Forward Scatter Radar Using a MIMO Configuration

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.

The problem of tracking the direction of arrivals (DOA) and the direction of departure (DOD) of multiple moving targets is considered in bistatic multiple input multiple output (MIMO) radar. A low complexity angle tracking algorithm in bistatic MIMO radar is proposed. The proposed algorithm derived the difference between previous and current covariance matrices. And the relationship between the different of covariance matrices and the different of target angle is derived. The performance of the proposed algorithm is improved by averaging the covariance matrix elements. Last DOD and DOA are got by least square method. This algorithm avoids the decomposition of the covariance matrix and reduces the computational complexity, which realizes DOA and DOD of the adjacent moment automatic association. The simulation results demonstrate effectiveness of the proposed algorithm. The algorithm provides the technical support for the practical application of MIMO radar.

An improved algorithm for joint direction of departure (DOD) and direction of arrival (DOA) estimation in bistatic MIMO radar based on fourth order cumulants is presented. Firstly, the data of receiver is reset and divided to acquire the rotational invariance property of transmitter and receiver, respectively. The fourth order cumulants matrixes in twain are constructed which are based on the basic definition of the cumulant. Then we use the propagator method (PM), which only requires linear operation but does not involve any eigendecomposition of the cumulant matrix, to estimate the DODs and DOAs, respectively. Finally, the maximum likelihood method is used to solve the pairing problem. The proposed method is effective in prohibiting the Gaussian colored noise and improves the performance of the angle estimation slightly. It does not need two-dimensional spectrum peak searching and eigenvalue decomposition on the cumulant matrix, thus the computation complexity is reduced. At the same time, it has no exceptive claim on the number of receive arrays or receive arrays. Simulation results verify the effectiveness and feasibility of the proposed method.

We study the problem of angle estimation for non-circular sources in bistatic multiple-input multiple-output (MIMO) radar system and propose a low-complexity ESPRIT---Root-MUSIC algorithm for joint direction of departure (DOD) and direction of arrival (DOA) estimation. By taking the non-circularity characteristics of the impinging signals, the received data can be extended, which corresponds to double the number of elements in MIMO radar. Then a novel signal subspace, based on the multistage wiener filter, can be determined directly from the extended data, which does not involve the estimation of covariance matrix and its eigenvalue decomposition (EVD), thus implying that the proposed method is computationally efficient. Finally, the DOD and DOA are estimated by ESPRIT and Root-MUSIC, respectively. Compared with EVD-based algorithms, the proposed method provides the comparable angle estimation performance with lower computational complexity. Some simulation results verify the validity of the proposed method.

Precise modelling of the Direction of Arrival (DoA) and Direction of Departure (DoD) of multipath components (MPCs) in a Multiple-Input Multiple-Output (MIMO) channel based on correlation properties and joint distributions have not been investigated properly. This paper examines the correlation between DoAs and DoDs, and presents a modelling approach particularly for the `general trend' of these dual-spatial domains, based on statistical results obtained from several indoor MIMO measurement campaigns. We show that the Laplacian distribution provides better fits for the directional parameters than the normal and Von Mises distributions. The `general trend' of DoA/DoD joint distribution and power density are modelled using a modified Gumbel's bivariate exponential approach. Results show that the proposed bivariate distribution provides a good match to the real data, and offers an alternative approach to generate realistic directional-based stochastic MIMO channel responses in different environments.

The feasibility of an X-ray Forward Scatter Radar (FSR) system, which exploits X-ray pulsars as opportunity transmitters, is examined. We provide a power budget estimate for detection of large cosmic objects using such a FSR system and assume the use of the high-resolution X-ray telescopes mounted on a satellite as the receivers. The numerical results are obtained for three types of pulsars and small asteroids, whose diameters equal and overcome 50m.

This letter discusses the problem of direction of departure (DOD) and direction of arrival (DOA) estimation for multi-input multi-output (MIMO) radar, and derives a reduced-dimension multiple signal classification (MUSIC) algorithm therein. The proposed algorithm, which only requires one-dimension search, can avoid the high computational cost within two-dimension MUSIC (2D-MUSIC) algorithm. We illustrate that the algorithm has better performance ESPRIT algorithm, and has very close performance to 2D-MUSIC algorithm. Further our algorithm requires no pair matching. Simulation results verify the usefulness of our algorithm.

