Prof. Bo Zhang Profile

Prof. Bo Zhang

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 21 February 2024 Paper

Analysis of search and rescue positioning accuracy based on improved particle swarm optimization algorithm

Guangmiao Ji, Kai Zhang, Bo Zhang, Dongkai Yang

Proceedings Volume 12988, 129881M (2024) https://doi.org/10.1117/12.3024850

KEYWORDS: Satellites, Particle swarm optimization, Particles, Error analysis, Evolutionary algorithms, Search and rescue, Time metrology, Computer simulations, Monte Carlo methods, Analytical research

Read Abstract +

In response to the existing beacon positioning methods in medium Earth orbit (MEO) search and rescue operations, an improved particle swarm optimization algorithm based on Time Difference of Arrival (TDOA) localization is proposed. The study analyzes and simulates the Geometric Dilution of Precision (GDOP) for TDOA localization, demonstrating that GDOP is directly proportional to time measurement errors and satellite position errors. Furthermore, a modified particle swarm optimization algorithm is presented, which incorporates an adaptive fitness function and adaptive parameter adjustments using a classic particle swarm optimization approach. The proposed method improves the inertial weight and introduces adaptive algorithms for fitness functions and inertial parameters. This improved particle swarm optimization algorithm not only effectively addresses issues such as poor convergence and susceptibility to local optima but also accurately determines the position of the beacon. Simulation results indicate that When the time error is 1μs, the improved Particle Swarm Optimization (PSO) algorithm achieves a positioning accuracy of 0.57km. This represents a 5% improvement over the classical PSO algorithm and a significant 15% improvement compared to the Weighted Least Squares (WLS) algorithm.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years