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The transmit/receive-receive (T/R-R) synergetic High Frequency Surface Wave Radar (HFSWR) has increasingly attracted attention due to its high localization accuracy, but multi-target pairing needs to be performed before localization in multi-target scenarios. However, existing multi-target parameter matching methods have primarily focused on track association, which falls under the category of information-level fusion techniques, with few methods based on detected points. In this paper, we propose a multi-target pairing method with high computational efficiency based on angle information for T/R-R synergetic HFSWR. To be more specific, a dual-receiving array signal model under long baseline condition is firstly constructed. Then, the amplitude and phase differences of the same target reaching two subarrays are calculated to establish the cross-correlation matrix. Subsequently, in order to extract the rotation factor matrices containing pairing information and improve angle estimation performance, we utilize the conjugate symmetry properties of the uniform linear array (ULA) manifold matrix for generalized virtual aperture extension. Ultimately, azimuths estimation and multi-target pairing are accomplished by combining the propagator method (PM) and the ESPRIT algorithm. The proposed method relies solely on angle information for multi-target pairing and leverages the rotational invariance property of Vandermonde matrices to avoid peak searching or iterations, making it computationally efficient. Furthermore, the proposed method maintains superb performance regardless of whether the spatial angles are widely separated or very close. Simulation results validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Beijing University of Technology is the property of Journal of Beijing University of Technology, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this review, we detail the commonality of mathematical intuitions that underlie three numerical methods used for the quantitative description of electron swarms propagating in a gas under the effect of externally applied electric and/or magnetic fields. These methods can be linked to the integral transport equation, following a common thread much better known in the theory of neutron transport than in the theory of electron transport. First, we discuss the exact solution of the electron transport problem using Monte Carlo (MC) simulations. In reality we will go even further, showing the interpretative role that the diagrams used in quantum theory and quantum field theory can play in the development of MC. Then, we present two methods, the Monte Carlo Flux and the Propagator method, which have been developed at this moment. The first one is based on a modified MC method, while the second shows the advantage of explicitly applying the mathematical idea of propagator to the transport problem.

The transmit/receive-receive (T/R-R) synergetic High Frequency Surface Wave Radar (HFSWR) has increasingly attracted attention due to its high localization accuracy, but multi-target pairing needs to be performed before localization in multi-target scenarios. However, existing multi-target parameter matching methods have primarily focused on track association, which falls under the category of information-level fusion techniques, with few methods based on detected points. In this paper, we propose a multi-target pairing method with high computational efficiency based on angle information for T/R-R synergetic HFSWR. To be more specific, a dual-receiving array signal model under long baseline condition is firstly constructed. Then, the amplitude and phase differences of the same target reaching two subarrays are calculated to establish the cross-correlation matrix. Subsequently, in order to extract the rotation factor matrices containing pairing information and improve angle estimation performance, we utilize the conjugate symmetry properties of the uniform linear array (ULA) manifold matrix for generalized virtual aperture extension. Ultimately, azimuths estimation and multi-target pairing are accomplished by combining the propagator method (PM) and the ESPRIT algorithm. The proposed method relies solely on angle information for multi-target pairing and leverages the rotational invariance property of Vandermonde matrices to avoid peak searching or iterations, making it computationally efficient. Furthermore, the proposed method maintains superb performance regardless of whether the spatial angles are widely separated or very close. Simulation results validate the effectiveness of the proposed method.

In recent years, the synthetic aperture radar tomography (TomoSAR) technique is becoming an increasingly hot research topic in the 3-D reconstruction of the structure of urban buildings. The robustness of the many advanced nonparametric spectral estimation methods in use is based on a high number of observations. This letter aims to evaluate the ability of the five nonparametric estimator techniques, including propagator, generalized maximum entropy (GME), minimum norm (MN), singular value decomposition (SVD), and Capon in the separation of scattering contributions with a small number of SAR images. The performance comparison of the employed algorithms is carried out on the simulated and real datasets. The study results show that the novel high-resolution propagator and GME estimators, respectively, based on the partitioning and extrapolation of the covariance matrix, can discriminate the number of scatterers in one azimuth-range resolution and satisfactorily prevent the appearance of undesirable sidelobes. The experimental results on real SAR images acquired by the TerraSAR-X satellite show the proposed propagator spectral estimation can significantly improve the 3-D building reconstruction in the urban environment. Also, the estimated height of the scatters using the propagator technique is similar to the data obtained from ground observations. [ABSTRACT FROM AUTHOR]

