Multisource Spatiotemporal Tracking Using Sparse Large Aperture Arrays

Kamil, Y.I.; Manikas, A.

doi:10.1109/taes.2017.2665198

Cited by 6 publications

(5 citation statements)

References 37 publications

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“…Exploiting more samples typically results in better tracking performance, especially for multi-target cases [24]. Despite this, it is still possible to use only one sample in each CPI.…”

Section: B Nonlinear Filtering Method: From Estimation To Trackingmentioning

confidence: 99%

“…In Steps 2 and 5 of the EKF, knowledge of the equivalent source signal s i is required, which may be unavailable in practice. However, it can be estimated based on the a priori state estimate qi|i−1 and the observation y o,i [24], which is essentially a signal detection problem under channel estimation error. An example of the EKF performance under imperfect knowledge of s i is shown in Figure 4.…”

Section: B Nonlinear Filtering Method: From Estimation To Trackingmentioning

confidence: 99%

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An effective localization method for mixed far-field and near-field strictly non-circular sources

Wang

Xian

Chen

et al. 2019

Digital Signal Processing

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In this paper, an effective direction-of-arrival (DOA) and range estimations method for mixed far-field and near-field non-circular sources is proposed based on a large centrosymmetric uniform linear array (ULA). By exploiting the noncircularity of the sources, an extended signal is generated by concatenating the received array data and its conjugate counterparts. Then the DOAs of farfield signals are estimated based on the extended covariance matrix with the traditional MUSIC algorithm. After eliminating the far-field components from the extended signal subspace, the extended covariance matrix of the near-field signals is obtained. Thus a near-field estimator is constructed based on symmetric property of the extended array manifold where the generalized ESPRIT method is adopted to estimate the DOAs of near-field sources. Finally, the range estimator is derived using the DOA estimations of near-field sources. Simulation results are provided to validate that the proposed method has achieved a better performance than existing ones and is quite suitable for massive MIMO (multiple-input multiple-out) system.

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“…Exploiting more samples typically results in better tracking performance, especially for multi-target cases [24]. Despite this, it is still possible to use only one sample in each CPI.…”

Section: B Nonlinear Filtering Method: From Estimation To Trackingmentioning

confidence: 99%

Section: B Nonlinear Filtering Method: From Estimation To Trackingmentioning

confidence: 99%

An effective localization method for mixed far-field and near-field strictly non-circular sources

Wang

Xian

Chen

et al. 2019

Digital Signal Processing

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“…In non-stationary environments, the locations of the targets are fast-varying and may need to be tracked on a snapshot-by-snapshot basis, e.g. [1], [2]. However, in stationary environments, the locations of the targets remain unchanged for the observation period.…”

Section: Notationsmentioning

confidence: 99%

MIMO Radar: An H-Infinity Approach for Robust Multitarget Tracking in Unknown Cluttered Environment

Liu,

Manikas

2024

IEEE Trans. Aerosp. Electron. Syst.

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In Multiple-Input Multiple-Output (MIMO) radar, adaptive multi-target tracking can be achieved using subspace tracking algorithms in conjunction with super-resolution subspace localisation algorithms. However, the presence of unknown radar clutter deteriorates or breaks algorithms that rely on the white noise assumption. In this paper, an H ∞ approach is proposed for robust tracking of each target's range, Direction of Arrival (DOA) and velocity with unknown clutter. Specifically, Doppler SpatioTemporal ARray (Doppler-STAR) and virtual Doppler-STAR manifold extenders are first proposed by combining the slow-time and fast-time dimensions of a pulse MIMO radar received signal. Then, in the 'extended' space, H ∞ adaptive filtering is used to track a noise subspace equivalent, which exists regardless of the noise assumption. Finally, the target parameters are extracted from the adaptively tracked noise subspace. Based on computer simulation studies, the performance of the proposed H ∞ tracking approach is evaluated using challenging tracking scenarios and compared against several existing subspace tracking algorithms.

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“…where T = t t 0 (22) is the time elapsed between t and t 0 . If T is assumed to be equal to the sampling period, i.e.…”

Section: A Source/user Mobility Modelmentioning

confidence: 99%

“…In addition to KF, the Extended Kalman filter (EKF) and the Unscented Kalman Filter (UKF) are suitable for the case that the measurement model is non-linear. For instance, the EKF has been employed in [22] for trajectory tracking of moving sources using a large aperture rigid array and in [23] for DOA tracking of moving sources using a small aperture rigid array. In [24], the EKF is combined with a particle filter forming an EKPF algorithm.…”

mentioning

confidence: 99%

Multi Direction-of-Arrival Tracking Using Rigid and Flexible Antenna Arrays

Tang

Manikas

2021

IEEE Trans. Wireless Commun.

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This paper is concerned with the problem of simultaneously tracking the direction-of-arrival (DOA) of far-field multiple moving sources/users in wireless communications using the vector-signal received by an antenna array of N elements. The antenna array can be rigid (fixed array locations) or flexible (time-varying array locations), and it is used in conjunction with a "manifold extender", a spatiotemporal state-space model and a Kalman-type tracking approach for non-stationary wireless channels. In particular, two tracking approaches are proposed. The first is based on an arrayed Extended Kalman Filter (arrayed-EKF) algorithm and the second on an arrayed Unscented Kalman Filter (arrayed-UKF) algorithm. Furthermore, if the array is rigid the spatiotemporal state-space model incorporates the DOAs and the angular velocities of the sources, while if it is flexible it also includes the array locations in the set of statevariables. The performance of the two approaches using both rigid and flexible arrays is evaluated using computer simulation studies and compared with a subspace tracking algorithm and a particle filter method under the same conditions. The results show that the arrayed-UKF and the arrayed-EKF show superior tracking performance, especially for low SNRs. Index Terms-Array processing, flexible array, DOA tracking, extended Kalman filter, unscented Kalman filter, spatiotemporal arrays, array manifolds, extended manifolds Zhuqing Tang (S'17) received the B. Eng. Degree (with Hons.) in Electronic and Communication Engineering from University of Liverpool, U.K., in 2016, and the M.Sc. degree in Communications and Signal Processing from Imperial College London, U.K., in 2017. She is currently pursuing the Ph.D. degree with the Communications and Signal Processing group, Department of Electrical and Electronic Engineering, Imperial College London, U.K., under the supervision of Prof. A. Manikas. Her research interests include array communications, array signal processing, flexible array signal processing, beamforming, and source tracking.

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Multisource Spatiotemporal Tracking Using Sparse Large Aperture Arrays

Cited by 6 publications

References 37 publications

An effective localization method for mixed far-field and near-field strictly non-circular sources

An effective localization method for mixed far-field and near-field strictly non-circular sources

MIMO Radar: An H-Infinity Approach for Robust Multitarget Tracking in Unknown Cluttered Environment

Multi Direction-of-Arrival Tracking Using Rigid and Flexible Antenna Arrays

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