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1. Shard Calculation

Query:

Response:

Calculated Shard: 61 (from laksa199)

2. Crawled Status Check

Query:

curl -X POST \
  'http://laksa061.int.ahrefs:8124/' \
  -H 'Content-Type: text/plain' \
  -H 'X-ClickHouse-Database: crawler3' \
  -H 'Authorization: Basic YXBpOg==' \
  -d 'SELECT getAhrefsURLFromUnparsed(src_unparsed) AS found_url, ifNull(toUnixTimestamp(download_stamp), 0) AS crawl_time, ifNull(toUnixTimestamp(props_url_first_seen), 0) AS first_indexed_time, download_http_code AS http_code, src_unparsed AS src_unparsed, src_root_hash AS src_root_hash, history_drop_reason AS history_drop_reason, meta_title AS meta_title, meta_descriptions AS meta_descriptions, attrs_boilerpipe_text AS attrs_boilerpipe_text, attrs_markdown AS attrs_markdown, attrs_readable_markdown AS attrs_readable_markdown, meta_canonical AS meta_canonical FROM crawler3.page_info_local FINAL PREWHERE (src_root_hash, src_unparsed) IN ((getAhrefsRootHashFromUnparsed(getAhrefsUnparsedNoserviceFromURL(\'https://papers.cool/arxiv/2603.05154\')), getAhrefsUnparsedNoserviceFromURL(\'https://papers.cool/arxiv/2603.05154\'))) FORMAT JSONEachRow'

Response:

