Direction Of Departure Research Articles

Taylor expansion MUSIC method for joint DOD and DOA estimation in a bistatic MIMO array

We propose a Taylor expansion multiple signal classification (TE MUSIC) method for joint direction of departure (DOD) and direction of arrival (DOA) estimation in a bistatic multiple-input multiple-output (MIMO) array. First, using a Taylor expansion of the steering vector, a two-dimensional (2D) search in the conventional MUSIC method for MIMO arrays is reduced to a two-step one-dimensional (ID) search in the proposed TE MUSIC method. Second, DOAs of the targets can be achieved via Lagrange multiplier by a ID search. Finally, substituting the DOA estimates into the 2D MUSIC spectrum function, DODs of the targets are obtained by another ID search. Thus, the DOD and DOA estimates can be automatically paired. The performance of the proposed method is better than that of the MIMO ESPRIT method, and is similar to that of the 2D MUSIC method. Furthermore, due to the ID search, the TE MUSIC method avoids the high computational complexity of the 2D MUSIC method. Simulation results are presented to show the effectiveness of the proposed method.

Improvement of DOD/DOA Estimation for Bistatic MIMO Radar

In this paper, we apply the Newton-type method for the optimization of cost functions for spectrum estimation-based bistatic DOD (direction of departure)/DOA (direction of arrival) estimation algorithm of the bistatic conventional beamforming and the bistatic capon beamforming algorithm in the bistatic MIMO radar system. Explicit expressions of the first derivatives and the second derivatives of the cost functions are presented. The Newton-type search for simultaneous update of the DOD/DOA based on the gradient and the Hessian of the cost function is also presented. The performance improvement in terms of estimation accuracy is demonstrated using the Monte–Carlo simulations.

Sparse-Based Millimeter Wave Channel Estimation With Mutual Coupling Effect

The imperfection of antenna array degrades the communication performance in the millimeter wave (mmWave) communication system. In this paper, the problem of channel estimation for the mmWave communication system is investigated, and the unknown mutual coupling (MC) effect between antennas is considered. By exploiting the channel sparsity in the spatial domain with mmWave frequency bands, the problem of channel estimation is converted into that of sparse reconstruction. The MC effect is described by a symmetric Toeplitz matrix, and the sparse-based mmWave system model with MC coefficients is formulated. Then, a two-stage method is proposed by estimating the sparse signals and MC coefficients iteratively. Simulation results show that the proposed method can significantly improve the channel estimation performance in the scenario with unknown MC effect and the estimation performance for both direction of arrival (DOA) and direction of departure (DoD) can be improved by about 8 dB by reducing the MC effect about 4 dB.

Overland movement and migration phenology in relation to breeding of Arctic Terns Sterna paradisaea

It is often implicitly assumed that seabirds migrate using marine environments, but this assumption is increasingly being challenged by electronic tracking data. The arrival and departure routes of Arctic Terns breeding on the North Sea coast of the United Kingdom (UK) are unknown but there has been speculation about the possibility of overland migration. Analysis of light‐level geolocator data from birds breeding on the Farne Islands suggests that these birds arrived and left their North Sea colony overland via the Irish Sea, rather than taking coastal routes along the east coast of the UK and through the English Channel. In addition, some departing birds may enter the North Atlantic by crossing Ireland rather than through the Irish Sea. The direction of arrival in spring had a more southerly orientation than the direction of autumn departure. The geolocator data allow migration phenology in relation to breeding to be defined and indicated that the birds arrived around 15 days before the first eggs were laid in the colony. Departure timing may be determined by seasonal progression and not markedly influenced by breeding success. This study supports the idea that overland migration may be a more widespread and consistent strategy for seabirds than has been realized.

A nested-PARAFAC based approach for target localization in bistatic MIMO radar systems

Bistatic MIMO radar systems gather several advantages such as increased resilience to electronic countermeasures, unknown receiver location, higher identifiability of targets and direct application of several high resolution adaptive techniques. In this paper, we propose a tensor-based method for joint direction of departure (DoD) and direction of arrival (DoA) estimation in bistatic MIMO radar systems. By assuming that the transmit array is divided into two maximally overlapping subarrays, we initially model the cross-covariance matrix of the matched filters outputs as a Nested-PARAFAC decomposition of a fourth-order covariance tensor. Then, exploiting the structure of this decomposition, we first propose a two stage algorithm for joint DoD and DoA estimation of multiple targets based on double alternating least squares (DALS). In addition, for scenarios in which the number of receive antennas exceeds the number of targets, we propose a closed-form solution to the second stage of the proposed method based on the least squares Khatri-Rao factorization (LS-KRF) concept. Simulation results show that the proposed method offers a highly-accurate localization of multiple targets in real-world scenarios where the antenna elements at the transmit and receive arrays have positioning errors as well as less complexity compared to competing state-of-the-art tensor-based solutions.