Direction of arrival (DOA) estimation for conformal arrays is challenging due to non-omnidirectional element patterns and shadow effects. Conical conformal array (CCA) can avoid the shadow effect at small elevation angles. So CCA is suitable for DOA estimation on both azimuth and elevation angles at small elevation angles. However, the element pattern in CCA cannot be obtained by conventional directional element coordinate transformation. Its local element pattern also has connection with the cone angle. The paper establishes the CCA radiation pattern in local coordinate system using 2-D coordinate transformation. In addition, in the case of large elevation angle, only half elements of the CCA can receive signal due to the shadow effect. The array degrees of freedom (DOF) are reduced by halves. We introduce the difference coarray method, which increases the DOF. Moreover, we propose a more accurate propagator method for 2-D cases. This method constructs a new propagation matrix and reduces the estimation error. In addition, this method reduces computational complexity by using linear computations instead of eigenvalue decomposition (EVD) and avoids spectral search. Simulation and experiment verify the estimation performance of the CCA. Both demonstrate the CCA model established in this paper is corresponding to the designed CCA antenna, and the proposed algorithms meet the needs of CCA angle detection. When the number of array elements is 12, the estimation accuracy is about 5 degrees. [ABSTRACT FROM AUTHOR]

Direction of arrival (DOA) estimation for conformal arrays is challenging due to non-omnidirectional element patterns and shadow effects. Conical conformal array (CCA) can avoid the shadow effect at small elevation angles. So CCA is suitable for DOA estimation on both azimuth and elevation angles at small elevation angles. However, the element pattern in CCA cannot be obtained by conventional directional element coordinate transformation. Its local element pattern also has connection with the cone angle. The paper establishes the CCA radiation pattern in local coordinate system using 2-D coordinate transformation. In addition, in the case of large elevation angle, only half elements of the CCA can receive signal due to the shadow effect. The array degrees of freedom (DOF) are reduced by halves. We introduce the difference coarray method, which increases the DOF. Moreover, we propose a more accurate propagator method for 2-D cases. This method constructs a new propagation matrix and reduces the estimation error. In addition, this method reduces computational complexity by using linear computations instead of eigenvalue decomposition (EVD) and avoids spectral search. Simulation and experiment verify the estimation performance of the CCA. Both demonstrate the CCA model established in this paper is corresponding to the designed CCA antenna, and the proposed algorithms meet the needs of CCA angle detection. When the number of array elements is 12, the estimation accuracy is about 5 degrees.

For scenarios of coherent underwater sources at low signal-to-noise ratio (SNR), a novel quaternion-based DOA algorithm without eigendecomposition is proposed using a linear vector-hydrophone array. We construct four quaternion models by judiciously arranging the received data to fully utilize the statistical information of the incident signals. To avoid the high computational complexity caused by the eigenvalue decomposition (EVD), we introduce the computationally efficient propagator method (PM) to estimate the elevation angles of the observed signals. In the quaternion algebra framework, we statistically eliminate the additive noise, which makes the PM method exhibit a robust performance in low SNR. By fully exploiting the direction information embedded in the velocity components, we achieve a high-resolution two-dimensional (2-D) DOA estimation result with a linear vector-hydrophone array. The simulations demonstrate that the proposed method offers stable estimation performance compared with the existing non-quaternion schemes without the need for any pair matching between the estimated azimuth and elevation angles.