{"found_url":"https:\/\/papers.cool\/arxiv\/2603.05154","crawl_time":1775301195,"first_indexed_time":1772864657,"http_code":200,"src_unparsed":"cool,papers!\/arxiv\/2603.05154 s443","src_root_hash":"17309916099783778261","history_drop_reason":null,"meta_title":"Revitalizing AR Process Simulation of Non-Gaussian Radar Clutter via Series-Based Analytic Continuation | Cool Papers - Immersive Paper Discovery","meta_descriptions":["Due to the conceptual simplicity, the linear filtering framework, notably the autoregressive (AR) process, has a long history in simulating clutter sequences with specified probability density functions (PDFs) and autocorrelation functions (ACFs). However, linear filtering inevitably distorts the input distribution, which may lead to inaccurate PDF reproduction or restrict applicability to very simple ACFs. To address these challenges, this study proposes a series-based analytic continuation strategy that revitalizes AR process clutter simulation by accurately precomputing the input pre-distortion required to compensate for AR filtering. First, the moments and cumulants of the AR input are derived based on the input-output relationship of the AR process, facilitating the moment and cumulant expansions of the Laplace transform (LT) and the logarithmic LT around zero, respectively. Second, both series expansions are analytically continued via the Padé approximation (PA) to recover the LT over the full complex plane. Notably, the PA-based continuation of the moment expansion, a conventional choice, can be highly inaccurate when the LT exhibits strong oscillations. By contrast, given the logarithmic LT generally has a simpler structure, the continuation of the cumulant expansion provides a more stable and accurate alternative. Third, the LT recovered from the cumulant expansion facilitates fast simulation of the AR input non-Gaussian white sequence via a random variable transformation method, thereby enabling an efficient AR process. Finally, simulations demonstrate that the proposed strategy enables accurate and fast simulation of non-Gaussian correlated clutter sequences."],"attrs_boilerpipe_text":"Due to the conceptual simplicity, the linear filtering framework, notably the autoregressive (AR) process, has a long history in simulating clutter sequences with specified probability density functions (PDFs) and autocorrelation functions (ACFs). However, linear filtering inevitably distorts the input distribution, which may lead to inaccurate PDF reproduction or restrict applicability to very simple ACFs. To address these challenges, this study proposes a series-based analytic continuation strategy that revitalizes AR process clutter simulation by accurately precomputing the input pre-distortion required to compensate for AR filtering. First, the moments and cumulants of the AR input are derived based on the input-output relationship of the AR process, facilitating the moment and cumulant expansions of the Laplace transform (LT) and the logarithmic LT around zero, respectively. Second, both series expansions are analytically continued via the Padé approximation (PA) to recover the LT over the full complex plane. Notably, the PA-based continuation of the moment expansion, a conventional choice, can be highly inaccurate when the LT exhibits strong oscillations. By contrast, given the logarithmic LT generally has a simpler structure, the continuation of the cumulant expansion provides a more stable and accurate alternative. Third, the LT recovered from the cumulant expansion facilitates fast simulation of the AR input non-Gaussian white sequence via a random variable transformation method, thereby enabling an efficient AR process. Finally, simulations demonstrate that the proposed strategy enables accurate and fast simulation of non-Gaussian correlated clutter sequences.\nSubjects\n:\nSignal Processing\n,\nApplications\nPublish\n:\n2026-03-05 13:26:03 UTC","attrs_markdown":"# 2603\\.05154\nTotal: 1\n\n## [\\#1](https:\/\/arxiv.org\/abs\/2603.05154 \"1\/1\") [Revitalizing AR Process Simulation of Non-Gaussian Radar Clutter via Series-Based Analytic Continuation](https:\/\/papers.cool\/arxiv\/2603.05154) [\\[PDF\\]]() [\\[Copy\\]]() [\\[Kimi\\]]() [\\[REL\\]]()\n**Authors**: [Xingxing Liao](https:\/\/arxiv.org\/search\/?searchtype=author&query=Xingxing%20Liao), [Junhao Xie](https:\/\/arxiv.org\/search\/?searchtype=author&query=Junhao%20Xie)\n\nDue to the conceptual simplicity, the linear filtering framework, notably the autoregressive (AR) process, has a long history in simulating clutter sequences with specified probability density functions (PDFs) and autocorrelation functions (ACFs). However, linear filtering inevitably distorts the input distribution, which may lead to inaccurate PDF reproduction or restrict applicability to very simple ACFs. To address these challenges, this study proposes a series-based analytic continuation strategy that revitalizes AR process clutter simulation by accurately precomputing the input pre-distortion required to compensate for AR filtering. First, the moments and cumulants of the AR input are derived based on the input-output relationship of the AR process, facilitating the moment and cumulant expansions of the Laplace transform (LT) and the logarithmic LT around zero, respectively. Second, both series expansions are analytically continued via the Padé approximation (PA) to recover the LT over the full complex plane. Notably, the PA-based continuation of the moment expansion, a conventional choice, can be highly inaccurate when the LT exhibits strong oscillations. By contrast, given the logarithmic LT generally has a simpler structure, the continuation of the cumulant expansion provides a more stable and accurate alternative. Third, the LT recovered from the cumulant expansion facilitates fast simulation of the AR input non-Gaussian white sequence via a random variable transformation method, thereby enabling an efficient AR process. Finally, simulations demonstrate that the proposed strategy enables accurate and fast simulation of non-Gaussian correlated clutter sequences.\n\n**Subjects**:  [Signal Processing](https:\/\/papers.cool\/arxiv\/eess.SP)   ,  [Applications](https:\/\/papers.cool\/arxiv\/stat.AP)\n\n**Publish**: 2026-03-05 13:26:03 UTC\n***\n\nDesigned by [kexue.fm](https:\/\/kexue.fm\/) \\| Powered by [kimi.ai](https:\/\/kimi.moonshot.cn\/?ref=papers.cool)\n\nInclude([OR](\"The logical relationship between keywords (OR\/AND)\")):\n\nExclude:\n\nSearch\n\nFilter\n\nHighlight\n\nStared Paper(s):\n\n\\#1 [Revitalizing AR Process Simulation of Non-Gaussian Radar Clutter via Series-Based Analytic Continuation](https:\/\/papers.cool\/arxiv\/2603.05154#2603.05154)\n\nExport\n\nMagic Token:\n\nKimi Language:\n\nDesc Language:\n\nSave\n\nBug report? Issue submit? Please visit:\n\n**Github:** <https:\/\/github.com\/bojone\/papers.cool>\n\nPlease read our [Disclaimer](https:\/\/github.com\/bojone\/papers.cool\/blob\/main\/Disclaimer\/README_en.md) before proceeding.\n\nFor more interesting features, please visit [kexue.fm](https:\/\/kexue.fm\/) and [kimi.ai](https:\/\/kimi.moonshot.cn\/?ref=papers.cool).","attrs_readable_markdown":"Due to the conceptual simplicity, the linear filtering framework, notably the autoregressive (AR) process, has a long history in simulating clutter sequences with specified probability density functions (PDFs) and autocorrelation functions (ACFs). However, linear filtering inevitably distorts the input distribution, which may lead to inaccurate PDF reproduction or restrict applicability to very simple ACFs. To address these challenges, this study proposes a series-based analytic continuation strategy that revitalizes AR process clutter simulation by accurately precomputing the input pre-distortion required to compensate for AR filtering. First, the moments and cumulants of the AR input are derived based on the input-output relationship of the AR process, facilitating the moment and cumulant expansions of the Laplace transform (LT) and the logarithmic LT around zero, respectively. Second, both series expansions are analytically continued via the Padé approximation (PA) to recover the LT over the full complex plane. Notably, the PA-based continuation of the moment expansion, a conventional choice, can be highly inaccurate when the LT exhibits strong oscillations. By contrast, given the logarithmic LT generally has a simpler structure, the continuation of the cumulant expansion provides a more stable and accurate alternative. Third, the LT recovered from the cumulant expansion facilitates fast simulation of the AR input non-Gaussian white sequence via a random variable transformation method, thereby enabling an efficient AR process. Finally, simulations demonstrate that the proposed strategy enables accurate and fast simulation of non-Gaussian correlated clutter sequences.\n\n**Subjects**:  [Signal Processing](https:\/\/papers.cool\/arxiv\/eess.SP)   ,  [Applications](https:\/\/papers.cool\/arxiv\/stat.AP)\n\n**Publish**: 2026-03-05 13:26:03 UTC\n***","meta_canonical":null}

