Bias-Reduced Localization for Drone Swarm Based on Sensor Selection
To address the problem of accurate localization of high-speed drone swarm intrusions, this paper adopts time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements, aiming to improve the performance of estimating the motion state of drone swarms. To this end, a two-step...
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MDPI AG
2025-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/13/4034 |
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| author | Bo Wu Bazhong Shen Yonggan Zhang Li Yang Zhiguo Wang |
| author_facet | Bo Wu Bazhong Shen Yonggan Zhang Li Yang Zhiguo Wang |
| author_sort | Bo Wu |
| collection | DOAJ |
| description | To address the problem of accurate localization of high-speed drone swarm intrusions, this paper adopts time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements, aiming to improve the performance of estimating the motion state of drone swarms. To this end, a two-step strategy is proposed in this study. Firstly, a small number of sensor nodes with random locations are selected in the wireless sensor network, and the constraint-weighted least squares (CWLS) method is used to obtain the rough position and speed information of the drone swarm. Based on this rough information, the objective function of node optimization is constructed and solved using the randomized semidefinite program (SDP) algorithm proposed in this paper to screen out the sensor nodes with optimal localization performance. Secondly, the sensor nodes screened in the first step are used to re-localize the drone swarm, and the CWLS problem is constructed by combining the TDOA and FDOA measurements, and a deviation elimination scheme is proposed to further improve the localization accuracy of the drone swarm. Simulation results show that the randomized SDP algorithm proposed in this paper has the optimal localization effect, and moreover, the bias reduction scheme proposed in this paper can make the localization error of the drone swarm reach the Cramér–Rao Lower Bound (CRLB) with a low signal-to-noise ratio (SNR). |
| format | Article |
| id | doaj-art-a3757eeb951e4f8a802c71c41ab8dc22 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-a3757eeb951e4f8a802c71c41ab8dc222025-08-20T03:50:20ZengMDPI AGSensors1424-82202025-06-012513403410.3390/s25134034Bias-Reduced Localization for Drone Swarm Based on Sensor SelectionBo Wu0Bazhong Shen1Yonggan Zhang2Li Yang3Zhiguo Wang4School of Telecommunications Engineering, Xidian University, Xi’an 710126, ChinaSchool of Telecommunications Engineering, Xidian University, Xi’an 710126, ChinaSchool of Telecommunications Engineering, Xidian University, Xi’an 710126, ChinaSchool of Computer Science and Technology, Xidian University, Xi’an 710126, ChinaShaanxi Sunny Science and Technology Co., Ltd., Xi’an 710075, ChinaTo address the problem of accurate localization of high-speed drone swarm intrusions, this paper adopts time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements, aiming to improve the performance of estimating the motion state of drone swarms. To this end, a two-step strategy is proposed in this study. Firstly, a small number of sensor nodes with random locations are selected in the wireless sensor network, and the constraint-weighted least squares (CWLS) method is used to obtain the rough position and speed information of the drone swarm. Based on this rough information, the objective function of node optimization is constructed and solved using the randomized semidefinite program (SDP) algorithm proposed in this paper to screen out the sensor nodes with optimal localization performance. Secondly, the sensor nodes screened in the first step are used to re-localize the drone swarm, and the CWLS problem is constructed by combining the TDOA and FDOA measurements, and a deviation elimination scheme is proposed to further improve the localization accuracy of the drone swarm. Simulation results show that the randomized SDP algorithm proposed in this paper has the optimal localization effect, and moreover, the bias reduction scheme proposed in this paper can make the localization error of the drone swarm reach the Cramér–Rao Lower Bound (CRLB) with a low signal-to-noise ratio (SNR).https://www.mdpi.com/1424-8220/25/13/4034sensor selectionbias reductionconstraint-weighted least squares (CWLS)frequency difference of arrival (FDOA)time difference of arrival (TDOA) |
| spellingShingle | Bo Wu Bazhong Shen Yonggan Zhang Li Yang Zhiguo Wang Bias-Reduced Localization for Drone Swarm Based on Sensor Selection Sensors sensor selection bias reduction constraint-weighted least squares (CWLS) frequency difference of arrival (FDOA) time difference of arrival (TDOA) |
| title | Bias-Reduced Localization for Drone Swarm Based on Sensor Selection |
| title_full | Bias-Reduced Localization for Drone Swarm Based on Sensor Selection |
| title_fullStr | Bias-Reduced Localization for Drone Swarm Based on Sensor Selection |
| title_full_unstemmed | Bias-Reduced Localization for Drone Swarm Based on Sensor Selection |
| title_short | Bias-Reduced Localization for Drone Swarm Based on Sensor Selection |
| title_sort | bias reduced localization for drone swarm based on sensor selection |
| topic | sensor selection bias reduction constraint-weighted least squares (CWLS) frequency difference of arrival (FDOA) time difference of arrival (TDOA) |
| url | https://www.mdpi.com/1424-8220/25/13/4034 |
| work_keys_str_mv | AT bowu biasreducedlocalizationfordroneswarmbasedonsensorselection AT bazhongshen biasreducedlocalizationfordroneswarmbasedonsensorselection AT yongganzhang biasreducedlocalizationfordroneswarmbasedonsensorselection AT liyang biasreducedlocalizationfordroneswarmbasedonsensorselection AT zhiguowang biasreducedlocalizationfordroneswarmbasedonsensorselection |