A frequency diverse array (FDA) multiple‐input multiple‐output (MIMO) radar employs a small frequency increment across transmit elements to produce an angle‐range‐dependent beampattern for target angle and range detection. The joint angle and range estimation problem is a trilinear model. The traditional trilinear alternating least square (TALS) algorithm involves high computational load due to excessive iterations. We propose a fast‐convergence trilinear decomposition (FC‐TD) algorithm to jointly estimate FDA‐MIMO radar target angle and range. We first use a propagator method to obtain coarse angle and range estimates in the data domain. Next, the coarse estimates are used as initialized parameters instead of the traditional TALS algorithm random initialization to reduce iterations and accelerate convergence. Finally, fine angle and range estimates are derived and automatically paired. Compared to the traditional TALS algorithm, the proposed FC‐TD algorithm has lower computational complexity with no estimation performance degradation. Moreover, Cramér‐Rao bounds are presented and simulation results are provided to validate the proposed FC‐TD algorithm effectiveness.

A frequency diverse array (FDA) multiple‐input multiple‐output (MIMO) radar employs a small frequency increment across transmit elements to produce an angle‐range‐dependent beampattern for target angle and range detection. The joint angle and range estimation problem is a trilinear model. The traditional trilinear alternating least square (TALS) algorithm involves high computational load due to excessive iterations. We propose a fast‐convergence trilinear decomposition (FC‐TD) algorithm to jointly estimate FDA‐MIMO radar target angle and range. We first use a propagator method to obtain coarse angle and range estimates in the data domain. Next, the coarse estimates are used as initialized parameters instead of the traditional TALS algorithm random initialization to reduce iterations and accelerate convergence. Finally, fine angle and range estimates are derived and automatically paired. Compared to the traditional TALS algorithm, the proposed FC‐TD algorithm has lower computational complexity with no estimation performance degradation. Moreover, Cramér‐Rao bounds are presented and simulation results are provided to validate the proposed FC‐TD algorithm effectiveness. [ABSTRACT FROM AUTHOR]

In this paper, we investigated into the problem of target location in a bistatic multiple-input multiple-output (MIMO), whose transmit antennas and receive antennas are electromagnetic vector sensors (EMVS). Unlike the traditional linear scaler-sensor array, a linear EMVS array can offer two-diemensional (2D) direction estimation, thus a bistatic EMVS-MIMO radar provides (2D) direction-of-arrival and 2D direction-of-departure estimation. Besides, it is able to estimate 2D transmit/receive polarization angles of the targets. An propagator method (PM)-based estimator is proposed. Firstly, it estimate the propagator from the covariance matrix. The parameters are achieved via utilizing the estimation method of signal parameters via rotational invariance technique (ESPRIT) and the vector cross-product technique. The proposed estimator is computationally friendly since it does not involving eigendecomposition of high-dimensional data. Also, it may has similar (or even better) parameter estimation accuracy than the current EPSRIT-Like algorithm. Simulation results verify the effectiveness of the proposed PM estimator.

A general predictive controller based on the subspace model identification method is proposed for vehicle stabilization. Traditional predictive controllers are always developed based on the principle model of vehicles, which inevitably suffers from parameter uncertainty and poor adaptability. In contrast to that, the proposed subspace-based general predictive controller is realized by a data-driven process and presents good adaptability in vehicle stability control. Inspired by subspace-based predictor construction, the keys of the predictive controller are as follows: (1) system model identification according to the model structure of the control object by input and output data; (2) output prediction of the system by the identified model; and (3) optimal control law designed by combining the linear–quadratic–Gaussian index with the predictive output. The main problem in the controller development lies in the recursive estimation of relevant matrices, which is limited by the subspace model identification theory. The implementation of the vector autoregressive with exogenous input model and the propagator method in subspace identification algorithm effectively solves the problem of estimation accuracy and calculation efficiency. Combined with a linear–quadratic–Gaussian index function, the predictive law for vehicle stability control is derived in detail. Finally, based on the vehicle model validated by standard road test, the effectiveness and robustness of the predictive controller are proved through the numerical simulations of various maneuvers under different road adhesive conditions. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we employ the geometry of uniform circular array to achieve classification and localization of mixed near-field and far-field sources. Considering that the eigendecomposition of the covariance matrix requires high computational cost, we develop the propagator method to obtain the noise subspace and reduce complexity. Firstly, since the direction parameters of far-field sources at centrosymmetry sensors hold a conjugate structure while the covariance matrix of near-field sources holds a Hermitian structure, we exploit the covariance differencing approach to extract the pure near-field sources from mixed sources. Then, we improve the ESPRIT-like method and one-dimensional MUSIC method to determine the 2-D direction-of-arrival (DOA) and range of near-field sources, respectively. Finally, by calculating the noise power of mixed sources, we utilize the oblique projection approach to extract the pure far-field sources and exploit the 2-D MUSIC method to determine the 2-D DOA of far-field sources. Simulations demonstrate that the proposed algorithm can avoid the pseudo-peaks in the 2-D DOA spatial spectrum of far-field sources and provide the satisfactory performance of mixed source localization. [ABSTRACT FROM AUTHOR]

