Research on Fast Iterative TDOA Localization Method Based on Spatial Grid Gradients

WANG Jie; WU Linghao; BU Xiangxi; LI Hang; LIANG Xingdong

doi:10.11999/JEIT241105

Volume 47 Issue 7

Jul. 2025

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Article Navigation > Journal of Electronics & Information Technology > 2025 > 47(7): 2108-2116

WANG Jie, WU Linghao, BU Xiangxi, LI Hang, LIANG Xingdong. Research on Fast Iterative TDOA Localization Method Based on Spatial Grid Gradients[J]. Journal of Electronics & Information Technology, 2025, 47(7): 2108-2116. doi: 10.11999/JEIT241105

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WANG Jie, WU Linghao, BU Xiangxi, LI Hang, LIANG Xingdong. Research on Fast Iterative TDOA Localization Method Based on Spatial Grid Gradients[J]. Journal of Electronics & Information Technology, 2025, 47(7): 2108-2116. doi: 10.11999/JEIT241105

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Research on Fast Iterative TDOA Localization Method Based on Spatial Grid Gradients

doi: 10.11999/JEIT241105 cstr: 32379.14.JEIT241105

WANG Jie¹,
WU Linghao¹,
BU Xiangxi^{2
,
,},
LI Hang^{2, 3},
LIANG Xingdong²

1.
School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
2.
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
3.
University of Chinese Academy of Sciences, Beijing 100049, China

Funds: The National Natural Science Foundation of China (62171229)

Received Date: 2024-12-16
Rev Recd Date: 2025-03-27

Available Online: 2025-04-07

Publish Date: 2025-07-22

Abstract

Abstract

Objective In various application scenarios such as Unmanned Aerial Vehicle (UAV) formation, emergency rescue, and low-altitude intelligent networks, passive localization technologies that offer low latency and high precision are of significant practical value. The Time Difference of Arrival (TDOA) localization method is widely adopted for wireless signal source localization due to its ability to operate without requiring the target to actively transmit a signal and its strong adaptability to different environments. Among the various methods used to enhance the accuracy of TDOA localization, the Taylor Iterative Method has gained significant popularity. However, this method requires the calculation of a Taylor expansion for each iteration, resulting in a high computational load. This computational burden often leads to issues such as poor real-time performance and degraded accuracy, which hinder the application of TDOA localization technology in low-latency engineering contexts. To overcome these challenges, this paper proposes a novel TDOA rapid iterative localization method based on spatial grid gradients. The proposed method can significantly reduce computational time while maintaining high levels of localization accuracy. Methods The proposed approach is based on the concept of spatial gridization, incorporating insights derived from the inherent gradient relationships between neighboring grids. These relationships are leveraged to integrate the grid framework into an iterative compensation model. This integration addresses the performance limitations associated with grid width in traditional gridization algorithms, thereby enhancing the efficiency of the iterative localization process. The overall computational process is divided into two distinct stages: preprocessing and iterative localization. The preprocessing stage occurs during the system’s initialization phase and includes constructing the spatial grid, calculating the TDOA gradients between grid points, and establishing the grid-based iterative matrix. Once this preprocessing is complete, the results are stored and readily accessible for future localization processes. During the localization stage, the precomputed iteration matrix is directly invoked and the initial value for the target’s position. The method then calculates and compensates for the deviation between the initial value and the actual target position. By employing a grid-based approach, the significant computational workload typically encountered during iterative localization is shifted to the preprocessing phase. This leads to a marked reduction in localization time, significantly improving computational efficiency. Results and Discussions To validate the effectiveness and performance of the proposed algorithm, simulations and field experiments are conducted. The results are compared with those of the classic spatial gridization algorithm and the Taylor Iterative Method. It is observed that the classic spatial gridization algorithm experiences a significant loss in localization accuracy as the grid width increases, accompanied by a dramatic increase in computation time. In contrast, the proposed algorithm remains unaffected by grid width and outperforms the traditional spatial gridization method in both localization accuracy and computation time (Fig. 3). A deeper comparison of the proposed algorithm with the Taylor Iterative Method is made by analyzing the effects of TDOA estimation errors, initial value errors, and iteration thresholds on the performance of both algorithms. Specifically, under varying TDOA estimation errors, the proposed algorithm reduces the average computation time by 76% compared to the Taylor Iterative Method, while maintaining similar localization accuracy (Fig. 4). Under varying initial value errors, the proposed algorithm reduces average computation time by 78%, with comparable localization accuracy (Fig. 5). As the iteration threshold increases, both algorithms experience a slight reduction in localization accuracy; however, their overall performance remains similar. In this scenario, the proposed algorithm still reduces computation time by approximately 76% when compared to the Taylor Iterative Method (Fig. 6). To further verify the applicability of the proposed algorithm in real-world scenarios, field experiments are also conducted. The field test results confirm the validity of the proposed method, demonstrating a 78% reduction in computation time compared to the Taylor Iterative Method, while maintaining comparable localization accuracy (Table 2). Conclusions The proposed TDOA fast iterative localization method, based on spatial grid gradients, effectively reduces computational complexity while maintaining localization accuracy. This method is well-suited for high real-time passive localization applications. It significantly enhances both the efficiency and practicality of TDOA localization systems. Future work will focus on expanding the applicability of this algorithm by integrating it with other localization techniques, such as Time of Arrival (TOA), Angle of Arrival (AOA), and Frequency Difference of Arrival (FDOA). This integration is expected to facilitate the development of low-altitude economic activities and contribute to advancing the capabilities of localization technologies.
- Passive localization,
- Time Difference of Arrival (TDOA),
- Spatial gridization,
- Gradient,
- Low-altitude intelligent networking

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References(21)

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