A ML-Based Direct Localization Method for Multiple Sources with Moving Arrays

Qin, Tianzhu; Li, Lin; Lu, Zhiyu; Wang, Da-Ming

doi:10.1109/icct.2018.8599922

Cited by 5 publications

(5 citation statements)

References 14 publications

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“…Lim (p) in form ( 23) can be regarded as a joint pseudo-PDF corresponding to multiple mutually independent random variables, which can be easily generated using Lq (p q ) Q q=1 . Hence, considering the design of Lim (p) shall be appropriate approximation of L2 (p) satisfies (23).…”

Section: Global Maximization Of the Clfmentioning

confidence: 99%

“…It should be noted that the choices of ρ 0 and ρ 1 affect the performance of the proposed estimator. Lim (p) can indeed be factorized in terms of the Q emitter locations as (23), where Lq (p) is expressed as…”

Section: Global Maximization Of the Clfmentioning

confidence: 99%

“…The iterative DPD estimator introduced in [15] is based on the alternating projection (AP) technique. [23] proposed a decoupled ML DPD algorithm later, which is also iterative. Nevertheless, the performance of these two ML DPD estimators is closely tied to the initialization, i.e., their ML cost function will not converge to the global maximum if initial guesses deviate significantly from true values.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Computational Efficient Maximum Likelihood Direct Position Determination Approach for Multiple Emitters Using Angle and Doppler Measurements

Wang¹,

Sun²,

Wan³

et al. 2021

Preprint

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Emitter localization is widely applied in the military and civilian fields. In this paper, we tackle the problem of position estimation for multiple stationary emitters using Doppler frequency shifts and angles by moving receivers. The computational load for the exhaustive maximum likelihood (ML) direct position determination (DPD) search is insufferable. Based on the Pincus' theorem and importance sampling (IS) concept, we propose a novel non-iterative ML DPD method. The proposed method transforms the original multidimensional grid search into random variables generation with multiple low-dimensional pseudo-probability density functions (PDF), and the circular mean is used for superior position estimation performance. The computational complexity of the proposed method is modest, and the off-grid problem that most existing DPD techniques face is significantly alleviated. Moreover, it can be implemented in parallel separately. Simulation results demonstrate that the proposed ML DPD estimator can achieve better estimation accuracy than state-of-the-art DPD techniques. With a reasonable parameter choice, the estimation performance of the proposed technique is very close to the Cramér-Rao lower bound (CRLB), even in the adverse conditions of low signal-to-noise ratios (SNR) levels.

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Section: Global Maximization Of the Clfmentioning

confidence: 99%

Section: Global Maximization Of the Clfmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Computational Efficient Maximum Likelihood Direct Position Determination Approach for Multiple Emitters Using Angle and Doppler Measurements

Wang¹,

Sun²,

Wan³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…After some time, M. Oispuu provided a direct position determination algorithm: using ML and Capon method [18] to improve the estimation accuracy in 2010. Then, T. Qin provided a DPD algorithm with moving arrays in 2018 [19]. T. Zhou proposed an iterative adaptive direct localization technology to deal with coherent sources [20] in 2019.…”

Section: Introductionmentioning

confidence: 99%

Direct Position Determination of Non-Circular Sources for Multiple Arrays via Weighted Euler ESPRIT Data Fusion Method

2022

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In recent years, direct position determination (DPD) with multiple arrays for non-circular (NC) signals is a hot topic to research. Conventional DPD techniques with spectral peak search methods have high computational complexity and are sensitive to the locations of the observation stations. Besides, there will be loss when the signal propagates in the air, which leads to different received signal-to-noise ratios (SNRs) for each observation station. To attack the problems mentioned above, this paper derives direct position determination of non-circular sources for multiple arrays via weighted Euler estimating signal parameters viarotational invariance techniques (ESPRIT) data fusion (NC-Euler-WESPRIT) method. Firstly, elliptic covariance information of NC signals and Euler transformation are used to extend the received signal. Secondly, ESPRIT is applied to avoid the high-dimensional spectral function search problem of each observation station. Then, we combine the information of all observation stations to construct a spectral function without complex multiplication to reduce the computational complexity. Finally, the data of each observation station is weighted to compensate for the projection error. The consequence of simulation indicates that the proposed NC-Euler-WESPRIT algorithm not only improves the estimation performance, but also greatly reduces the computational complexity compared with subspace data fusion (SDF) technology and NC-ESPRIT algorithm.

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“…Wireless positioning is an important research direction in the civilian and military fields [1]. There are many wireless positioning algorithms, among which the two-step positioning algorithms are the most concerned.…”

Section: Introductionmentioning

confidence: 99%

A Frequency Domain Direct Localization Method Based on Distributed Antenna Sensing

Zhao

Zheng

et al. 2021

Wireless Communications and Mobile Computing

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Traditional two-step passive localization methods need to extract the parameters like the direction of arrival (DOA), time of arrival (TOA), and time difference of arrival (TDOA) from the original data to determine the source position, which causes the poor positioning accuracy due to error accumulation. In this paper, a direct position determination (DPD) method is proposed to improve the positioning accuracy and robustness, which is based on a correlation algorithm. Firstly, the cost function directly related to the location of the source can be established by synthesizing the data received by multiantenna in the frequency domain. Then, the position of the source is estimated by the correlation DPD method to search the monitoring area. Compared to the improved TDOA algorithm and Least Squares DPD algorithm, the proposed method shows better localization accuracy of different SNRs. Finally, based on real measured data, it can be seen that the results of the proposed algorithm are better than the improved TDOA algorithm.

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A ML-Based Direct Localization Method for Multiple Sources with Moving Arrays

Cited by 5 publications

References 14 publications

A Computational Efficient Maximum Likelihood Direct Position Determination Approach for Multiple Emitters Using Angle and Doppler Measurements

A Computational Efficient Maximum Likelihood Direct Position Determination Approach for Multiple Emitters Using Angle and Doppler Measurements

Direct Position Determination of Non-Circular Sources for Multiple Arrays via Weighted Euler ESPRIT Data Fusion Method

A Frequency Domain Direct Localization Method Based on Distributed Antenna Sensing

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