Low complexity 3D-OMP algorithms for DOD DOA and Doppler frequency estimation in bistatic MIMO radar

ABSTRACTThe biggest challenge of the traditional 3D orthogonal matching pursuit (OMP) method for direction-of-departure (DOD), direction-of-arrival (DOA) and Doppler frequency estimation in bistatic multiple-input multiple-output (MIMO) radar is the heavy computational burden due to a large number of atoms in the overcomplete dictionary. In this paper, low complexity 3D-OMP algorithms are investigated. First, the traditional 3D-OMP algorithm is given. Then, two-dimensionality reduced OMP-based algorithms are proposed exploiting the property of Khatri-Rao product and proper sparse representation. Also, the multiple measurement vectors (MMV) model is introduced to our OMP algorithms to guarantee the estimation performance. The simulation results show that the DOD, DOA and Doppler frequency can be effectively estimated with a small number of pulses and low computation cost. With similar accuracy compared with the traditional 3D-OMP method, much lower computational burden can be achieved by using the proposed methods.

Parameter Estimation Based on Sigmoid Transform in Wideband Bistatic MIMO Radar System under Impulsive Noise Environment.

Since second-order statistics-based methods rely heavily on Gaussianity assumption and fractional lower-order statistics-based methods depend on a priori knowledge of non-Gaussian noise, there remains a void in wideband bistatic multiple-input/multiple-output (MIMO) radar systems under impulsive noise. In this paper, a novel method based on Sigmoid transform was used to estimate target parameters, which do not need a priori knowledge of the noise in an impulsive noise environment. Firstly, a novel wideband ambiguity function, termed Sigmoid wideband ambiguity function (Sigmoid-WBAF), is proposed to estimate the Doppler stretch and time delay by searching the peak of the Sigmoid-WBAF. A novel Sigmoid correlation function is proposed. Furthermore, a new MUSIC algorithm based on the Sigmoid correlation function (Sigmoid-MUSIC) is proposed to estimate the direction-of-departure (DOD) and direction-of-arrival (DOA). Then, the boundness of the Sigmoid-WBAF to the symmetric alpha stable () noise, the feasibility analysis of the Sigmoid-WBAF, and complexity analysis of the Sigmoid-WBAF and Sigmoid-MUSIC are presented to evaluate the performance of the proposed method. In addition, the Cramér–Rao bound for parameter estimation was derived and computed in closed form, which shows that better performance was achieved. Simulation results and theoretical analyses are presented to verify the effectiveness of the proposed method.

Joint DOD and DOA Estimation in Bistatic MIMO Radar With Distributed Nested Arrays

In this paper, we investigate the problem of joint direction of departure (DOD) and the direction of arrival (DOA) estimation in bistatic MIMO radar. A new bistatic MIMO radar with the distributed nested array is proposed. Based on the new array, the DOD and DOA estimation algorithm called automatically paired DR-ESPRIT based on the angle disambiguation algorithm by using the range information of targets is proposed. By designing distributed nested array and using Khatri-Rao product processing, the long baseline and short baseline in the transceiver arrays are virtually extended simultaneously. The non-ambiguous and high-accuracy sine estimations of the DOD and DOA can be accomplished using DR-ESPRIT algorithm and the range information of targets and the geometry of bistatic MIMO radar. The virtual extension of array aperture and high angle estimation accuracy can be achieved for the MIMO radar with automatic pairing and without increasing the number of antennas compared with traditional uniform linear MIMO array. And, the proposed corresponding algorithm gives significant improvement in the DOD and DOA estimation performance. The simulation results validate the theoretical algorithm.

