In this paper, using the mutual information, we bridge the probability density function and the minimum mean-square error (MMSE) between the observed data and the desired signal, and then employ the MMSE to construct ...
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ISBN:
(纸本)9781849190107
In this paper, using the mutual information, we bridge the probability density function and the minimum mean-square error (MMSE) between the observed data and the desired signal, and then employ the MMSE to construct an MMSE-based MDL criterion for accurate source enumeration. The presented numerical results demonstrate that the proposed method is superior to the existing MDL methods in detection performance.
The maximum likelihood (ML) and maximum a posteriori (MAP) estimation techniques are widely used to address the direction-of-arrival (DOA) estimation problems, an important topic in sensor array processing. Convention...
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ISBN:
(纸本)9781479999897
The maximum likelihood (ML) and maximum a posteriori (MAP) estimation techniques are widely used to address the direction-of-arrival (DOA) estimation problems, an important topic in sensor array processing. Conventionally the ML estimators in the DOA estimation context assume the sensor noise to follow a Gaussian distribution. In real-life application, however, this assumption is sometimes not valid, and it is often more accurate to model the noise as a non-Gaussian process. In this paper we derive an iterative ML as well as an iterative MAP estimation algorithm for the DOA estimation problem under the spherically invariant random process noise assumption, one of the most popular non-Gaussian models, especially in the radar context. Numerical simulation results are provided to assess our proposed algorithms and to show their advantage in terms of performance over the conventional ML algorithm.
By simultaneously transmitting and receiving mul- tiple coded waveforms with multiple-input multiple-output (MIMO) configuration,the MIMO radar appears more at- tractive than the traditional phased-array radar in perf...
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ISBN:
(纸本)9781457713675
By simultaneously transmitting and receiving mul- tiple coded waveforms with multiple-input multiple-output (MIMO) configuration,the MIMO radar appears more at- tractive than the traditional phased-array radar in perfor- ***,when applied in the MIMO radar system,the classical subspace-based methods for high-resolution DOA estimation are computationally prohibited as the observed covariance matrix is the Krvnecker product of the transmit and rcccivc covariancc matriccs,considcrably incrcasing the sizc of the covariance *** cure this problem,a computationally efficient subspace-based method for DOA estimation is ad- dressed in this *** proposed method only needs vector- vector operations,and does not involve the covariance matrix calculation and its EVD or inversion ***,the proposed method is computationally attractive for practical *** results are included to illustrate the performance of the proposed method.
In this paper, a new compressive sensing (CS)-based direction of arrival (DOA) estimation technique using the Dantzig selector is proposed. The proposed scheme can identify more source signals than the number of senso...
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ISBN:
(纸本)9781509063901
In this paper, a new compressive sensing (CS)-based direction of arrival (DOA) estimation technique using the Dantzig selector is proposed. The proposed scheme can identify more source signals than the number of sensors used, without requiring an a priori knowledge of the number of source signals to be estimated and without any constraint or assumption about the nature of the signal sources using a fewer number of snapshots. The performance of the proposed scheme is compared to that of the Zhang penalty-based algorithm and the MVDR A-LASSO DOA estimation technique. The computational complexity of the proposed algorithm is substantially lower than that of the Zhang penalty-based method or MVDR A-LASSO.
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