Super Resolution TOA Estimation Algorithm with Maximum Likelihood ICA Based Pre-Processing

Okano, Tetsuhiro; Kidera, Shouhei; Kirimoto, Tetsuo

doi:10.1587/transcom.e96.b.1194

Cited by 3 publications

(6 citation statements)

References 10 publications

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“…When compared with MUSIC separation, to achieve more accuracy and higher resolution, MLICA-based preprocessing has been introduced [4]. To obtain multiple received signals in a single observation, this method generates multiple frequency-shifted signals known as quasi multiple channels.…”

Section: Mlica-based Music Methodsmentioning

confidence: 99%

“…These methods suffer from low resolution and accuracy for separating highly correlated signals, which is an inherent problem in radar signal processing. To achieve accurate separation of highly correlated interference signals, one approach [4] has incorporated maximum likelihood independent component analysis (MLICA) into the MUSIC method; the effectiveness of this approach has been experimentally confirmed. However, conventional methods manifest limited range resolution when separating highly correlated signals, even after tuning parameters.…”

Section: Introductionmentioning

confidence: 99%

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Super-Resolution Time of Arrival Estimation Using Random Resampling in Compressed Sensing

Noto

Shang

Kidera

et al. 2018

IEICE Trans. Commun.

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There is a strong demand for super-resolution time of arrival (TOA) estimation techniques for radar applications that can that can exceed the theoretical limits on range resolution set by frequency bandwidth. One of the most promising solutions is the use of compressed sensing (CS) algorithms, which assume only the sparseness of the target distribution but can achieve super-resolution. To preserve the reconstruction accuracy of CS under highly correlated and noisy conditions, we introduce a random resampling approach to process the received signal and thus reduce the coherent index, where the frequency-domain-based CS algorithm is used as noise reduction preprocessing. Numerical simulations demonstrate that our proposed method can achieve super-resolution TOA estimation performance not possible with conventional CS methods.

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Section: Mlica-based Music Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Super-Resolution Time of Arrival Estimation Using Random Resampling in Compressed Sensing

Noto

Shang

Kidera

et al. 2018

IEICE Trans. Commun.

Self Cite

View full text Add to dashboard Cite

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“…where G = N r + N b , the value of N r is obtained by Eq. (21), N b ≥ 2N r + 1 and g = 0.8; (5) use Eq. (16) to filter out the strongest multipath component and the other target components in the signal x i [n] in FrFD, and recover the result into time domain with α, that is…”

Section: The Algorithm Of Iterative 'Cleaning' Processmentioning

confidence: 99%

“…For a multipath channel, a threshold detection method is studied and proposed in [16], [18], and [17], in order to detect the direct path based on MF outputs. Different from non-coherent and coherent methods, the super-resolution method is used to detect the first path component in frequency domain, where a multiple signal classification (MUSIC) and independent component analysis (ICA) based method are proposed in [19] and [21], respectively. In [20], a MUSIC based super-resolution method to realize TOA estimation of acoustic chirps is proposed.…”

Section: Introductionmentioning

confidence: 99%

“…However, it still needs to firstly estimate the channel impulse response (CIR) using the MF method. Meanwhile, the frequency domain techniques only improve the TOA resolution in flat fading channels which are not feasible assumptions in many applications, and the performance seriously degrades when there are a number of highly correlated interference signals in the same range, especially in lower signal to ratio (SNR) situations [21].…”

Section: Introductionmentioning

confidence: 99%

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TOA Estimation of Chirp Signal in Dense Multipath Environment for Low-Cost Acoustic Ranging

Zhang

Chen

Wang

et al. 2019

IEEE Trans. Instrum. Meas.

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In this paper, a novel time of arrival (TOA) estimation method is proposed based on an iterative cleaning process to extract the first path signal. The purpose is to address the challenge in dense multipath indoor environments that the power of the first path component is normally smaller than other multipath components, where the traditional matchfiltering (MF) based TOA estimator causes huge errors. Along with parameter estimation, the proposed process is trying to detect and extract the first path component by eliminating the strongest multipath component using a band-elimination filter in fractional Fourier Domain (FrFD) at each iterative procedure. To further improve the stability, a slack threshold and a strict threshold are introduced. Six simple and easily calculated termination criteria are proposed to monitor the iterative process. When the iterative 'cleaning' process is done, the outputs include the enhanced first path component and its estimated parameters. Based on these outputs, an optimal reference signal for the matchfiltering (MF) estimator can be constructed, and a more accurate TOA estimation can be conveniently obtained. The results from numerical simulations and experimental investigations verified that, for acoustic chirp signal TOA estimation, the accuracy of the proposed method is superior to those obtained by the conventional MF estimators.

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A Novel Time Delay Estimation Algorithm for 5G Vehicle Positioning in Urban Canyon Environments

Deng

Zheng

Wang

et al. 2020

Sensors

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Vehicle positioning with 5G can effectively compensate for the lack of vehicle positioning based on GNSS (Global Navigation Satellite System) in urban canyons. However, there is also a large ranging error in the non-line of sight (NLOS) propagation of 5G. Aiming to solve this problem, we consider a new time delay estimation algorithm called non-line of sight cancellation multiple signal classification (NC-MUSIC). This algorithm uses cross-correlation to identify and cancel the NLOS signal. Then, an unsupervised multipath estimation method is used to estimate the number of multipaths and extract the noise subspace. The MUSIC spectral function can be calculated by the noise subspace. Finally, the time delay of the direct path is estimated by searching the peak of MUSIC spectral function. This paper adopts the 5G channel model developed by 3GPP TR38.901 for simulation experiments. The experiment results demonstrated that the proposed algorithm has obvious advantages in terms of NLOS propagation for urban canyon environments. It provided a high-precision time delay estimation algorithm for observed time difference of arrival (OTDOA), joint angle of arrival (AOA) ranging, and other positioning methods in the 5G vehicle positioning method, which can effectively improve the positioning accuracy of 5G vehicle positioning in urban canyon environments.

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Super Resolution TOA Estimation Algorithm with Maximum Likelihood ICA Based Pre-Processing

Cited by 3 publications

References 10 publications

Super-Resolution Time of Arrival Estimation Using Random Resampling in Compressed Sensing

Super-Resolution Time of Arrival Estimation Using Random Resampling in Compressed Sensing

TOA Estimation of Chirp Signal in Dense Multipath Environment for Low-Cost Acoustic Ranging

A Novel Time Delay Estimation Algorithm for 5G Vehicle Positioning in Urban Canyon Environments

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