Sparsity-Based Direction-of-Departure and Direction-of-Arrival Estimation for Bistatic Multiple-Input Multiple-Output Radar

This paper provides an efficient method to determine the direction of departure (DOD) and direction of arrival (DOA) in bistatic multiple-input multiple-output (MIMO) radars. The proposed method firstly decouples the DOD and DOA parameters by converting the original received signal model into two separate new signal models. The new signal model corresponding to DOA can be directly obtained by matched filtering operation. In order to obtain the model for DOD, vectorization operation and kronecker transformation are utilized after the matched filtering operation. Both the new signal models for DOA and DOD behave like an augmented signal model of uniform linear array (ULA). Then, a covariance- vector sparsity-aware estimator is developed to find the accurate angular parameter. Meanwhile, in order to improve the estimation accuracy, the additive noise is eliminated by exploiting the toeplitze structure inherent in the array received covariance matrix and the asymptotic distribution of the sampling errors is also derived. Furthermore, the regularization parameter setting used by the proposed estimator is derived with the aid of the Lagrangian duality theory to guarantee the sparsity of solution. Simulation results are conducted to verify the effectiveness and the superiority of the sparsity-based estimator over other methods in terms of the angular estimation accuracy.

Joint DOD and DOA Estimation for Coherently Distributed Sources in Bistatic MIMO Radar Based on Joint Diagonalization

In this paper, an efficient method via unitary joint diagonalization is proposed to deal with the coherently distributed sources in bistatic multiple-input multiple-output (MIMO) radar systems. In order to determine the direction of departure (DOD) and direction of arrival (DOA) of the coherently distributed source, a special array configuration is designed both for transmitter and receiver in bistatic MIMO radar system. Then, a new signal model can be obtained by utilizing the designed array characteristic and the rotational invariance relationship is constructed both for DOD and DOA. For the special rotational invariance relationship, the joint diagonalization technique is adopted to locate the coherently distributed sources and the angular parameters are automatically paired without multidimensional search process. The proposed joint diagonalization method can also determine the targets when multiple imping sources have the common DODs or DOAs. Finally, the approximate determined Cramer-Rao bound of the proposed estimator for the coherently distributed sources is also derived. And, additional numerical results are conducted to verify the effectiveness and superiority of the proposed method.

Transmit Beamspace Design for Bistatic MIMO Radar With Improved Angle Estimation

The energy dispersion of traditional bistatic multiple-input multiple-output (MIMO) radar severely degrades the angle estimation performance. To overcome this limitation, we propose to design the transmit beamspace matrix and apply the design result to jointly estimate the target directions of departure (DOD) and directions of arrival (DOA). The essence of this method is to minimize the distance between a desired transmit steering vector and the actual one within a given angular sector. Moreover, we enforce two inequality constraints to achieve approximately uniform power distribution across the transmit array elements. In addition, we carefully devise the desired transmit steering vector to expand the virtual transmit aperture and further improve the DOD estimation performance. Furthermore, we use tensor-decomposition and a phase-searching method to estimate the target DODs and DOAs. Finally, the simulation results demonstrate the effectiveness of the proposed algorithm.

Computational Efficient DOA, DOD, and Doppler Estimation Algorithm for MIMO Radar

Joint direction-of-arrival (DOA), direction-of-departure (DOD), and Doppler estimation for multiple-input multiple-output (MIMO) radar usually are too time-consuming to implement because of high-dimensional searching procedures. This letter proposes a computational efficient algorithm for MIMO radar joint DOA, DOD, and Doppler estimation, which adopts an unstructured maximum likelihood method to sequentially estimate the Doppler, DOD, and DOA by three one-dimensional searching procedures based on the extended invariance principle. Numerical results validate the effectiveness together with its superiorities over conventional maximum likelihood algorithms.

Fast PARAFAC Algorithm for Target Localization in Bistatic MIMO Radar in the Co-Existence of Unknown Mutual Coupling and Spatially Colored Noise

Target positioning using multiple-input multiple-output (MIMO) radar system has aroused extensive attention in the past decade. However, most of the existing positioning algorithms are only suitable for ideal scenarios (e.g., well-calibrated sensors, orthogonal waveforms, Gaussian white noise). In this paper, we focus on the target localization problem in a bistatic MIMO system in the co-existence of mutual coupling and spatially colored noise, i.e., to estimate the direction-of-arrival (DOA) and direction-of-departure (DOD) in such non-ideal scenario. To tackle this issue, a parallel factor (PARAFAC) analysis algorithm is proposed. Firstly, the de-noising operation is carried out to suppress the spatially colored noise. Then a PARAFAC analysis model is constructed to explore the tensor nature of measurement. After computing PARAFAC decomposition on the new tensor, the factor matrices associate with DOD and DOA are obtained. Thereafter, the de-coupling operation is followed to eliminate the mutual coupling. Finally, the idea of least squares is utilized to recovery DOD and DOA from the factor matrices. The proposed algorithm can achieve closed-form solution for DOD and DOA estimation without pairing calculation. Moreover, it provides better estimation performance than the state-of-the-art approaches. Detailed analyses are illustrated and simulation results verify the effectiveness of the proposed algorithm.