In bistatic multiple input multiple output (MIMO) radar, more number of detectable incident signals and a higher angle estimation performance can be obtained by using conjugate estimation of signal parameters via rotational invariance techniques (ESPRITs) with the characteristic of noncircularity. The result is achieved under the assumption that all the received signals are noncircular. When the incident signals are the coexistence of noncircular and circular signals, the conjugate ESPRIT will not valuable. Therefore, this paper proposes a method of the joint of direction of departure (DOD) and direction of arrival (DOA) estimation, which is appropriate for the coexistence of noncircular and circular signals in bistatic MIMO radar. First, the received data model of the bistatic MIMO radar is given. Second, we modify the received signal model by the use of noncircularity characteristic. Third, we derive out the equation of spatially rotational invariant containing the DOD and DOA information. Last, we solve the equation to obtain the DOD and DOA by means of total least squares technology. The proposed algorithm has the three advantages. One is that it has better angle estimation accuracy than that of method which does not use the noncircularity characteristic. Another one is that it has more number of detectable incident signals. The last one is that the more number of noncircular signals, the higher angle estimation accuracy will be. Results from numerical experiments are used to show the effectiveness of our proposed algorithm.

An improved algorithm for joint direction of departure (DOD) and direction of arrival (DOA) estimation in bistatic MIMO radar based on fourth order cumulants is presented. Firstly, the data of receiver is reseted and divided to acquire the rotational invariance property of transmitter and receiver, respectively. The fourth order cumulants matrixes in twain are constructed which is based on the basic definition of the cumulant. Then we use propagator method(PM), which requires only linear operation but does not involve any eigendecomposition of the cumulant matrix, to estimate the DODs and DOAs, respectively. Finally, the maximum likelihood method is used to solve the pairing problem. The proposed method is effective in prohibiting the Gaussian colored noise and improves the performance of the angle estimation. It does not need two-dimensional spectrum peak searching and eigenvalue decomposition on the cumulant matrix, thus the computation complexity is reduced. At the same time, it has no claim on the number of receive arrays or receive arrays. Simulation results are presented to verify the effectiveness and feasibility of the proposed method.

Computationally efficient method for joint DOD and DOA estimation of coherent targets in MIMO radar

A novel beamspace ESPRIT (B-ESPRIT) algorithm is proposed to estimate the direction of departures (DODs) and direction of arrivals (DOAs) for bistatic MIMO radar. It restores the rotational invariance structure lost in the beamspace transformation for both the transmit array and the receive array, and then the DODs and DOAs can be estimated through ESPRIT. The proposed algorithm can achieve a significant computational saving over the element-space ESPRIT (E-ESPRIT) algorithm for bistatic MIMO radar. Simulation results validate these conclusions.

Reduced-complexity multiple parameters estimation via toeplitz matrix triple iteration reconstruction with bistatic MIMO radar

The feasibility of the estimation of a target’s coordinates solely from the Doppler data measured by forward-scatter radar systems (RSs) is analyzed. It is shown that the coordinates’ ambiguity is inherent in a bistatic RS with one transmitting antenna and one receiving antenna. This ambiguity is eliminated in a system with one transmitting antenna and two receiving antennas located close to each other. Algorithms for determining the target’s coordinates in this system are proposed. The accuracy of estimating the target’s coordinates is evaluated and compared to that obtained with a bistatic RS that measures the Doppler frequency and a signal’s direction of arrival.

A novel method for the joint estimation of the Direction-Of-Departure (DOD) and Direction-Of-Arrival (DOA) estimation in multiple-input multiple-output (MIMO) array is proposed in this paper. By using the time delay, we can obtain two cross-correlation matrices, where the effect of additive noise is eliminated. Then singular value decomposition (SVD) technique is used to achieve the signal subspace matrix. And an ESPRIT-like method is used to resolve the signal subspace and achieve the estimates of DOAs. Finally, we substitute the DOAs into the MIMO MUSIC spectrum function and do an one-dimensional search to get the estimates of DODs. The proposed method can achieve automatically paired estimations of DOD and DOA. Simulation results are given to prove the rightness of the proposed method.

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.