This paper discusses the direction-of-arrival (DOA) estimation problem in multiple-inputmultiple-output (MIMO) radar with non-orthogonal waveforms. Unlike the traditional orthogonal waveforms-based counterparts, spat...
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This paper discusses the direction-of-arrival (DOA) estimation problem in multiple-inputmultiple-output (MIMO) radar with non-orthogonal waveforms. Unlike the traditional orthogonal waveforms-based counterparts, spatially colored noise as well as corrupted virtual direction matrix would appear due to the correlated waveforms in DOA estimation. To tackle these issues, an improved propagator estimator is developed. The spatially colored noise caused by waveform imperfectly is eliminated via cross-correlation technique. Thereafter, DOA is obtained by exploiting the propagator and invariant techniques. The proposed estimator does not involve eigendecomposition of the high dimensional matrix, and it provides closed-form solution for DOA estimation. Compared with the existing frameworks, it is computationally much more economic. Moreover, it offers very close DOA estimation accuracy to the matrix-based methods. Numerical simulations are carried out to show the improvement of the proposed estimator.
Target localization is a fundamental task of multiple-inputmultiple-output (MIMO) radar systems with numerous applications. In this paper, we investigate into the localization problem in a bistatic MIMO radar with el...
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Target localization is a fundamental task of multiple-inputmultiple-output (MIMO) radar systems with numerous applications. In this paper, we investigate into the localization problem in a bistatic MIMO radar with electromagnetic vector sensors (EMVS). Unlike the traditional scaler sensors, an EMVS is able to offer two dimensional (2D) direction finding, and it can provide additional polarization characteristics of the source. Therefore, target localization in bistatic EMVS-MIMO radar system involves 2D direction-of-departure (2D-DOD) and 2D direction-of-arrival (2D-DOA) estimation. Besides, we can obtain transmit polarization characteristics as well as polarization characteristics of the targets. To exploit the tensor nature of the array measurement after matched filters, a tensor subspace algorithm is developed, which estimates the target parameters via cross-product technique from tensor subspace. The proposed algorithm, which obtains closed-form solutions for parameters estimation, shows more accurate performance than the existing algorithm. Numerical simulations verify the effectiveness and improvement of the proposed algorithm.
The frequency diverse array (FDA) radar has drawn great attention due to its range-, angle-, and time-dependent beam-pattern. However, the FDA radar suffers ambiguities in determining target locations, i.e., angle and...
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The frequency diverse array (FDA) radar has drawn great attention due to its range-, angle-, and time-dependent beam-pattern. However, the FDA radar suffers ambiguities in determining target locations, i.e., angle and range of targets are coupled. In this letter, we find that the key to decoupling the target location lies in destroying the mutual dependence of angle and range. Hence, the multiple-frequency continuous wave (MFCW) radar is proposed. The range measurement is obtained by transmitting continuous waves with multiple frequencies and separating the multiple echoes by digital mixer in the receiving end. The range information exists in the phase difference among the multiple echoes, and this process can be seen as sampling in range by multiple frequencies. Then, if a receiving array is used for spatial sampling, an angle measurement is obtained and the target location is decoupled. Four modes of the MFCW radar are derived and the reason for the uncoupled angle-range indication in the beampattern is analyzed in detail. The effectiveness of the proposed scheme is verified by simulation results.
This paper provides an effective method for the joint direction-of-departure (DOD) and direction-of-arrival (DOA) estimation in bistatic multiple-inputmultiple-output (MIMO) radar in the presence of unknown spatial c...
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This paper provides an effective method for the joint direction-of-departure (DOD) and direction-of-arrival (DOA) estimation in bistatic multiple-inputmultiple-output (MIMO) radar in the presence of unknown spatial colored noise. First, a temporal cross-correlation matrix is constructed to eliminate the spatially colored noise, which would not bring virtual aperture loss. To further utilize the inherent multidimensional structure, the covariance matrix is formulated into a quadrilinear decomposition model. Thereafter, an alternating least square algorithm is developed to approximate the loading matrices. Finally, DODs and DOAs can be easily obtained via the least squares fitting method. The proposed scheme can achieve automotive paired DODs and DOAs, and it has much better direction estimation performance than the existing methods. Besides, it does not require singular value decomposition of the array data. Numerical experiments verify the improvement of our scheme.
For multiple-inputmultiple-outputradar employing the advantage of virtual array, the general assumption of the perfect orthogonality between multiple waveforms is proved to be unachievable. Therefore, to achieve good...
