Research on Multi-platform Time Difference of Arrival and Frequency Difference of Arrival Joint Location Technology

Wang, Kai; Zhang-long, Chen; Yan, Qiufei

doi:10.1109/icsip52628.2021.9688845

Cited by 3 publications

(2 citation statements)

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“…The time difference of arrival determines an iso-time-difference hyperbolic surface, and the frequency difference yields an iso-frequency-difference surface. Combined with the Earth's surface equation, they enable the determination of the target source's location [26]. The key to joint time-frequency difference positioning is the estimation of time delay and Doppler frequency difference.…”

Section: Time-frequency Difference Location Theorymentioning

confidence: 99%

High-Precision Joint TDOA and FDOA Location System

Xiao,

Dong,

Liao

et al. 2024

Remote Sensing

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Passive location based on TDOA (time difference of arrival) and FDOA (frequency difference of arrival) is the mainstream method for target localization. This paper proposes a fast time–frequency difference positioning method to address issues such as low accuracy, large computational resource utilization, and limited suitability for real-time signal processing in the conventional CAF (cross-ambiguity function)-based approach, aiming to complete the processing of the target radiation source to obtain the target parameters within a short timeframe. In the mixing product operation step of the CAF, a frequency-domain approach replaces the time-domain convolution operation in PW-ZFFT (pre-weighted Zoom-FFT) to reduce the computational load of the CAF. Additionally, a quadratic surface fitting method is used to enhance the accuracy of TDOA and FDOA. The localization solution is obtained using Newton’s method, which can provide more accurate results compared to analytical methods. Next, a signal processing platform is designed with FPGA (field-programmable gate array) and multi-core DSP (digital signal processor), and works by dividing and mapping the algorithm functional modules according to the hardware’s characteristics. We analyze the architectural advantages of multi-core DSP and design methods to improve program performance, such as EDMA transfer optimization, inline function optimization, and cache optimization. Finally, this paper constructs simulation tests in typical positioning scenarios and compares them to hardware measurement results, thus confirming the correctness and real-time capability of the program.

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Section: Time-frequency Difference Location Theorymentioning

confidence: 99%

High-Precision Joint TDOA and FDOA Location System

Xiao,

Dong,

Liao

et al. 2024

Remote Sensing

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“…Sound source localization techniques based on microphone arrays and their applications are currently receiving increasing attention in academia [3]. Currently, there are many theories and methods for microphone-array-based sound source localization, which can be broadly classified into the Received Signal Strength (RSS) localization method [4,5], Angle of Arrival (AOA) localization method [6,7], Time of Arrival (TOA) localization method [8,9], Time Difference of Arrival (TDOA) localization methods [10][11][12], Frequency Difference of Arrival (FDOA) [13,14] localization method, Multiple Signal Classification (MUSIC) positioning method [15,16], and many other methods based on beamforming or other advanced methods [2,17,18], etc.…”

Section: Introductionmentioning

confidence: 99%

A Sound Source Localization Method Based on Frequency Divider and Time Difference of Arrival

Zhao

Zhang

et al. 2023

Applied Sciences

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In recent years, sound source localization, as a passive localization technique with higher safety and convenience compared with other localization techniques such as active emission of electromagnetic waves, has received more and more attention in academia. This paper researches and improves the far-field sound source localization algorithm based on the generalized cross-correlation method (GCC) Time Difference of Arrival (TDOA) estimation algorithm and completes the design and implementation of the microphone array sound source localization system. This paper adds a frequency divider to the traditional generalized correlation time delay estimation algorithm for pre-processing, sampling, and localization of sound source acoustic waves and adopts a low-cost microphone array deployment scheme as far as possible to improve the flexibility and practicality of the localization system; at the same time, the “Minimum Sphere Method” is used at the back end of the algorithm to classify the localization coordinates at different frequencies and, finally, output reasonable sound source coordinates. In the back-end of the algorithm, the “Minimum Sphere Method” is used to classify the localization coordinates at different frequencies and, finally, output the reasonable sound source coordinates. The experimental results show that the sound source localization system designed in this paper has good performance in terms of localization accuracy and cost-effectiveness and overcomes the failure of the generalized mutual correlation algorithm in the original application of high noise environment and multi-source environment localization.

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