Grid-Free DOD and DOA Estimation for MIMO Radar via Duality-Based 2D Atomic Norm Minimization

Atomic norm minimization (ANM) has recently become a powerful tool for gridless compressed sensing (CS). In this paper, the issue of joint estimation of direction-of-departure (DOD) and direction-of-arrival (DOA) for bistatic multiple-input-multiple-output (MIMO) radar is investigated via two dimensional (2D) ANM. However, a major problem of the primal 2D-ANM is that the direct conversion of 2D-ANM into its semi-definite programming (SDP) problem is not strictly established theoretically and is just an approximation, which results in a decline in estimation performance. Besides, the primal 2D-ANM is limited to a single measurement vector (SMV) model. We propose a duality-based 2D-ANM algorithm for grid-free DOD and DOA estimation in MIMO radar, in which the 2D-ANM problem is effectively solved over its optimal variables in the dual-domain with SDP. Thus it retains the benefits of 2D-ANM and holds in theory. Also, it is applicable for SMV as well as multiple measurement vectors (MMV) models and appropriate for non-uniform linear arrays. The simulation results show that the proposed algorithm avoids the grid mismatch effect in DOD and DOA estimation in contrast to the conventional CS methods, and is robust to target correlation and the single-snapshot environment in comparison with the traditional subspace methods.

Sparse Bayesian Approach for DOD and DOA Estimation With Bistatic MIMO Radar

This study addresses the problem of joint direction-of-departure (DOD) and direction-of-arrival (DOA) estimation with bistatic multiple-input multiple-output (MIMO) radar. To the best of our knowledge, a limited number of sparse Bayesian learning (SBL)-based methods exist that can be applied to joint DOD and DOA estimation. This is because of the heavy computational load and strong correlation between the nearby basis. To overcome these challenges, we present a new coarse non-uniformly sampled 2D grid and propose an improved SBL-based method for joint estimation of the DOD and DOA in MIMO radar. With the new grid, the computational load can be significantly reduced, and the nearby 2D grid points can provide a low correlation basis. To handle the modeling error derived from the coarse grid, we also introduce a modified linear approximation method into the SBL framework in which the locations of grid points are considered as adjustable parameters, and the grid points can be updated recursively. Finally, a block majorization-minimization algorithm is applied to perform Bayesian inference. Experimental results indicate that our method can improve the joint DOD and DOA estimation performance, particularly in the case of low signal-noise-ratio, limited snapshots, or correlated signals.

Joint DOD and DOA Estimation for Bistatic MIMO Radar: A Covariance Trilinear Decomposition Perspective

The covariance methods exert an effect on spatially colored (correlated) noise elimination during direction finding in multiple-input multiple-output (MIMO) radar. However, most of the existing methods seem difficulty to achieve a good balance between accuracy and efficiency. This paper aims at formulating a covariance trilinear decomposition perspective for direction-of-departure (DOD) and direction-of-arrival (DOA) estimation for bistatic MIMO radar. First, the array covariance matrix model is presented for de-noising. Furthermore, the noiseless covariance matrix is rearranged into a trilinear decomposition model. Finally, joint DOD and DOA estimation are linked to trilinear decomposition, which can be easily accomplished by exploiting the existing COMFAC technique. The proposed scheme can exploit the tensor structure of the covariance matrix, and it is attractive from the perspective of computational complexity. Moreover, it can be easily extended to the spatially colored noise scenario. The proposed algorithm is analyzed in terms of identifiability, flexibility, and complexity, and the stochastic Cramer-Rao bound on joint DOD and DOA estimation is derived. The computer simulations verify the effectiveness and improvement of the proposed method.

