiaoqiang Long, Xinnian Guo, Yalin Liu

The augmented set-membership affine projection algorithm reduces computational complexity while delivering better performance than the standard augmented affine projection algorithm on colored inputs.

arxiv:2605.18432 v1 · 2026-05-18 · eess.SP

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\pithnumber{J6JLORGKZCGPZK37NESLW4TONF}

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Record completeness

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4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

The augmented set-membership affine projection algorithm (ASM-APA) not only has low computational complexity but also offers improved performance compared with AAPA, with a provided condition for maintaining stability.

C2weakest assumption

That directly incorporating set-membership filtering into the augmented APA structure preserves or enhances convergence behavior while cutting complexity, without introducing new instability or performance loss under the tested conditions.

C3one line summary

The ASM-APA combines set-membership filtering with augmented affine projection to deliver lower computational complexity and improved performance over AAPA for colored signals, supported by complexity and stability analysis.

References

28 extracted · 28 resolved · 0 Pith anchors

[1] A Family of Adaptive V olterra Filters Based on Maximum Correntropy Criterion for Improved Active Control of Impulsive Noise, 2022 · doi:10.1007/s00034-

[2] A New Affine Projection Algorithm with Adaptive l0-norm Constraint for Block-Sparse System Identification, 2023 · doi:10.1007/s00034-022-

[3] Robust Proportionate Normalized Least Mean M -Estimate Algorithm for Block -Sparse System Identification, 2022 · doi:10.1109/tcsii.2021.3082425

[4] Variable Step -size LMS Algorithm Based on Hyperbolic Tangent Function, 2023 · doi:10.1007/s00034-023-02303-8

[5] Lawson -Norm-Based Adaptive Filter for Channel Estimation and In -Car Echo Cancellation, 2022 · doi:10.1109/tcsii.2022.3145569

Formal links

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Receipt and verification

First computed	2026-05-20T00:06:00.672052Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

4f92b744cac88cfcab7f6924bb726e6964a41a587b91e2d2b15ac56f8460dbba

Aliases

arxiv: 2605.18432 · arxiv_version: 2605.18432v1 · doi: 10.48550/arxiv.2605.18432 · pith_short_12: J6JLORGKZCGP · pith_short_16: J6JLORGKZCGPZK37 · pith_short_8: J6JLORGK

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/J6JLORGKZCGPZK37NESLW4TONF \
  | 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: 4f92b744cac88cfcab7f6924bb726e6964a41a587b91e2d2b15ac56f8460dbba

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "a89e1dd4d0086b09d11227f4956a7b554c8f11d0f0878c7552cfdc0cf52a5afc",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "eess.SP",
    "submitted_at": "2026-05-18T14:05:31Z",
    "title_canon_sha256": "d40140e3ff0c94fb8dbef5b2ed40294f21e781d80325c1c64a845513086b486c"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.18432",
    "kind": "arxiv",
    "version": 1
  }
}