Combined AGADESN with DBSCAN Algorithm for Cluster Target Motion Intention Recognition
In this paper, we consider the problem of motion intention recognition for cluster targets with splitting behaviour and lack of motion prior information. This is a challenge to the classical Bayesian inference based intention recognition algorithms because they rely heavily on a priori knowledge. In...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
|
| Series: | International Journal of Aerospace Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/8220029 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832565356628017152 |
|---|---|
| author | Xirui Xue Shucai Huang Daozhi Wei |
| author_facet | Xirui Xue Shucai Huang Daozhi Wei |
| author_sort | Xirui Xue |
| collection | DOAJ |
| description | In this paper, we consider the problem of motion intention recognition for cluster targets with splitting behaviour and lack of motion prior information. This is a challenge to the classical Bayesian inference based intention recognition algorithms because they rely heavily on a priori knowledge. In order to solve these problems, a joint algorithm of deep echo state network optimized by adaptive genetic algorithm (AGADESN) and DBSCAN clustering algorithm is proposed in this paper. We use improved Olfati-Saber model with direction noise to generate cluster motion and use the cluster motion data to drive AGADESN algorithm to predict cluster destination, which achieves higher destination prediction accuracy than DESN algorithm. We innovatively design the motion similarity distance (MSD) and take the destination prediction output as one of the distance inputs, alleviating the lack of differentiation among different cluster targets caused by only relying on speed and position distance at the early stage of cluster motion. Based on the MSD, DBSCAN clustering algorithm is used to identify clusters in the field of view to determine whether splitting behaviour occurs. Simulation results demonstrate the effectiveness of the proposed algorithm in cluster target motion intention recognition and its superiority over DESN algorithm and DBSCAN algorithm only based on speed and position distance. |
| format | Article |
| id | doaj-art-21211ed13535407bb71bce105607a3e5 |
| institution | Kabale University |
| issn | 1687-5974 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Aerospace Engineering |
| spelling | doaj-art-21211ed13535407bb71bce105607a3e52025-02-03T01:07:57ZengWileyInternational Journal of Aerospace Engineering1687-59742022-01-01202210.1155/2022/8220029Combined AGADESN with DBSCAN Algorithm for Cluster Target Motion Intention RecognitionXirui Xue0Shucai Huang1Daozhi Wei2Air and Missile Defense CollegeAir and Missile Defense CollegeAir and Missile Defense CollegeIn this paper, we consider the problem of motion intention recognition for cluster targets with splitting behaviour and lack of motion prior information. This is a challenge to the classical Bayesian inference based intention recognition algorithms because they rely heavily on a priori knowledge. In order to solve these problems, a joint algorithm of deep echo state network optimized by adaptive genetic algorithm (AGADESN) and DBSCAN clustering algorithm is proposed in this paper. We use improved Olfati-Saber model with direction noise to generate cluster motion and use the cluster motion data to drive AGADESN algorithm to predict cluster destination, which achieves higher destination prediction accuracy than DESN algorithm. We innovatively design the motion similarity distance (MSD) and take the destination prediction output as one of the distance inputs, alleviating the lack of differentiation among different cluster targets caused by only relying on speed and position distance at the early stage of cluster motion. Based on the MSD, DBSCAN clustering algorithm is used to identify clusters in the field of view to determine whether splitting behaviour occurs. Simulation results demonstrate the effectiveness of the proposed algorithm in cluster target motion intention recognition and its superiority over DESN algorithm and DBSCAN algorithm only based on speed and position distance.http://dx.doi.org/10.1155/2022/8220029 |
| spellingShingle | Xirui Xue Shucai Huang Daozhi Wei Combined AGADESN with DBSCAN Algorithm for Cluster Target Motion Intention Recognition International Journal of Aerospace Engineering |
| title | Combined AGADESN with DBSCAN Algorithm for Cluster Target Motion Intention Recognition |
| title_full | Combined AGADESN with DBSCAN Algorithm for Cluster Target Motion Intention Recognition |
| title_fullStr | Combined AGADESN with DBSCAN Algorithm for Cluster Target Motion Intention Recognition |
| title_full_unstemmed | Combined AGADESN with DBSCAN Algorithm for Cluster Target Motion Intention Recognition |
| title_short | Combined AGADESN with DBSCAN Algorithm for Cluster Target Motion Intention Recognition |
| title_sort | combined agadesn with dbscan algorithm for cluster target motion intention recognition |
| url | http://dx.doi.org/10.1155/2022/8220029 |
| work_keys_str_mv | AT xiruixue combinedagadesnwithdbscanalgorithmforclustertargetmotionintentionrecognition AT shucaihuang combinedagadesnwithdbscanalgorithmforclustertargetmotionintentionrecognition AT daozhiwei combinedagadesnwithdbscanalgorithmforclustertargetmotionintentionrecognition |