Particle Filter Algorithm for DOA Tracking Using Co-Prime Array

Dong, Feibiao; Xu, Limei; Li, Xuesheng

doi:10.1109/lcomm.2019.2953466

Cited by 17 publications

(7 citation statements)

References 18 publications

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“…As a limitation, our method is still currently applicable to slow-moving targets, although the DOA change rate in our paper is already relatively fast compared to the experiments in other papers [20,38,39]. For the fast-moving sound source in…”

Section: Discussionmentioning

confidence: 88%

Multi-frequency sequential sparse Bayesian learning for DOA estimation of the moving wideband sound source

Guo¹,

卢²

2022

Meas. Sci. Technol.

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To obtain the Direction of arrival (DOA) of the moving sound source from the sequential measurements collected by the microphone array is the main task in acoustic tracking and detection. Thanks to the development of compressive sensing (CS) and sparse Bayesian learning (SBL), treating time-varying DOA estimation as time-varying sparse signal recovery is considered to be a promising idea. However, most methods have assumed that the source is narrowband and the DOA is on the predefined sparse grid at each estimation step. In fact, most sound sources in the air are wideband and the DOA variescontinuously. Therefore, the multi-frequency sequential sparse Bayesian learning (SBL) is proposed for the DOA estimation of the moving wideband sound source in this paper. In this method, gamma hyperprior is used as sparsity-promoting prior for multi-frequency bins so that the multi-frequency measurements can be utilized simultaneously, and with an inexact dynamic model, the sparsity-dependent information from the multi-frequency sequential measurements can be propagated to the next estimation step to improve the performance. Besides, the off-grid refinement is incorporated into the framework to adapt to the continuously varying DOA. Simulation results demonstrate that the proposed method has better performances under low signal-to-noise conditions with higher estimation accuracy and less computation time compared to other state-of-the-art methods. The field experiments show that our proposed method can has a stronger ability to suppress grating lobes and spatial aliasing than conventional methods in the estimation for wideband DOA and adapt to the scenarios where the number of sources also changes.

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Section: Discussionmentioning

confidence: 88%

Multi-frequency sequential sparse Bayesian learning for DOA estimation of the moving wideband sound source

Guo¹,

卢²

2022

Meas. Sci. Technol.

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“…For the traditional compact MIMO radar whose transmitting and receiving arrays are uniform linear array, the effective number of virtual elements is the sum of transmitting elements and receiving elements minus 1, and continuous virtual elements can be generated by dimensionality reduction transformation [16]. For the monostatic nested array MIMO radar in this paper, MN virtual array elements will be formed after matched filtering, some of which are redundant elements.…”

Section: Dimensionality Reduction Preprocessingmentioning

confidence: 99%

A robust DOA tracking algorithm for monostatic nested array MIMO radar

Peng,

Zhou,

Deng

et al. 2023

Fourth International Conference on Signal Processing and Computer Science (SPCS 2023)

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“…In the next simulations, we compare the proposed method with several approaches, including the two-step MUSIC algorithm, two-step ESPRIT algorithm, RD-DPD algorithm, NC-particle filter (PF) algorithm, NC-Kalman filter (KF) algorithm, the proposed method for circular signals (PMC), and CRB. The NC-PF algorithm is a direct tracking method based on the PF [31], which replaces its own likelihood function with the cost function in the RD-DPD algorithm. It obtains the positions of the sources and achieves tracking via the weighted summation of particles.…”

Section: Algorithms Complexitymentioning

confidence: 99%

Direct position tracking method for non‐circular signals with distributed passive arrays via first‐order approximation

Cao,

Zhang,

Hao

2023

ETRI Journal

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In this study, a direct position tracking method for non‐circular (NC) signals using distributed passive arrays is proposed. First, we calculate the initial positions of sources using a direct position determination (DPD) approach; next, we transform the tracking into a compensation problem. The offsets of the adjacent time positions are calculated using a first‐order Taylor expansion. The fusion calculation of the noise subspace is performed according to the NC characteristics. Because the proposed method uses the signal information from the previous iteration, it can realize automatic data associations. Compared with traditional DPD and two‐step localization methods, our novel process has lower computational complexity and provides higher accuracy. Moreover, its performance is better than that of the traditional tracking methods. Numerous simulation results support the superiority of our proposed method.

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Particle Filter Algorithm for DOA Tracking Using Co-Prime Array

Cited by 17 publications

References 18 publications

Multi-frequency sequential sparse Bayesian learning for DOA estimation of the moving wideband sound source

Multi-frequency sequential sparse Bayesian learning for DOA estimation of the moving wideband sound source

A robust DOA tracking algorithm for monostatic nested array MIMO radar

Direct position tracking method for non‐circular signals with distributed passive arrays via first‐order approximation

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