Underwater TDOA/FDOA joint localisation method based on cross‐ambiguity function

Jiang, Feng; Zhang, Zhenkai; Najafabadi, Hamid Esmaeili; Yang, Yi

doi:10.1049/iet-rsn.2020.0003

Cited by 7 publications

(5 citation statements)

References 29 publications

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“…In Section 3.2, we perform a peak search on the cross-ambiguity function to obtain estimates of time delay and frequency shift. Due to the continuous variation of the timefrequency differences between signals, this method can only provide coarse estimates of TDOA and FDOA [33][34][35]. Therefore, the quadratic surface fitting method is used to improve the accuracy of the time-frequency difference estimation.…”

Section: Quadratic Surface Fittingmentioning

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: Quadratic Surface Fittingmentioning

confidence: 99%

High-Precision Joint TDOA and FDOA Location System

Xiao,

Dong,

Liao

et al. 2024

Remote Sensing

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“…For several decades, passive localization technology has been widely used in many fields, such as signal processing [1], wireless sensor networks [2], and underwater sources localization [3]. For stationary sources, localization methods include Time of Arrival (TOA) [4], Angle of Arrival (AOA) [5], and Time Difference of Arrival (TDOA) [6,7].…”

Section: Introductionmentioning

confidence: 99%

Joint localization algorithm of TDOA and FDOA considering the position error of underwater sensors

Meng

Zhang

Lin

2022

IET Radar Sonar & Navi

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The joint localization method of TDOA and FDOA is widely used because it can estimate the underwater moving source well. Usually, a two-step weighted least squares (TSWLS) algorithm is used to estimate the source. However, the noise square term is always neglected in the TSWLS algorithm. In order to improve this situation, an improved TSWLS algorithm is proposed under the circumstance of sensor position inaccuracy. First, a new localization equation is constructed by the time difference equation, and the source position and velocity are initially estimated. The intermediate variables are then Taylor expanded and inserted into the localization equation of the algorithm's first step to modify the initial estimate value. Finally, the source's precise position and velocity are determined, and the proposed method was demonstrated to achieve the Cramer-Rao lower bound (CRLB) accuracy through simulations.

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“…The noise distribution [1,2] plays an important role in developing underwater signal processors. Traditional signal processors such as underwater localization [3][4][5][6][7][8][9][10][11][12][13], underwater tracking [9,14,15], sonar imaging [16][17][18][19][20][21][22][23][24][25][26][27], direction of arrival (DOA) estimation [28][29][30][31][32], and underwater acoustic communication (UAC) are mostly based on Gaussian noise, which can be supported by a central limit theorem. Besides, the Gaussian model is just determined by the first-order and second-order statistics [33].…”

Section: Introductionmentioning

confidence: 99%

A Viterbi Decoder under Class A Modeled Noise in Shallow Water

Wang

Fan

Zhang

et al. 2022

Wireless Communications and Mobile Computing

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A traditional Viterbi decoder is primarily optimized for additive white Gaussian noise (AWGN). With the AWGN channel, it offers good decoding performance. However, the underwater acoustic communication (UAC) channel is extremely complicated. In addition to white noise, there are a variety of artificial and natural impulse noise that occur suddenly. The traditional Viterbi decoder cannot obtain the optimum performance under this case. In order to solve this problem, this paper introduces a novel Viterbi decoder with the impulsive noise, which is considered to be subjected to Middleton Class A distribution in shallow ocean. Since Middleton Class A noise is very complicated, a simplified model is first introduced. Then, the error analysis of simplified model under various parameters is discussed in detail. The analysis shows that the simplified one just leads to slight error. Hereafter, a novel Veterbi decoder using the simplified model is discussed. Compared to a traditional decoder, a preprocessing is just required. The performance of soft decision-based decoder in the Middleton Class A noise channel (MAIN) and AWGN are further compared. Based on our simulations, the new decoder can significantly improve the performance in comparison with conventional one, which further validates our presented method.

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Underwater TDOA/FDOA joint localisation method based on cross‐ambiguity function

Cited by 7 publications

References 29 publications

High-Precision Joint TDOA and FDOA Location System

High-Precision Joint TDOA and FDOA Location System

Joint localization algorithm of TDOA and FDOA considering the position error of underwater sensors

A Viterbi Decoder under Class A Modeled Noise in Shallow Water

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