The problem of two dimensional direction of arrival estimation of coherent signals using three parallel uniform linear arrays is investigated. In the proposed method a pre-processing technique, forward and backward spatial smoothing is employed to resolve the coherency. Then the propagator method is exploited for estimating the two dimensional angles with automatic pairing for three parallel uniform linear arrays. Numerical simulations are done to show the efficacy of the proposed method. It validates the estimation accuracy, resolution and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

We consider the problem of two-dimensional (2D) direction of arrival (DOA) estimation for planar array, and propose a successive propagator method (PM)-based algorithm. The rotational invariance property of the propagator matrix is exploited to obtain the initial angle estimations, while the accurate estimates can be achieved through successive one-dimensional and local spectrum-peak searches. The proposed algorithm can obtain automatically paired 2D-DOA estimations, and it requires no eigenvalue decomposition of the covariance matrix of received data, which remarkably reduces the computational cost compared with traditional 2D-PM algorithm. In addition, the DOA estimation performance of the proposed algorithm is better than estimation of signal parameters via rotational invariance technique (ESPRIT) algorithm and PM algorithm, and is close to 2D-PM algorithm which requires 2D spectrum-peak search. Numerical simulations demonstrate the effectiveness and improvement of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A high-precision vehicle handling dynamic model is of great importance in both the analysis of system performance and development of stability controller. This study introduces a new modeling algorithm based on subspace identification. Different from traditional batchwise subspace identification method, the new modeling method is proposed in the framework of online realization and unbiased estimation, which is ensured by a recursive propagator method (PM) combined with a vector autoregressive with exogenous (VARX) input model. Also, an observable and controllable 4-order linear time-varying (LTV) vehicle handling dynamic model, including rolling motion, is presented. To validate the identification algorithm, real vehicle standard road tests, including the step input test and impulse input test, are conducted, and sampling data are collected to apply into the identification procedure. The feasibility and accuracy of this online identification method are demonstrated. Furthermore, the stability of identification method is validated by an identification process based on road test data with measurement noise interference, and the superiority of the proposed identifiable LTV model is proved compared to the linear time-invariant (LTI) model. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the problem of two-dimensional (2D) direction of arrival (DOA) estimation of multiple signals for generalized coprime planar arrays consisting of two rectangular uniform planar subarrays. We propose a fast-convergence trilinear decomposition approach, which uses propagator method (PM) as the initialization of the angle estimation to speed the convergence of trilinear decomposition. The received signal of each subarray can be fitted into a trilinear model or parallel factor (PARAFAC) model so that the trilinear alternating least square algorithm can be used to estimate the angle information. Meanwhile, the necessary initialization of DOA estimates can be achieved via PM, which endows the proposed approach a fast convergence and subsequently results in a low complexity. Specifically, we eliminate the ambiguous estimates by utilizing the coprime property and the true DOA estimates can be achieved by selecting the nearest ones of all DOA estimates. The proposed approach can obtain the same estimation performance as the conventional PARAFAC algorithm, but with a low computational cost. Numerical simulation results are provided to validate the effectiveness and superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In two-dimensional (2-D) direction-of-arrival (DOA) estimation, paring the azimuth and elevation angles of multiple sources is an important issue. In this letter, we propose a new automatically paired 2-D DOA estimation method by designing the geometry of two antenna subarrays and using the propagator method (PM). A special geometry between two parallel uniform linear arrays (ULAs) with a position displacement on the axial direction is proposed to facilitate the elevation and azimuth pairing and estimation. The simulation results have shown that the proposed method can achieve the same 2-D DOA estimation performance as the existing methods, while the complexity is reduced considerably. [ABSTRACT FROM AUTHOR]

