Evidence map›Paper›PMID 37112289›Full record

ArticleSensors (Basel, Switzerland)2023

Procapra Przewalskii Tracking Autonomous Unmanned Aerial Vehicle Based on Improved Long and Short-Term Memory Kalman Filters.

Wei Luo, Yongxiang Zhao, Quanqin Shao, Xiaoliang Li, Dongliang Wang, Tongzuo Zhang, Fei Liu, Longfang Duan, Yuejun He, Yancang Wang and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Wei LuoNorth China Institute of Aerospace Engineering, Langfang 065000, China.ORCID 0000-0002-8876-4958
Yongxiang ZhaoNorth China Institute of Aerospace Engineering, Langfang 065000, China.
Quanqin ShaoKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
Xiaoliang LiNorth China Institute of Aerospace Engineering, Langfang 065000, China.
Dongliang WangKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.ORCID 0000-0002-1377-8394
Tongzuo ZhangUniversity of Chinese Academy of Sciences, Beijing 101407, China.
Fei LiuIntelligent Garden and Ecohealth Laboratory (iGE), College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.ORCID 0000-0003-0266-6896
Longfang DuanNorth China Institute of Aerospace Engineering, Langfang 065000, China.
Yuejun HeNorth China Institute of Aerospace Engineering, Langfang 065000, China.
Yancang WangNorth China Institute of Aerospace Engineering, Langfang 065000, China.
Guoqing ZhangNorth China Institute of Aerospace Engineering, Langfang 065000, China.
Xinghui WangNorth China Institute of Aerospace Engineering, Langfang 065000, China.
Zhongde YuNorth China Institute of Aerospace Engineering, Langfang 065000, China.

Funding

National Natural Science Foundation of China 42071289
6 · The paper itself

Abstract

This paper presents an autonomous unmanned-aerial-vehicle (UAV) tracking system based on an improved long and short-term memory (LSTM) Kalman filter (KF) model. The system can estimate the three-dimensional (3D) attitude and precisely track the target object without manual intervention. Specifically, the YOLOX algorithm is employed to track and recognize the target object, which is then combined with the improved KF model for precise tracking and recognition. In the LSTM-KF model, three different LSTM networks (

Indexed as

autonomous unmanned aerial vehicleKalman filterlong and short-term memoryobject trackingProcapra przewalskii protection

Identifiers

PMID37112289
PMCPMC10144096

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.