3. Robots.txt Check

Query:

Response:

4. Spam/Ban Check

Query:

Response:

5. Seen Status Check

ℹ️ Skipped - page is already crawled

📄

INDEXABLE

✅

CRAWLED

8 days ago

🤖

ROBOTS ALLOWED

Page Info Filters

Filter	Status	Condition	Details
HTTP status	PASS	`download_http_code = 200`	HTTP 200
Age cutoff	PASS	`download_stamp > now() - 6 MONTH`	0.3 months ago
History drop	PASS	`isNull(history_drop_reason)`	No drop reason
Spam/ban	PASS	`fh_dont_index != 1 AND ml_spam_score = 0`	ml_spam_score=0
Canonical	PASS	`meta_canonical IS NULL OR = '' OR = src_unparsed`	Not set

Page Details

Property	Value
URL	https://papers.cool/arxiv/2603.05154
Last Crawled	2026-04-04 11:13:15 (8 days ago)
First Indexed	2026-03-07 06:24:17 (1 month ago)
HTTP Status Code	200
Meta Title	Revitalizing AR Process Simulation of Non-Gaussian Radar Clutter via Series-Based Analytic Continuation \| Cool Papers - Immersive Paper Discovery
Meta Description	Due to the conceptual simplicity, the linear filtering framework, notably the autoregressive (AR) process, has a long history in simulating clutter sequences with specified probability density functions (PDFs) and autocorrelation functions (ACFs). However, linear filtering inevitably distorts the input distribution, which may lead to inaccurate PDF reproduction or restrict applicability to very simple ACFs. To address these challenges, this study proposes a series-based analytic continuation strategy that revitalizes AR process clutter simulation by accurately precomputing the input pre-distortion required to compensate for AR filtering. First, the moments and cumulants of the AR input are derived based on the input-output relationship of the AR process, facilitating the moment and cumulant expansions of the Laplace transform (LT) and the logarithmic LT around zero, respectively. Second, both series expansions are analytically continued via the Padé approximation (PA) to recover the LT over the full complex plane. Notably, the PA-based continuation of the moment expansion, a conventional choice, can be highly inaccurate when the LT exhibits strong oscillations. By contrast, given the logarithmic LT generally has a simpler structure, the continuation of the cumulant expansion provides a more stable and accurate alternative. Third, the LT recovered from the cumulant expansion facilitates fast simulation of the AR input non-Gaussian white sequence via a random variable transformation method, thereby enabling an efficient AR process. Finally, simulations demonstrate that the proposed strategy enables accurate and fast simulation of non-Gaussian correlated clutter sequences.
Meta Canonical	null
Boilerpipe Text	Due to the conceptual simplicity, the linear filtering framework, notably the autoregressive (AR) process, has a long history in simulating clutter sequences with specified probability density functions (PDFs) and autocorrelation functions (ACFs). However, linear filtering inevitably distorts the input distribution, which may lead to inaccurate PDF reproduction or restrict applicability to very simple ACFs. To address these challenges, this study proposes a series-based analytic continuation strategy that revitalizes AR process clutter simulation by accurately precomputing the input pre-distortion required to compensate for AR filtering. First, the moments and cumulants of the AR input are derived based on the input-output relationship of the AR process, facilitating the moment and cumulant expansions of the Laplace transform (LT) and the logarithmic LT around zero, respectively. Second, both series expansions are analytically continued via the Padé approximation (PA) to recover the LT over the full complex plane. Notably, the PA-based continuation of the moment expansion, a conventional choice, can be highly inaccurate when the LT exhibits strong oscillations. By contrast, given the logarithmic LT generally has a simpler structure, the continuation of the cumulant expansion provides a more stable and accurate alternative. Third, the LT recovered from the cumulant expansion facilitates fast simulation of the AR input non-Gaussian white sequence via a random variable transformation method, thereby enabling an efficient AR process. Finally, simulations demonstrate that the proposed strategy enables accurate and fast simulation of non-Gaussian correlated clutter sequences. Subjects : Signal Processing , Applications Publish : 2026-03-05 13:26:03 UTC
