Two-UAV Intersection Localization System Based on the Airborne Optoelectronic Platform - PubMed Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2017 Jan 6;17(1):98.
doi: 10.3390/s17010098.

Two-UAV Intersection Localization System Based on the Airborne Optoelectronic Platform

Guanbing Bai  1   2 , Jinghong Liu  3 , Yueming Song  4 , Yujia Zuo  5   6
Affiliations

Two-UAV Intersection Localization System Based on the Airborne Optoelectronic Platform

Guanbing Bai et al. Sensors (Basel). .

Abstract

To address the limitation of the existing UAV (unmanned aerial vehicles) photoelectric localization method used for moving objects, this paper proposes an improved two-UAV intersection localization system based on airborne optoelectronic platforms by using the crossed-angle localization method of photoelectric theodolites for reference. This paper introduces the makeup and operating principle of intersection localization system, creates auxiliary coordinate systems, transforms the LOS (line of sight, from the UAV to the target) vectors into homogeneous coordinates, and establishes a two-UAV intersection localization model. In this paper, the influence of the positional relationship between UAVs and the target on localization accuracy has been studied in detail to obtain an ideal measuring position and the optimal localization position where the optimal intersection angle is 72.6318°. The result shows that, given the optimal position, the localization root mean square error (RMS) will be 25.0235 m when the target is 5 km away from UAV baselines. Finally, the influence of modified adaptive Kalman filtering on localization results is analyzed, and an appropriate filtering model is established to reduce the localization RMS error to 15.7983 m. Finally, An outfield experiment was carried out and obtained the optimal results: σ B = 1.63 × 10 - 4 ( ° ) , σ L = 1.35 × 10 - 4 ( ° ) , σ H = 15.8 ( m ) , σ s u m = 27.6 ( m ) , where σ B represents the longitude error, σ L represents the latitude error, σ H represents the altitude error, and σ s u m represents the error radius.

Keywords: UAV (unmanned aerial vehicles); accuracy analysis; adaptive Kalman filtering; airborne optoelectronic platform; coordinate transformation; intersection localization.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Schematic diagram of single-station localization.
Figure 2
Figure 2
Structure chart of the onboard optoelectronic platform.
Figure 3
Figure 3
Schematic diagram of two-UAV intersection localization.
Figure 4
Figure 4
Definition of coordinate systems and their relations. (a) correlation diagram of terrestrial rectangular coordinate and geographic coordinate; (b) correlation diagram of geographic coordinateand UAV coordinate; (c) correlation diagram of UAV coordinate and camera coordinate.
Figure 5
Figure 5
Coordinate transformation process.
Figure 6
Figure 6
Positional relationship of the two visual axes. (a) rendezvous; (b) non-uniplannar intersection.
Figure 7
Figure 7
Positional relationship between UAVs and the target.
Figure 8
Figure 8
Error curves.
Figure 9
Figure 9
Positional relationship between the UAVs and the target.
Figure 10
Figure 10
Error curves.
Figure 11
Figure 11
Localization errors after modified adaptive Kalman filtering.
Figure 12
Figure 12
Localization track.
Figure 13
Figure 13
Feature position 1: (a) target detection by UAV1; (b) target detection by UAV2; and (c) positional relationship graph between the two UAVs and the target.
Figure 14
Figure 14
Feature position 2: (a) target detection by UAV1; (b) target detection by UAV2; and (c) positional relationship graph between the two UAVs and the target.
Figure 15
Figure 15
Feature position 3: (a) target detection by UAV1; (b) target detection by UAV2; and (c) positional relationship graph between the two UAVs and the target.
Figure 16
Figure 16
Feature position 4: (a) target detection by UAV1; (b) target detection by UAV2: and (c) positional relationship graph between the two UAVs and the target.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Eric J.S. Geo-Pointing and Threat Location Techniques for Airborne Border Surveillance; Proceedings of the IEEE International Conference on Technologies for Homeland Security (HST); Waltham, MA, USA. 12–14 November 2013; pp. 136–140.
    1. Gao F., Ma X., Gu J., Li Y. An Active Target Localization with Moncular Vision; Proceedings of the IEEE International Conference on Control & Automation (ICCA); Taichung, Taiwan. 18–21 June 2014; pp. 1381–1386.
    1. Su L., Hao Q. Study on Intersection Measurement and Error Analysis; Proceedings of the IEEE International Conference on Computer Application and System Modeling (ICCASM); Taiyuan, China. 22–24 October 2010.
    1. Liu L., Liu L. Intersection Measuring System of Trajectory Camera with Long Narrow Photosensitive Surface; Proceedings of the Society of Photo-Optical Instrumentation Engineers (SPIE); Beijing, China. 26 January 2006; pp. 1006–1011.
    1. Hu T. Double UAV Cooperative Localization and Remote Location Error Analysis; Proceedings of the 5th International Conference on Advanced Design and Manufacturing Engineering; Shenzhen, China. 19–20 September 2015; pp. 76–81.

LinkOut - more resources