A computationally efficient method for jointly estimating both Directions Of Arrival (DOA) and ranges of near field sources is presented. The proposed algorithm does not need any spectral peak searching and the 2-D pa...A computationally efficient method for jointly estimating both Directions Of Arrival (DOA) and ranges of near field sources is presented. The proposed algorithm does not need any spectral peak searching and the 2-D parameters are automatically paired. It is suitable for arbitrary additive Gaussian noise environment. Furthermore, its performances are confirmed by computer simulations.展开更多
High-Order Cumulants （HOC） and cross-correlation was combined to suppress the Gaussian color noises and the tin-related noises in real applications. The cross-HOC TOA estimation model was developed based on the diag...High-Order Cumulants （HOC） and cross-correlation was combined to suppress the Gaussian color noises and the tin-related noises in real applications. The cross-HOC TOA estimation model was developed based on the diagonal slice of the forth-cross-cumu-lant. The eigen analysis was carried out, and the eigea noise space and the eigen signal space was achieved. Then the Frequency Domain TOA estimation algorithm based on Cross-HOC was developed. Different simulation experiments were carried out to draw out the conclusions.展开更多
Compared with the rank reduction estimator(RARE) based on second-order statistics(called SOS-RARE), the RARE based on fourth-order cumulants(referred to as FOC-RARE) can handle more sources and restrain the negative i...Compared with the rank reduction estimator(RARE) based on second-order statistics(called SOS-RARE), the RARE based on fourth-order cumulants(referred to as FOC-RARE) can handle more sources and restrain the negative impacts of the Gaussian colored noise. However, the unexpected modeling errors appearing in practice are known to significantly degrade the performance of the RARE. Therefore, the direction-of-arrival(DOA) estimation performance of the FOC-RARE is quantitatively derived. The explicit expression for direction-finding(DF) error is derived via the first-order perturbation analysis, and then the theoretical formula for the mean square error(MSE) is given. Simulation results demonstrate the validation of the theoretical analysis and reveal that the FOC-RARE is more robust to the unexpected modeling errors than the SOS-RARE.展开更多
Compared to the rank reduction estimator （RARE） based on second-order statistics （called SOS-RARE）, the RARE employing fourth-order cumulants （referred to as FOC-RARE） is capable of dealing with more sources and...Compared to the rank reduction estimator （RARE） based on second-order statistics （called SOS-RARE）, the RARE employing fourth-order cumulants （referred to as FOC-RARE） is capable of dealing with more sources and mitigating the negative influences of the Gaussian colored noise. However, in the presence of unexpected modeling errors, the resolution behavior of the FOC-RARE also deteriorate significantly as SOS-RARE, even for a known array covariance matrix. For this reason, the angle resolution capability of the FOC-RARE was theoretically analyzed. Firstly, the explicit formula for the mathematical expectation of the FOC-RARE spatial spectrum was derived through the second-order perturbation analysis method. Then, with the assumption that the unexpected modeling errors were drawn from complex circular Gaussian distribution, the theoretical formulas for the angle resolution probability of the FOC-RARE were presented. Numerical experiments validate our analytical results and demonstrate that the FOC-RARE has higher robustness to the unexpected modeling en＇ors than that of the SOS-RARE from the resolution point of view.展开更多
基金Supported in part by Trans-Century Trainning Programme Foundation for the Talents by the State Education Commission and the National Natural Science Foundation of China (No.60172028)
文摘A computationally efficient method for jointly estimating both Directions Of Arrival (DOA) and ranges of near field sources is presented. The proposed algorithm does not need any spectral peak searching and the 2-D parameters are automatically paired. It is suitable for arbitrary additive Gaussian noise environment. Furthermore, its performances are confirmed by computer simulations.
文摘High-Order Cumulants （HOC） and cross-correlation was combined to suppress the Gaussian color noises and the tin-related noises in real applications. The cross-HOC TOA estimation model was developed based on the diagonal slice of the forth-cross-cumu-lant. The eigen analysis was carried out, and the eigea noise space and the eigen signal space was achieved. Then the Frequency Domain TOA estimation algorithm based on Cross-HOC was developed. Different simulation experiments were carried out to draw out the conclusions.
基金Project(61201381) supported by the National Natural Science Foundation of ChinaProject(YP12JJ202057) supported by the Future Development Foundation of Zhengzhou Information Science and Technology College,China
文摘Compared with the rank reduction estimator(RARE) based on second-order statistics(called SOS-RARE), the RARE based on fourth-order cumulants(referred to as FOC-RARE) can handle more sources and restrain the negative impacts of the Gaussian colored noise. However, the unexpected modeling errors appearing in practice are known to significantly degrade the performance of the RARE. Therefore, the direction-of-arrival(DOA) estimation performance of the FOC-RARE is quantitatively derived. The explicit expression for direction-finding(DF) error is derived via the first-order perturbation analysis, and then the theoretical formula for the mean square error(MSE) is given. Simulation results demonstrate the validation of the theoretical analysis and reveal that the FOC-RARE is more robust to the unexpected modeling errors than the SOS-RARE.
基金Project(61201381)supported by the National Nature Science Foundation of ChinaProject(YP12JJ202057)supported by the Future Development Foundation of Zhengzhou Information Science and Technology College,China
文摘Compared to the rank reduction estimator （RARE） based on second-order statistics （called SOS-RARE）, the RARE employing fourth-order cumulants （referred to as FOC-RARE） is capable of dealing with more sources and mitigating the negative influences of the Gaussian colored noise. However, in the presence of unexpected modeling errors, the resolution behavior of the FOC-RARE also deteriorate significantly as SOS-RARE, even for a known array covariance matrix. For this reason, the angle resolution capability of the FOC-RARE was theoretically analyzed. Firstly, the explicit formula for the mathematical expectation of the FOC-RARE spatial spectrum was derived through the second-order perturbation analysis method. Then, with the assumption that the unexpected modeling errors were drawn from complex circular Gaussian distribution, the theoretical formulas for the angle resolution probability of the FOC-RARE were presented. Numerical experiments validate our analytical results and demonstrate that the FOC-RARE has higher robustness to the unexpected modeling en＇ors than that of the SOS-RARE from the resolution point of view.