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For multiple-inputmultiple-outputradar employing the advantage of virtual array, the general assumption of the perfect orthogonality between multiple waveforms is proved to be unachievable. Therefore, to achieve good orthogonality, transmission schemes employing frequency diversity or chirp modulation diversity have been extensively investigated. Inspired by the idea of "diversity means superiority," approach which integrates multiple diversities in a single waveform might be used to improve the orthogonality. In this paper, a novel waveform named discrete frequency and chirp-rate coding waveform (DFCCW) consisting of both frequency and chirp modulation diversities is proposed. Based on the auto-and cross-ambiguity functions of the DFCCW, we prove that the proposed approach is able to decrease the cross ambiguity peak values. To generate and optimize a set of Pareto-optimal DFCCWs, a many-objective optimization algorithm named nondominated sorting genetic algorithm with differential evolution is presented. Besides, the optimal criteria considering Doppler effects ensure that the DFCCW possess good performance in presence of moving targets. Numerical results show that the DFCCW achieves better orthogonality than the waveforms that utilize only single diversity type.
In multiple-inputmultiple-output (MIMO) radar settings, it is often desired to transmit power only to a given location or sets of locations defined by the beampattern. Some previous works achieve transmit beampattern...
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In multiple-inputmultiple-output (MIMO) radar settings, it is often desired to transmit power only to a given location or sets of locations defined by the beampattern. Some previous works achieve transmit beampattern synthesis by developing new MIMO radar architectures. In these radar architectures, orthogonal waveforms must be utilised. In fact, the perfect orthogonal waveforms are hardly achievable so that the radar performance loss is inevitable. On the other hand, some waveform design methods, which show superior performance, are proposed. However, the modulus variation and high computational complexity impede their applications in reality. In this study, a simple method for transmit beampattern synthesis is proposed. Based on the circulating code principle, the beampattern synthesis problem has been simplified as a single waveform spectral shaping issue. To solve the spectral shaping issue, an extended circulating code (ECC) with constant modulus is provided. The proposed ECC can achieve an arbitrary beampattern with a single constant modulus waveform while the computational complexity is relatively low. Due to these properties, ECC is simple to apply in practice. The method is evaluated by simulated data and the results can verify its effectiveness.
Target positioning using multiple-inputmultiple-output (MIMO) radar system has aroused extensive attention in the past decade. However, most of the existing positioning algorithms are only suitable for ideal scenario...
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Target positioning using multiple-inputmultiple-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.
The existing methods to design orthogonal waveforms for multiple-input multiple-output radar mainly focus on the optimisation of autocorrelation and cross-correlation properties. Their performance will degrade severel...
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The existing methods to design orthogonal waveforms for multiple-input multiple-output radar mainly focus on the optimisation of autocorrelation and cross-correlation properties. Their performance will degrade severely in the presence of Doppler shifts. To overcome this limitation, the authors take an unknown Doppler shifts range into consideration and formulate a new waveform optimisation problem. Since the optimisation problem is highly non-linear, the authors propose an algorithm, called sequential cone programming, to tackle it. The key idea is to use the first-order Taylor expansion to approximate the constraints at each iteration. The authors show that the approximation can be solved via second-order cone programming. In addition, the autocorrelation peak sidelobe level and cross-correlation peak level could be further reduced by setting an appropriate threshold function. Simulation results demonstrate the efficiency of the proposed method compared with state-of-art methods.
multiple-inputmultiple-output (MIMO) radar plays a more and more important role in radar imaging, target detection, and other fields owing to its unique array structure. Transmitting orthogonal signals by different t...
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multiple-inputmultiple-output (MIMO) radar plays a more and more important role in radar imaging, target detection, and other fields owing to its unique array structure. Transmitting orthogonal signals by different transmitters, a MIMO radar can significantly enhance the azimuth resolution. It forms multiple observation channels from different transmit and receive array elements, achieving an equivalent large aperture in azimuth direction. However, the amplitude and phase errors in each channel are introduced, which will lead to inferior imaging performance of the system, especially in azimuth direction. To solve this problem, a method to estimate the amplitude and phase errors of each channel based on a strong scatterer is proposed here. Peak amplitude of one-dimensional range between channel is estimated using constrained least square (CLS) based on ideal point target characteristics. It proved to be valid by a simulation and an imaging experiment.
The problem of DOA estimation for sub-array multiple-input multiple-output radar is concerned. Employing compressive sampling concept and the minimum mean-square error (MMSE) technology, the proposed algorithm alterna...
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The problem of DOA estimation for sub-array multiple-input multiple-output radar is concerned. Employing compressive sampling concept and the minimum mean-square error (MMSE) technology, the proposed algorithm alternates between updating sample covariance matrix and the MMSE filter bank values until convergence. Simplified array manifolds are considered to decrease the computational complexity. The core idea of the algorithm is to determine the DOA by calculating the spatial distribution of signal power adaptively. Simulation results show that the new algorithm performs well both in a wide SNR range and limited samples, compared with the MUSIC, PIAA-APES, and OGSBI algorithms. The most outstanding advantage of the new algorithm is that it can maintain high estimation accuracy under limited samples without knowing the number of targets.
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