Blind Joint Doa and Dod Estimation and Identifiability Results for Mimo Radar With Different Transmit / Receive Array Manifolds

Zhang, Xiaofei; Gao, Xin; Feng, Gaopeng; Xu, and Dazhuan

doi:10.2528/pierb09050603

Cited by 13 publications

(11 citation statements)

References 25 publications

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“…Two signal directions are set to [θ 1 Because of the performance difference between CESA and 2D ESPRIT. The range of SNR in this experiment is different from Experiment 3.…”

Section: Methodsmentioning

confidence: 99%

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Two-Dimensional Doa Estimation Using a Co-Prime Symmetric Cross Array

Liu¹,

Li²,

Wu³

et al. 2014

PIER C

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Abstract-In this paper, a co-prime symmetric sparse cross array is employed to estimate twodimensional (2-D) direction-of-arrival (DOA). Some special forth-order-cumulants (FOCs) of array received data are used to construct a high-order matrix that is equivalent to a cross-covariance matrix based on two uniform linear arrays. After some modifications, an existing 2-D DOA estimation algorithm becomes more effective, and it shows further improved performance when the co-prime symmetric cross array is utilized. Numerical simulations demonstrate the effectiveness of our methods.

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“…Two signal directions are set to [θ 1 Because of the performance difference between CESA and 2D ESPRIT. The range of SNR in this experiment is different from Experiment 3.…”

Section: Methodsmentioning

confidence: 99%

“…Direction of arrival (DOA) estimation of signals by sensor array is common in various applications, such as radar and wireless communication, which has been extensively discussed in the literature [1,2]. For the extendable aperture, the sparse arrays have caused wide concern for decades.…”

Section: Introductionmentioning

confidence: 99%

Two-Dimensional Doa Estimation Using a Co-Prime Symmetric Cross Array

Liu¹,

Li²,

Wu³

et al. 2014

PIER C

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“…Diversity of the TX channels can be achieved by employing time division multiplexing, frequency division multiplexing, spatial coding, and orthogonal waveforms [8]. The virtually formed phased array can be designed to produce the desired pattern by arranging the placements of the TX and RX elements in an appropriate way [9][10][11].…”

Section: Mimo Phased Array Radar System Designmentioning

confidence: 99%

FMCW Based Mimo Imaging Radar for Maritime Navigation

Huang¹,

Brennan²,

Patrick³

et al. 2011

PIER

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Abstract-The berthing of large ships in inclement weather with frequently poor visibility presents a challenge. To assist with this application, it may be beneficial to utilise standard radar imaging. Whilst this may be achieved using a mechanically-scanned system, reliability, cost and weight issues, coupled with the need to primarily image only a 120 • sector on the port and starboard of the ship, make phased array radar an attractive possibility. Multiple-Input MultipleOutput (MIMO) radar, with its ability to enhance the resolution available from a given number of elements, is particularly suited to a short-range application such as this in which there is sufficient time to switch between antenna elements as an alternative to more complex implementations. This paper describes a system of this nature from its basic architecture to development and validation, including some artefacts of the particular topology employed.

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“…Recently, beamforming algorithms using particlefilter [23], neural networks [24], and global optimizations techniques such as PSO [25] and differential evolution (DE) [29] have been proposed. In [25], the authors assume that information regarding the array geometry, signal to noise ratio, directions of arrival of signal and interferences can be obtained exactly by using the methods presented in [26][27][28]. In [29], we proposed a DE based adaptive beamformer which performed well for look direction as well as for a considerable amount of geometry error.…”

Section: Introductionmentioning

confidence: 99%

A Differential Evolution Approach for Robust Adaptive Beamforming Based on Joint Estimation of Look Direction and Array Geometry

Mallipeddi¹,

Lie²,

Suganthan³

et al. 2011

PIER

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Abstract-The performance of traditional beamformers tends to degrade due to inaccurate estimation of covariance matrix and imprecise knowledge of array steering vector.The inaccurate estimation of covariance matrix can be attributed to limited data samples and the presence of desired signal in the training data. The mismatch between the actual and presumed steering vectors can be due to the error in the position (geometry) and/or in the look direction estimate. In this paper, we propose a differential evolution (DE) based robust adaptive beamforming that is able to achieve near optimal performance even in the presence of geometry error. Initially, we estimate an optimal steering vector by maximizing and minimizing the signal power in and out of the desired signal's angular range, respectively. Then, we estimate the look direction and reconstruct the covariance matrix. Based on the obtained steering vector, estimate for look direction and reconstructed covariance matrix, near optimal output SINR, can be obtained with the increase in the input SNR without observing any saturation even in the presence of geometry error. Numerical simulations are presented to demonstrate the efficacy of the proposed algorithm.

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Blind Joint Doa and Dod Estimation and Identifiability Results for Mimo Radar With Different Transmit / Receive Array Manifolds

Cited by 13 publications

References 25 publications

Two-Dimensional Doa Estimation Using a Co-Prime Symmetric Cross Array

Two-Dimensional Doa Estimation Using a Co-Prime Symmetric Cross Array

FMCW Based Mimo Imaging Radar for Maritime Navigation

A Differential Evolution Approach for Robust Adaptive Beamforming Based on Joint Estimation of Look Direction and Array Geometry

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