Particle Filtering and Posterior Cramér-Rao Bound for 2-D Direction of Arrival Tracking Using an Acoustic Vector Sensor

Zhong, Xiaofeng; Premkumar, A.B.; Madhukumar, A. S.

doi:10.1109/jsen.2011.2168204

Cited by 49 publications

(38 citation statements)

References 34 publications

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“…Similar to the L 1 -SVD algorithm [11], singular value decomposition (SVD) is adopted to reduce both the computational complexity and the sensitivity to noise. Taking the SVD of X, we have X = ULV H and define (27) where I K is a K ×K identity matrix and 0 is a K ×(L −K ) zeros matrix. Applying (27), the sparse representation MMV data model of the AVSA in (24) can be rewritten with the reduced dimension representation as follows…”

Section: Measurement Models For the Subarrays In An Avs Arraymentioning

confidence: 99%

“…They have explored the trigonometric interrelations among the sensors of each individual AVS to develop the non-spatial realization of ESPRIT with both a single AVS and an AVSA for DOA estimation of multiple uncorrelated sources. Furthermore, the AVS and AVSA have been proposed for the DOA estimation for different source types, alternative array structures [1,9,13,20,21], and for source tracking [27,28]. It is noted that the methods discussed above can be viewed as the extensions of the traditional SSA-DOA estimation methods to AVS-DOA estimation methods.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Source DOA Estimation Using an Acoustic Vector Sensor Array Under a Spatial Sparse Representation Framework

Zou

Ritz

2015

Circuits Syst Signal Process

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This paper investigates the estimation of the two-dimensional direction of arrival (2D-DOA) of sound sources using an acoustic vector sensor array (AVSA) within a spatial sparse representation (SSR) framework (AVS-SSR-DOA). SSR-DOA estimation methods rely on a pre-defined grid of possible source DOAs and essentially suffer from the grid-effect problem: Reducing the size of the grid spacing leads to increased computational complexity. In this paper, we propose a two-step approach to tackle the grid-effect problem. Specifically, omnidirectional sensor array-based SSR-DOA estimation firstly provides initial low-cost DOA estimates using a coarse grid spacing. Secondly, a closed-form solution is derived by exploring the unique subarray manifold matrix correlation and subarray signal correlation of the AVSA, which allows for DOA estimates between the pre-defined angles of the grid and potentially achieves higher DOA estimation accuracy. To further alleviate the estimation bias due to noise and sparse representation model errors, line-fitting (LF) techniques and subspace techniques (ST) are employed to develop two novel DOA estimation algorithms, referred to as AVS-SSR-LF and AVS-SSR-ST, respectively. Extensive simulations validate the effectiveness of the proposed algorithms when estimating the DOAs of B Yue-Xian Zou

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Section: Measurement Models For the Subarrays In An Avs Arraymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Source DOA Estimation Using an Acoustic Vector Sensor Array Under a Spatial Sparse Representation Framework

Zou

Ritz

2015

Circuits Syst Signal Process

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“…Recently, solutions that employ low-cost, low-complexity sensors such as microphones [10], [13], magnetometers [14], [15], or accelerometers [16] have emerged.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Tracking Based on Fusion of Magnetometer and Accelerometer Sensor Measurements With Particle Filtering

Hostettler

Djurić

2015

IEEE Trans. Veh. Technol.

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This is a post-print of a paper published in IEEE Transactions on Vehicular Technology. When citing this work, you must always cite the original article:R. Hostettler and P. M. Djurić, "Vehicle tracking based on fusion of magnetometer and accelerometer sensor measurements with particle filtering," Vehicular Technology, IEEE Transactions on, vol. 64,no. 11, Abstract-In this article, we propose a method for vehicle tracking on roadways using measurements of magnetometers and accelerometers. The measurements are used to build a low-cost, low-complexity vehicle tracking sensor platform for highway traffic monitoring. First, the problem is formulated by introducing the process model for the motion of the vehicle on the road and two measurement models, one for each of the sensors. Second, it is shown how the measurements of the sensors can be fused using particle filtering. The standard sampling importance resampling (SIR) particle filter is extended for processing of multi-rate sensor measurements and models that employ unknown static parameters. The latter are treated by Rao-Blackwellization. The performance of the method is demonstrated by computer simulations. It is found that it is feasible to fuse the two sensors for vehicle tracking and that the proposed multi-rate particle filter performs better than particle filters that process only measurements of one of the sensors. The main contribution of this paper is the novel approach of fusing the measurements of road-mounted magnetometers and accelerometers for vehicle tracking and traffic monitoring.

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“…The AVS manifold is independent of source signal's frequency, which enhances its utility in wideband acoustic signal processing applications [1], including acoustic source detection [2], localization [1] and tracking [3], [4] in underwater, battlefield, and room acoustic environments [5], [6]. More recent AVS applications include speech enhancement for hands free communication, sound source localization in air, and detection of seismic activities.…”

Section: Introductionmentioning

confidence: 99%

A distributed particle filter for acoustic source tracking using an acoustic vector sensor network

Zhong

Mohammadi

Premkumar

et al. 2014

2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)

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Abstract-This letter presents a distributed particle filtering (PF) approach for wideband acoustic source tracking using an acoustic vector sensor (AVS) network. At each distributed AVS node of the AVS network, the unscented information PF (UIPF) provides local estimates of the source location. A distributed consensus algorithm, based on the first and second order statistics of the localized UIPF estimated states, then obtains the global position estimate for the overall AVS network. Since the UIPF inverts smaller order matrices, its computational complexity is significantly less as compared to its centralized counterpart. The performance of the proposed tracking algorithm is studied under both single source and multiple source tracking scenarios.Index Terms-Acoustic vector sensor, sensor network, particle filtering, multiple source tracking, unscented information filter.

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Particle Filtering and Posterior Cramér-Rao Bound for 2-D Direction of Arrival Tracking Using an Acoustic Vector Sensor

Cited by 49 publications

References 34 publications

Multi-Source DOA Estimation Using an Acoustic Vector Sensor Array Under a Spatial Sparse Representation Framework

Multi-Source DOA Estimation Using an Acoustic Vector Sensor Array Under a Spatial Sparse Representation Framework

Vehicle Tracking Based on Fusion of Magnetometer and Accelerometer Sensor Measurements With Particle Filtering

A distributed particle filter for acoustic source tracking using an acoustic vector sensor network

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