Markdown	# 2603\.05154 Total: 1 ## [\#1](https://arxiv.org/abs/2603.05154 "1/1") [Revitalizing AR Process Simulation of Non-Gaussian Radar Clutter via Series-Based Analytic Continuation](https://papers.cool/arxiv/2603.05154) [\[PDF\]]() [\[Copy\]]() [\[Kimi\]]() [\[REL\]]() Authors: [Xingxing Liao](https://arxiv.org/search/?searchtype=author&query=Xingxing%20Liao), [Junhao Xie](https://arxiv.org/search/?searchtype=author&query=Junhao%20Xie) Due to the conceptual simplicity, the linear filtering framework, notably the autoregressive (AR) process, has a long history in simulating clutter sequences with specified probability density functions (PDFs) and autocorrelation functions (ACFs). However, linear filtering inevitably distorts the input distribution, which may lead to inaccurate PDF reproduction or restrict applicability to very simple ACFs. To address these challenges, this study proposes a series-based analytic continuation strategy that revitalizes AR process clutter simulation by accurately precomputing the input pre-distortion required to compensate for AR filtering. First, the moments and cumulants of the AR input are derived based on the input-output relationship of the AR process, facilitating the moment and cumulant expansions of the Laplace transform (LT) and the logarithmic LT around zero, respectively. Second, both series expansions are analytically continued via the Padé approximation (PA) to recover the LT over the full complex plane. Notably, the PA-based continuation of the moment expansion, a conventional choice, can be highly inaccurate when the LT exhibits strong oscillations. By contrast, given the logarithmic LT generally has a simpler structure, the continuation of the cumulant expansion provides a more stable and accurate alternative. Third, the LT recovered from the cumulant expansion facilitates fast simulation of the AR input non-Gaussian white sequence via a random variable transformation method, thereby enabling an efficient AR process. Finally, simulations demonstrate that the proposed strategy enables accurate and fast simulation of non-Gaussian correlated clutter sequences. Subjects: [Signal Processing](https://papers.cool/arxiv/eess.SP) , [Applications](https://papers.cool/arxiv/stat.AP) Publish: 2026-03-05 13:26:03 UTC * Designed by [kexue.fm](https://kexue.fm/) \\| Powered by [kimi.ai](https://kimi.moonshot.cn/?ref=papers.cool) Include([OR]("The logical relationship between keywords (OR/AND)")): Exclude: Search Filter Highlight Stared Paper(s): \#1 [Revitalizing AR Process Simulation of Non-Gaussian Radar Clutter via Series-Based Analytic Continuation](https://papers.cool/arxiv/2603.05154#2603.05154) Export Magic Token: Kimi Language: Desc Language: Save Bug report? Issue submit? Please visit: Github:** <https://github.com/bojone/papers.cool> Please read our [Disclaimer](https://github.com/bojone/papers.cool/blob/main/Disclaimer/README_en.md) before proceeding. For more interesting features, please visit [kexue.fm](https://kexue.fm/) and [kimi.ai](https://kimi.moonshot.cn/?ref=papers.cool).
Readable Markdown	Due to the conceptual simplicity, the linear filtering framework, notably the autoregressive (AR) process, has a long history in simulating clutter sequences with specified probability density functions (PDFs) and autocorrelation functions (ACFs). However, linear filtering inevitably distorts the input distribution, which may lead to inaccurate PDF reproduction or restrict applicability to very simple ACFs. To address these challenges, this study proposes a series-based analytic continuation strategy that revitalizes AR process clutter simulation by accurately precomputing the input pre-distortion required to compensate for AR filtering. First, the moments and cumulants of the AR input are derived based on the input-output relationship of the AR process, facilitating the moment and cumulant expansions of the Laplace transform (LT) and the logarithmic LT around zero, respectively. Second, both series expansions are analytically continued via the Padé approximation (PA) to recover the LT over the full complex plane. Notably, the PA-based continuation of the moment expansion, a conventional choice, can be highly inaccurate when the LT exhibits strong oscillations. By contrast, given the logarithmic LT generally has a simpler structure, the continuation of the cumulant expansion provides a more stable and accurate alternative. Third, the LT recovered from the cumulant expansion facilitates fast simulation of the AR input non-Gaussian white sequence via a random variable transformation method, thereby enabling an efficient AR process. Finally, simulations demonstrate that the proposed strategy enables accurate and fast simulation of non-Gaussian correlated clutter sequences. Subjects: [Signal Processing](https://papers.cool/arxiv/eess.SP) , [Applications](https://papers.cool/arxiv/stat.AP) Publish: 2026-03-05 13:26:03 UTC ***
Shard	61 (laksa)
Root Hash	17309916099783778261
Unparsed URL	cool,papers!/arxiv/2603.05154 s443