Improved Noise Modeling with Mutual Coupling in Receiving Antenna Arrays for Direction-of-Arrival Estimation

Niow, C. H.; Hui, Hon Tat

doi:10.1109/twc.2012.020812.111580

Cited by 11 publications

(6 citation statements)

References 17 publications

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“…where Z is the number of snapshots and where ( ) In order to provide a preliminary assessment of the performances of the proposed method, a planar square array of 25 = × = (4) has been computed in order to evaluate the effectiveness of the method. Fig.…”

Section: Mathematical Formulation and Illustrative Numerical Exammentioning

confidence: 99%

“…The estimation of the directions-of-arrival (DoAs) of signals impinging on an antenna array is a topic of great interest in many research areas, like electromagnetic, seismic/geophysical and acoustic sensing [1]- [4]. The knowledge of the DoAs allows the localization of the corresponding sources and leads to the possibility of reconfiguring the transmitting/receiving system in order to suppress the effect on interfering signals and/or enhancing the sensitivity of the system toward desired directions.…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic direction of arrival estimation through Bayesian compressive sensing

Carlin

Rocca

Oliveri

et al. 2014

The 8th European Conference on Antennas and Propagation (EuCAP 2014)

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In this paper a class of recently introduced Bayesian approaches for the solution of directions-of-arrival (DoAs) estimation problems is illustrated and discussed. More in detail, the problem of the estimation of the DoAs of electromagnetic narrow-band signals impinging on an antenna array is addressed in this paper by means of a strategy based on the Bayesian Compressive Sensing (BCS). Firstly, the relation between the data (i.e. the measured voltages) and the unknown DoAs is formulated as a linear relationship with sparse solution. Successively, the problem is solved by applying the BCS in order to estimate the most probable sparse solution fitting the measured input data. Preliminary numerical examples aimed at assessing the effectiveness of the considered techniques are presented.

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Section: Mathematical Formulation and Illustrative Numerical Exammentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Probabilistic direction of arrival estimation through Bayesian compressive sensing

Carlin

Rocca

Oliveri

et al. 2014

The 8th European Conference on Antennas and Propagation (EuCAP 2014)

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“…However, in many applications, the additive noises in each channel are correlated with each other, i.e., the noise becomes coloured. This coloured noise model is reasonable in practice since the noise can be coupled due to the internal circuitry of the antenna elements [10,11] or the prevailing external noise environment such as reverberation noise in sonar or external seismic noise [12][13][14][15][16]. Since high-order statistics of wireless communication signals can suppress coloured Gaussian noise and potentially extend the effective array aperture, they have been investigated extensively for DOA estimation in the presence of unknown coloured noise [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

“…2. In [6][7][8][9][10][11], only the temporal and spatial white Gaussian noise is considered, while in this paper, we study the JAFE in a more general case, i.e., the temporal and spatial coloured Gaussian noise, and propose an FOC-based estimator to jointly determine DOA, as well as frequency robust to white or coloured noise.…”

Section: Introductionmentioning

confidence: 99%

Efficient Cumulant-Based Methods for Joint Angle and Frequency Estimation Using Spatial-Temporal Smoothing

et al. 2019

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Most non-Gaussian signals in wireless communication array systems contain temporal correlation under a high sampling rate, which can offer more accurate direction of arrival (DOA) and frequency estimates and a larger identifiability. However, in practice, the estimation performance may severely degrade in coloured noise environments. To tackle this issue, we propose real-valued joint angle and frequency estimation (JAFE) algorithms for non-Gaussian signals using fourth-order cumulants. By exploiting the temporal correlation embedded in signals, a series of augmented cumulant matrices is constructed. For independent signals, the DOA and frequency estimates can be obtained, respectively, by leveraging a dual rotational invariance property. For coherent signals, the dual rotational invariance is constructed to estimate the generalized steering vectors, which associates the coherent signals into different groups. Then, the coherent signals in each group can be resolved by performing the forward-backward spatial smoothing. The proposed schemes not only improve the estimation accuracy, but also resolve many more signals than sensors. Besides, it is computationally efficient since it performs the estimation by the polynomial rooting in the real number field. Simulation results demonstrate the superiorities of the proposed estimator to its state-of-the-art counterparts on identifiability, estimation accuracy and robustness, especially for coherent signals.

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“…In [12], a decoupled method for 2D direction of arrival estimation in the presence of the elevation-dependent mutual coupling was proposed for the compact uniform circular arrays (UCAs) based on the rank reduction theory. In [13], a very simple but effective MUSIC DOA estimation algorithm was introduced by decoupling the antenna mutual coupling in the coupled noise component with the assumption that the uncoupled noise power can be determined a priori and can be removed from the array received power. In [14], the mutual-coupling problems in transmitting and receiving antenna arrays were revisited.…”

Section: Introductionmentioning

confidence: 99%

CC-MUSIC: An Optimization Estimator for Mutual Coupling Correction of L-Shaped Nonuniform Array with Single Snapshot

Hou

Zhang

2015

Mathematical Problems in Engineering

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For the case of the single snapshot, the integrated SNR gain could not be obtained without the multiple snapshots, which degrades the mutual coupling correction performance under the lower SNR case. In this paper, a Convex Chain MUSIC (CC-MUSIC) algorithm is proposed for the mutual coupling correction of the L-shaped nonuniform array with single snapshot. It is an online self-calibration algorithm and does not require the prior knowledge of the correction matrix initialization and the calibration source with the known position. An optimization for the approximation between the no mutual coupling covariance matrix without the interpolated transformation and the covariance matrix with the mutual coupling and the interpolated transformation is derived. A global optimization problem is formed for the mutual coupling correction and the spatial spectrum estimation. Furthermore, the nonconvex optimization problem of this global optimization is transformed as a chain of the convex optimization, which is basically an alternating optimization routine. The simulation results demonstrate the effectiveness of the proposed method, which improve the resolution ability and the estimation accuracy of the multisources with the single snapshot.

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Improved Noise Modeling with Mutual Coupling in Receiving Antenna Arrays for Direction-of-Arrival Estimation

Cited by 11 publications

References 17 publications

Probabilistic direction of arrival estimation through Bayesian compressive sensing

Probabilistic direction of arrival estimation through Bayesian compressive sensing

Efficient Cumulant-Based Methods for Joint Angle and Frequency Estimation Using Spatial-Temporal Smoothing

CC-MUSIC: An Optimization Estimator for Mutual Coupling Correction of L-Shaped Nonuniform Array with Single Snapshot

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