A new direction finding algorithm called conjugate propagator method （COP） for one-dimension signals was proposed. The new algorithm uses the array output and its conjugate form to extend the array aperture so that it can detect more signals than sensors and has better performance （in terms of resolution and angular precision） than OPM （or-thonormal-PM） and MUSIC algorithms. The mean square error （MSE） performance of COP was analyzed and the ana-lytical expression of MSE was given. The computational complexity of COP was analyzed also. Simulation results show the better performance of COP than OPM and MUSIC algorithms and the accordance between the theoretical results and the simulation results.

Copyright of Transactions of Beijing Institute of Technology is the property of Beijing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As L-shaped array can provide good angle estimation performance and is easy to implement, many two-dimensional (2-D) direction-of-arrival (DOA) estimation algorithms have been developed. In this letter, we present a low-computation-complexity DOA estimation method for L-shaped array that exploits the conjugate symmetry property of the array manifold matrix to increase the effective array aperture and improve the angle estimation performance. Hence, the DOA estimation is obtained via combining propagator method (PM) with ESPRIT algorithm, which does not need spectrum peak search or additional angle pair matching procedure. Simulation results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

In this paper, we address the problem of four-dimensional angle and Doppler frequency estimation for L-shaped bistatic multiple input multiple output radar in spatial coloured noise. A novel method of joint estimation of Doppler frequency, two-dimensional direction of departure and two-dimensional direction of arrival based on the propagator method is discussed. Utilising the cross-correlation matrix which is formed by the adjacent outputs of matched filter in the time domain, the special matrix is constructed to eliminate the influence of spatial coloured noise. The proposed algorithm provides lower computational complexity and has very close parameter estimation to the estimation of signal parameters via rotational invariance techniques algorithm and DOA-matrix algorithm in high signal-to-noise ratio and Cramér–Rao bound is given. Furthermore, multidimensional parameters can be automatically paired by this algorithm to avoid the performance degradation resulting from wrong pairing. Numerical simulation results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of joint time of arrival (TOA) and direction of arrival (DOA) estimation in impulse radio ultra-wideband systems with a two-antenna receiver and links the joint estimation of TOA and DOA to the sparse representation framework. Exploiting this link, an orthogonal matching pursuit algorithm is used for TOA estimation in the two antennas, and then the DOA parameters are estimated via the difference in the TOAs between the two antennas. The proposed algorithm can work well with a single measurement vector and can pair TOA and DOA parameters. Furthermore, it has better parameter-estimation performance than traditional propagator methods, such as, estimation of signal parameters via rotational invariance techniques algorithms matrix pencil algorithms, and other new jointestimation schemes, with one single snapshot. The simulation results verify the usefulness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

LOWDIN is a computational program that implements the Any Particle Molecular Orbital (APMO) method. The current version of the code encompasses Hartree-Fock, second-order Møller-Plesset, configuration interaction, density functional, and generalized propagator theories. LOWDIN input file offers a unique flexibility, allowing users to exploit all the programs' capabilities to study systems containing any type and number of quantum species. This review provides a basic introduction to LOWDIN's key computational details and capabilities. © 2013 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

The article presents a study which aims to introduce angle estimation of two dimensional (2D) direction of departure (DOD) and 2D direction of arrival (DOA) for bistatic MIMO radar with propagator method.

This paper presents a fast and high-resolution estimation approach using polarization information combined with angle information for multi-target localization in bistatic multiple-input multiple-output (MIMO) radar. The propagator method (PM) is extended to jointly estimate the direction of departure (DOD), the direction of arrival (DOA) and the polarization parameters. The PM avoids the singular value decomposition (SVD) of the covariance matrix of the received signals so that the computational complexity is reduced. In addition, the closely spaced targets can be well distinguished by polarization diversity. The Cramer-Rao bounds (CRBs) of the estimated parameters are derived. The position of a target is calculated based on the estimated angles. The simulation results demonstrate that the proposed approach can achieve better performance compared with conventional methods of target localization. [ABSTRACT FROM AUTHOR]

