A Passive DOA Estimation Algorithm of Underwater Multipath Signals via Spatial Time-frequency Distributions

Abstract Direction of arrival estimation (DOA) of LFM signal is an essential task in radar, sonar, acoustics and biomedical. In this paper, a short time Fourier transform multi-step knowledge aided iterative generalized minimum residual (STFT-MS-KAI-GMRES) approach is presented to amend the angle measurement of this signal. A three stage algorithm is proposed. First, the process is initiated with formulating an estimation algorithm for the carrier frequency and chirp rate, followed by calculation of STFT of the output of array element; this yields a spatial time-frequency distribution (STFD) matrix. Next, the Krylov subspace-based estimation algorithm is formulated in the presence of MS-KAI-ESPRIT algorithm. If the number of antennas increases, the accuracy of the algorithm will increase, but we will incur more communication costs. Results are presented showing attainment of the CRLB by STFT-MS-KAI-GMRES the for an adequately large signal to noise ratio (SNR). An important feature of the method presented in the current study is the low computational complexity that has higher suitability for practical applications.

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ES-DPR: A DOA-Based Method for Passive Localization in Indoor Environments

Sensors ◽

10.3390/s19112482 ◽

2019 ◽

Vol 19 (11) ◽

pp. 2482 ◽

Cited By ~ 5

Author(s):

Zhang Chen ◽

Jinlong Wang

Keyword(s):

Multipath Propagation ◽

Estimation Algorithm ◽

Doa Estimation ◽

Recognition Algorithm ◽

Indoor Environments ◽

Direct Path ◽

Multipath Signals ◽

Localization Approach ◽

Passive Localization ◽

Methods Numerical

In this paper, we propose a novel indoor passive localization approach called eigenspace-based DOA with direct-path recognition (ES-DPR), based on a DOA estimation algorithm with multiple omnidirectional antennas deployed in a uniform linear array (ULA). To address the multipath propagation interference problem in the indoor environments, we utilize the azimuth and RSS estimation results, which are calculated by using the eigenspace-based DOA (ES-DOA) algorithm, in a novel style. A direct-path bearing recognition algorithm is introduced to identify the real DOA of the signal source in different indoor environments, by combining the azimuth and RSS estimation with ensemble learning methods. Numerical simulations are conducted to verify the validity and superiority of the proposed method. The results show that the proposed ES-DPR method can achieve high resolution and has strong anti-noise capability in dealing with the multipath signals, and the direct-path recognition algorithm is reliable and robust in different indoor environments, even in undetectable direct-path conditions.

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The Performance Analysis of DOA Estimation Algorithm for Troposcatter Signal based on Time Frequency Diffusion Modification

Proceedings of the 2017 2nd International Conference on Communication and Information Systems - ICCIS 2017 ◽

10.1145/3158233.3159312 ◽

2017 ◽

Author(s):

Jing Wang ◽

Ruyi Zhu ◽

Shuai Han

Keyword(s):

Performance Analysis ◽

Estimation Algorithm ◽

Doa Estimation ◽

Time Frequency

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Joint Estimation of Time-Frequency Signature and DOA Based on STFD for Multicomponent Chirp Signals

International Scholarly Research Notices ◽

10.1155/2014/937139 ◽

2014 ◽

Vol 2014 ◽

pp. 1-6 ◽

Cited By ~ 1

Author(s):

Ziyue Zhao ◽

Congfeng Liu

Keyword(s):

Array Processing ◽

Estimation Method ◽

Doa Estimation ◽

Joint Estimation ◽

Subspace Method ◽

Signal Model ◽

Frequency Distributions ◽

Time Frequency ◽

Chirp Signals ◽

Estimation Of Time

In the study of the joint estimation of time-frequency signature and direction of arrival (DOA) for multicomponent chirp signals, an estimation method based on spatial time-frequency distributions (STFDs) is proposed in this paper. Firstly, array signal model for multicomponent chirp signals is presented and then array processing is applied in time-frequency analysis to mitigate cross-terms. According to the results of the array processing, Hough transform is performed and the estimation of time-frequency signature is obtained. Subsequently, subspace method for DOA estimation based on STFD matrix is achieved. Simulation results demonstrate the validity of the proposed method.

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