Transmit beamspace-based DOD and DOA estimation method for bistatic MIMO radar

Abstract In this paper, we propose a novel angle estimation method in bistatic multiple-input multiple-output (MIMO) radar, which combines the transmit beamspace (TB) technique with the unitary ESPRIT (U-ESPRIT) model. The TB matrix is designed to focus the transmitted energy within the desired spatial sector, thus achieving the transmit coherent gain. Due to the special data structure of the TB-based bistatic MIMO radar, a corresponding method is proposed to construct the real-valued model. Unfortunately, the existed interpolation error inevitably degrades the direction-of-departure (DOD) estimation performance. To compensate the DOD estimation error, a look-up table is created, which establishes a one-to-one mapping relationship for DOD. The Cramer-Rao bound (CRB) on angle estimation in TB-based bistatic MIMO radar is also derived for performance analysis. Compared with existing methods, the proposed algorithm has better estimation performance due to the improved signal-to-noise ratio (SNR) gain and requires less computational complexity. Numerical simulations are presented to demonstrate the estimation performance of the proposed algorithm.

Sparse DOD/DOA Estimation in a Bistatic MIMO Radar With Mutual Coupling Effect

The unknown mutual coupling effect between antennas significantly degrades the target localization performance in the bistatic multiple-input multiple-output (MIMO) radar. In this paper, the joint estimation problem for the direction of departure (DOD) and direction of arrival (DOA) is addressed. By exploiting the target sparsity in the spatial domain and formulating a dictionary matrix with discretizing the DOD/DOA into grids, compressed sensing (CS)-based system model is given. However, in the practical MIMO radar systems, the target cannot be precisely on the grids, and the unknown mutual coupling effect degrades the estimation performance. Therefore, a novel CS-based DOD/DOA estimation model with both the off-grid and mutual coupling effect is proposed, and a novel sparse reconstruction method is proposed to estimate DOD/DOA with updating both the off-grid and mutual coupling parameters iteratively. Moreover, to describe the estimation performance, the corresponding Cramér–Rao lower bounds (CRLBs) with all the unknown parameters are theoretically derived. Simulation results show that the proposed method can improve the DOD/DOA estimation in the scenario with unknown mutual coupling effect, and outperform state-of-the-art methods.

Effective joint DOA-DOD estimation for the coexistence of uncorrelated and coherent signals in massive multi-input multi-output array systems

This paper deals with the joint direction-of-arrival (DOA) and direction-of-departure (DOD) estimation when the uncorrelated and coherent (i.e., fully correlated) narrowband signals coexist in multiple-input multiple-output (MIMO) array systems. Two new approaches based on weighted subspace fitting and oblique projection for two-dimensional direction estimation, i.e., WSFOPDE and improved WSFOPDE, are proposed. In the WSFOPDE approach, the basic procedure includes three stages. First, the DOA of all signals can be directly acquired by minimizing a reduced-dimensional weighted subspace fitting function. Then, the DOA information of uncorrelated signals are discerned by a classifying indicator; and subsequently, their auto-paired transmit steering vectors with respect to DOD information are derived. Finally, via a new Toeplitz-structured oblique projection, an virtual MIMO array data with only coherent signals remaining is constructed to assist the corresponding auto-paired DOD estimation. In order to promote the accuracy of angle estimation, we also design an improved version. It inherits the above basic procedure and, meanwhile, introduces one-dimensional local DOA spectrum searching to refine the DOA-DOD estimation. Compared with some existing strategies, WSFOPDE and its improved version perform better from the united perspective of computational complexity and estimation accuracy. Numerical simulations verify the advantages and also demonstrate that both can be served as a better alternative to the competitors.

Position Estimation of Automatic-Guided Vehicle Based on MIMO Antenna Array

The existing positioning methods for the automatic guided vehicle (AGV) in the port can not achieve high location precision, Therefore, a novel multiple input multiple output (MIMO) antenna radar positioning scheme is proposed in this paper. The positioning problem for AGV is considered, and the joint estimation problem for direction of departure (DoD) and direction of arrival (DoA) is addressed in the multiple-input multiple-output (MIMO) radar system. With the radar detect the transponder and estimate the DoA/DoD, the relative location between the transponder and the AGV can be obtained. The corresponding Cramér–Rao lower bounds (CRLBs) for the target parameters are also derived theoretically. Finally, we compare the positioning accuracy of the traditional global position system (GPS) with the proposed MIMO radar system. Simulation results show that the proposed method can achieve better performance than the traditional GPS.