Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T13:29:39.608184Z
Paper Citation Record · LEDGER
Angle-distance decomposition based on deep learning for active sonar detection
As of 9 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2507.20651.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
12 of 12 outbound references displayed
- verified exact0
- verified fuzzy9
- unresolved3
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation cbe72dc4-c540-4a1e-9584-c79f636e7ce4 · outbound
This paper cites First, the entire DOA range is discretized to form a set of possible DOA values, Θ = {θ1,.
Angle-distance decomposition based on deep learning for active sonar detection First, the entire DOA range is discretized to form a set of possible DOA values, Θ = {θ1,
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T13:29:41.886265Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 4cb55c50-f742-4e1b-a353-4a54f5347702 · outbound
This paper cites Un- like other methods that directly use images as input fea- tures, we utilize phase information as the input feature representation in this research.
Angle-distance decomposition based on deep learning for active sonar detection Un- like other methods that directly use images as input fea- tures, we utilize phase information as the input feature representation in this research
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T13:29:41.572711Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 6aec5087-2376-4ff5-8bd7-cc50aa765c55 · outbound
This paper cites Therefore, we transform the distance detection task into a binary classification problem p ∈ {p0, p1}.
Angle-distance decomposition based on deep learning for active sonar detection Therefore, we transform the distance detection task into a binary classification problem p ∈ {p0, p1}
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T13:29:41.356345Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 3300dcf3-fd14-4a19-9025-3a7ad0be32e1 · outbound
This paper cites Transformers dis- pense with recurrence and convolutions in favor of self– attention, which allows for global context aggregation at every layer.
Angle-distance decomposition based on deep learning for active sonar detection Transformers dis- pense with recurrence and convolutions in favor of self– attention, which allows for global context aggregation at every layer
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T13:29:41.067166Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation c575f360-c150-4c95-ac3d-0a8240c22851 · outbound
This paper cites How- ever, in the field of underwater acoustics, there is a chal- lenge of data scarcity, mainly due to high costs, long acquisition times, and data security concerns.
Angle-distance decomposition based on deep learning for active sonar detection How- ever, in the field of underwater acoustics, there is a chal- lenge of data scarcity, mainly due to high costs, long acquisition times, and data security concerns
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T13:29:40.802749Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 67a1bf00-5be4-47b6-96a4-8b43c0a1cca7 · outbound
This paper cites an unresolved cited work.
Angle-distance decomposition based on deep learning for active sonar detection Unresolved cited work
Reference 6
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T13:29:40.618831Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 0f8e8121-9732-4acc-9b50-136d627ef50c · outbound
This paper cites The model was trained for 20 epochs with an initial learning rate of 1 × 10−4 using the Adam optimizer.
Angle-distance decomposition based on deep learning for active sonar detection The model was trained for 20 epochs with an initial learning rate of 1 × 10−4 using the Adam optimizer
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T13:29:40.421929Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation f5fb0d6d-83f9-4274-8b02-ce87f812689d · outbound
This paper cites MAE represents the mean absolute difference between predictions and true values.
Angle-distance decomposition based on deep learning for active sonar detection MAE represents the mean absolute difference between predictions and true values
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T13:29:40.259519Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation b4e4e38c-2bbe-41e5-beb5-1ac59a3e7557 · outbound
This paper cites In Table II, we compare the performance of ASTD with other methods, such as TDNN 14, ResNet18, and ResNet5012, for the distance detection task under differ- ent SNR conditions.
Angle-distance decomposition based on deep learning for active sonar detection In Table II, we compare the performance of ASTD with other methods, such as TDNN 14, ResNet18, and ResNet5012, for the distance detection task under differ- ent SNR conditions
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T13:29:40.088633Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 40ab3e87-fef8-49b4-87e5-2af84b24b1b3 · outbound
This paper cites an unresolved cited work.
Angle-distance decomposition based on deep learning for active sonar detection Unresolved cited work
Reference 10
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T13:29:39.895223Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 0cbbb9e2-caea-4e56-90e0-355678d2e413 · outbound
This paper cites Performance under different source numbers and encoding methods Source No.
Angle-distance decomposition based on deep learning for active sonar detection Performance under different source numbers and encoding methods Source No
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T13:29:39.751551Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 3733418c-44e5-49db-9337-763e77badd5f · outbound
This paper cites AST: Audio Spectrogram Transformer.
Angle-distance decomposition based on deep learning for active sonar detection AST: Audio Spectrogram Transformer
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T13:29:39.608184Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.