This paper discusses the problem of two-dimensional direction of arrival estimation of coherent sources for acoustic vector sensors array. Compared with subspace-based methods, such as root multiple signal classification and estimation of signal parameters via rotational invariance technique, the propagator method (PM) has lower computational complexity. However, only in high-snapshots situation, can the PM algorithm enjoy a better estimation performance. Besides, all of these algorithms mentioned above cannot work for coherent sources. In this paper, we combine PM algorithm with Toeplitz Hermitian matrix reconstruction, and propose an improved algorithm, which works well in the case of coherent signals and a single snapshot. Furthermore, the proposed method can achieve automatically paired two-dimensional angle estimation. Simulation results verify that the proposed method has the better angle performance and less computational complexity than spatial smoothing methods. [ABSTRACT FROM AUTHOR]

This communication presents the hybridized version of recently proposed high resolution “Two-stage separated virtual steering vector-based (SVSV) rank reduction propagator method” (HSVSV) for inverse synthetic aperture radar imaging. HSVSV rearranges the 2D data on virtual array and makes use of SVSV method twice. HSVSV outperformed the SVSV in terms of estimating the positions of target scattering centers lying on horizontal and/or vertical line in target space for signal to noise ratios 0\leSNR\le 17~dB, at the cost of doubling the computational load. The superiority of HSVSV to SVSV algorithm is demonstrated using both simulated and experimental ISAR data. [ABSTRACT FROM PUBLISHER]

Abstract: A computationally efficient propagator and two-stage separated virtual steering vector based algorithm (SVSV) is recently developed by D. Liu and J. Liang for two-dimensional (2D) frequency estimation. SVSV has introduced a novel electric angle z to jointly estimate the 2D components (frequencies or scatterers'' locations) and to avoid the incorrect mismatch. However, SVSV cannot resolve the multiple 2D components having equivalent value of z. This problem limits the use of SVSV in ISAR imaging application. Hence, in this paper, we have proposed a new modified SVSV method called MSVSV for 2D ISAR imaging. Main contributions of our work are: (a) use of the propagator based subspace methods (SVSV and MSVSV) for 2D high resolution ISAR imaging. (b) We have suggested a technique to estimate the number of unknown 2D components prior to calculating the propagator operator from the covariance matrix. (c) We have incorporated a technique, due to which MSVSV can successfully resolve the multiple scatterers sharing the same electric angles, where SVSV fails. Radar images are generated for simulated and experimental target data (collected at AERO, Pakistan) by using SVSV, MSVSV and conventional Fourier Transform methods. [Copyright &y& Elsevier]

Abstracts: In this paper, we study the problem of two-dimensional direction-of-arrival (DOA) estimation with two parallel uniform arrays (ULA). The computational loads of the propagator method (PM) can be significantly smaller since the PM does not require any eigen-value decomposition of the cross correlation matrix and singular value decomposition of the received data. We propose an improved two-dimensional DOA estimation algorithm for two-parallel uniform linear arrays using PM. Compared with Wu''s algorithm, the proposed algorithm has better angle estimation performance, and lower complexity. Furthermore, the proposed algorithm can achieve automatically paired two-dimensional angle estimation. The simulation results verify the effectiveness of the proposed algorithm. [Copyright &y& Elsevier]

In this article, we consider a computationally efficient direction of departure and direction of arrival estimation problem for a bistatic multiple-input multiple-output (MIMO) radar. The computational loads of the propagator method (PM) can be significantly smaller since the PM does not require any eigenvalue decomposition of the cross correlation matrix and singular value decomposition of the received data. An improved PM algorithm is proposed to obtain automatically paired transmit and receive angle estimations in the MIMO radar. The proposed algorithm has very close angle estimation performance to conventional PM, which has a much higher complexity than our algorithm. For high signal-to-noise ratio, the proposed algorithm has very close angle estimation to estimation of signal parameters via rotational invariance technique algorithm. The variance of the estimation error and Cramér–Rao bound of angle estimation are derived. Simulation results verify the usefulness of our algorithm. [ABSTRACT FROM AUTHOR]