Bearing estimation for a distributed source: modeling, inherent accuracy limitations and algorithms

Raich, Raviv; Goldberg, J.; Messer, H.

doi:10.1109/78.823970

Cited by 77 publications

(47 citation statements)

References 19 publications

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“…The rank of depends on the rank of the signal covariance matrix . Utilizing (2) and (8), we obtain (19) Employing the matrix inversion lemma, we obtain (20) Hence, we can rewrite (18) as follows: (21) and then, the subspace matrix is a matrix (22) If the signal subspace is indeed low rank with rank , the approximation sign is replaced by an equality sign. This leads to an interpretation of the LR detector as a bank of beamformers.…”

Section: A Subspace Beamformer-type Imentioning

confidence: 99%

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Detection of Distributed Sources Using Sensor Arrays

Jin

Friedlander

2004

IEEE Trans. Signal Process.

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Abstract-In this paper, we consider the problem of detecting a random spatially distributed signal source by an array of sensors. We start with an approximate likelihood ratio (LR) detector and analyze its performance. Using the generalized likelihood ratio (GLR) approach, we then derive detectors under several assumptions on the available statistics. The performance of these detectors is evaluated, and the effect of the angular spread of the source is investigated. The detection performance behaves differently under different scenarios. We notice that the degrees of freedom (DOF) of the distributions of the detection statistics depend on both the signal angular spread and the number of data snapshots. Specifically, at a high SNR level and with small degrees of freedom, an increase of angular spread improves the detection performance. However, with large degrees of freedom, the increase of angular spread reduces detection performance. We provide a detailed discussion of the behavior of detection performance under various conditions. A comparison between the GLR detectors and conventional beamformer detectors is made by computer simulations. The results indicate that the GLR detectors perform better as the angular spread becomes large than that of the conventional beamformer detectors.Index Terms-Detection, distributed source, generalized likelihood ratio (GLR), sensor array.

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Section: A Subspace Beamformer-type Imentioning

confidence: 99%

“…Prior work on distributed sources focuses primarily on source localization and DOA estimation (see, e.g., [1], [4], [19], and [23]). Estimation of the number of distributed sources has also been studied in [1].…”

Section: Introductionmentioning

confidence: 99%

Detection of Distributed Sources Using Sensor Arrays

Jin

Friedlander

2004

IEEE Trans. Signal Process.

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“…For obtaining the exact nominal DOA and angular spread of a spatially distributed source, the problem of distributed source model has been widely studied since the early 1990s, and a large number of methods are proposed for the parameter estimation of distributed source [9][10][11][12][13][14][15][16][17]. However, most of the models and the estimation algorithms can be only exploited in the case of narrowband source, because the location vector in the time domain is time varying when the incident source is wideband.…”

Section: Introductionmentioning

confidence: 99%

Separation and localization of multiple distributed wideband chirps using the fractional Fourier transform

Zhang

Liu

et al. 2015

J Wireless Com Network

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In this paper, we consider the problem of localizing the multiple distributed wideband chirp sources using the fractional Fourier transform. The model in the time domain and that in the fractional Fourier domain derived by the Taylor series expansion are presented respectively. The representation of location vector in the Dechirping domain is illustrated which is only related to the central angle. A novel direction of arrival (DOA) estimation algorithm in the Dechirping domain is proposed, which is extended from the conventional multiple signal classification (MUSIC) algorithm in the time domain. Using this algorithm, except for estimating the DOA, the incidence source number can be determined as well which is allowed to exceed the sensor number in the array. To demonstrate the performance of proposed algorithm, numerical results are conducted. Compared to the previous FrFT-MUSIC algorithm based on the assumption of point source model, the proposed algorithm performs a better estimation performance, especially for large angular spread and low signal-to-noise ratio.

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“…Therefore, the researchers considered a more realistic signal model called spatially distributed source model. Depending on the nature of scattering, distributed sources have been classified into two types: coherently and incoherently distributed (CD and ID) sources [5,6]. For ID source model contrary to the CD case, the rank of the noise-free covariance matrix is different to the number of sources.…”

Section: Introductionmentioning

confidence: 99%

An efficient central DOA tracking algorithm for multiple incoherently distributed sources

Hassen

Samet

2015

EURASIP J. Adv. Signal Process.

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In this paper, we develop a new tracking method for the direction of arrival (DOA) parameters assuming multiple incoherently distributed (ID) sources. The new approach is based on a simple covariance fitting optimization technique exploiting the central and noncentral moments of the source angular power densities to estimate the central DOAs. The current estimates are treated as measurements provided to the Kalman filter that model the dynamic property of directional changes for the moving sources. Then, the covariance-fitting-based algorithm and the Kalman filtering theory are combined to formulate an adaptive tracking algorithm. Our algorithm is compared to the fast approximated power iteration-total least square-estimation of signal parameters via rotational invariance technique (FAPI-TLS-ESPRIT) algorithm using the TLS-ESPRIT method and the subspace updating via FAPI-algorithm. It will be shown that the proposed algorithm offers an excellent DOA tracking performance and outperforms the FAPI-TLS-ESPRIT method especially at low signal-to-noise ratio (SNR) values. Moreover, the performances of the two methods increase as the SNR values increase. This increase is more prominent with the FAPI-TLS-ESPRIT method. However, their performances degrade when the number of sources increases. It will be also proved that our method depends on the form of the angular distribution function when tracking the central DOAs. Finally, it will be shown that the more the sources are spaced, the more the proposed method can exactly track the DOAs.Keywords: Tracking DOAs, Incoherently distributed sources, Covariance fitting technique, Kalman filter Review IntroductionThe most commonly considered system model in the direction of arrival (DOA)-finding techniques is the point source model where the signals are assumed to be generated from far-field point sources [1][2][3][4]. However, in real surroundings, especially in modern wireless communication systems, local scattering in the source vicinity causes angular spreading. Therefore, the researchers considered a more realistic signal model called spatially distributed source model. Depending on the nature of scattering, distributed sources have been classified into two types: coherently and incoherently distributed (CD and ID) sources [5,6]. For ID source model contrary to the CD case, the rank of the noise-free covariance matrix is different to the number of sources. Moreover, it increases with the angular spread. Therefore, traditional subspace-based methods become not applicable in this case. To deal with *Correspondence: sonia.benhassen@yahoo.fr 1 Tunisia Polytechnic School, B.P. 743-2078, La Marsa, Tunisia Full list of author information is available at the end of the article this problem, efforts have been directed to specifically design new techniques to estimate the angular parameters for ID sources. Some of them that are able to handle a single ID source were developed in [7][8][9][10][11][12][13]. Many other estimators were also developed to estimate the angular parame...

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Bearing estimation for a distributed source: modeling, inherent accuracy limitations and algorithms

Cited by 77 publications

References 19 publications

Detection of Distributed Sources Using Sensor Arrays

Detection of Distributed Sources Using Sensor Arrays

Separation and localization of multiple distributed wideband chirps using the fractional Fourier transform

An efficient central DOA tracking algorithm for multiple incoherently distributed sources

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