Angle Estimation Algorithm And Angle Glint Suppression Technology Of MIMO Radar

Multiple-input multiple-output (MIMO) radar has been a hot topic in the radar field recently. It uses waveform diversity to improve the radar system performances. According to the spatial distribution of antennas, MIMO radar can be categorized into two types, MIMO radar with collocated antennas (collocated MIMO radar) and that with widely separated antennas (statistical MIMO radar). The collocated MIMO radar can form a virtual aperture, increase the degrees of freedom, and offer better parameter estimation and interference and jamming suppression. Statistical MIMO radar enjoys spatial diversity to against RCS fluctuation and angle glint, and it can improve target detection and angle estimation performance. The problems of angle estimation of MIMO radar with collocated array antennas and angle glint suppression of widely MIMO radar are discussed herein. The main works are summarized as follows:1、High precision angle estimation for collocated MIMO radar(1) Angle estimation of coherent sources in non-ideal noise for monostatic MIMO radar. The geometry of two sub-receiver arrays is proposed. The non-ideal noise is eliminated by exploiting spatial limited correlation of noise. A two-dimensional (2D) forward spatial smoothing algorithm is utilized to recovery the full rank of covariance matrix. And a2D DOA estimation algorithm is addressed based on propagator method. The proposed method avoids the angle-ambiguity and has a good performance.(2) Joint direction of departure (DOD) and direction of arrive (DOA) estimation method of multi-target for extended-aperture bistatic MIMO radar. The transmit and receive array elements are composed of mated element antennas, respectively. The internal mated elements spacing is no more than a half-wavelength and the adjacent paired elements spacing farther apart than a half-wavelength to support extended-aperture. The DOA matrix method is employed to obtain the highly accurate but ambiguous DOD and DOA estimations and the low accurate but unambiguous reference DOD and DOA estimations. The high accurate DOD and DOA estimations are extracted from the ambiguous DOD and DOA estimations resolved by reference DOD and DOA estimations. The algorithm can be paired automatically and increased the target estimation accuracy with no additional elements. The CRB of the MIMO radar arrays is derived, which reveals the performance advantage of the proposed algorithm.2、Low computational complexity angle estimation for bistatic MIMO radar A class of receive-transmit-receive (RTR) method to reduce the dimension of the data covariance matrix is studied. Two typical algorithms, namely RTR-MUSIC and RTR-ESPRIT, are proposed to illuminate this kind of method. The proposed RTR-MUSIC algorithm arranges the isolated data in a matrix form rather than the traditional vector form, greatly reducing the dimension of the covariance matrix. The initial DOA estimations are got by exploiting a1D receive-MUSIC. And then the accurate DOD and DOA estimations are obtained by exploiting two1D transmit-MUSIC and receive-MUSIC algorithms, respectively. Between each of the two MUSIC algorithms, a receive spatial beamforming process and a transmit spatial beamforming process are implemented by orthogonal projection operators. The DOD and DOA estimations are automatically paired because the final covariance matrix contains only one target. The proposed algorithm has low computational complexity which is even lower than that of traditional ESPRIT algorithm and high accuracy. The RTR-ESPRIT method overcomes the shortcoming of the RTR-MUSIC algorithm which cannot directly use ESPRIT. DOD and DOA estimations can be solved in a close form and paired automatically. The RTR-ESPRIT method avoids the one-dimensional search and further reduces the computational complexity. This method is also proved to be applicable to the case of coherent signals and single snapshot, without requiring of smoothing thus avoiding loss of estimation accuracy. The two methods aforementioned have strong applicability and are beneficial for engineering applications.3、High resolution array signal processing methods for bistatic MIMO radar with electromagnetic vector sensors(1) A novel bistatic MIMO radar system with multiple conventional transmit sensors and multiple receive electromagnetic vector sensors is introduced. Based on the system model, two methods based on1D optimal weighted subspace fitting and1D MUSIC are proposed to extend arrive aperture by utilizing the internal structure features of the vector sensors. The two methods are suitable for irregular array geometry, and require neither additional parameter pairing nor mult-dimensional searching. The minimum distance of the receive array is not demanded, so the accuracy of target angle is greatly improved. The Cramer-Rao bound (CRB) of the array is derived, which reveals the performance advantage of the proposed methods.(2) Joint estimations of DOD, DOA, polarization angle and phase difference. Three joint parameter estimation algorithms, namely, four-dimensional (4D) MUSIC, ESPRIT and iterative1D-MUSIC are proposed.4D-MUSIC obtains parameter estimations by usage of the orthogonality between noise subspace and signal subspace, but requires4D search. ESPRIT algorithm has no need to search, but it gets low accuracy. The iterative MUSIC algorithm first uses the internal structure of the vector sensors to obtain a set of initial DOA estimates, and then two1D-MUSIC searches are employed to get the DOD and DOA estimations in succession. Finally, a polarization ESPRIT algorithm is proposed for polarization estimation. The iterative1D-MUSIC algorithm is suitable for irregular array geometry, enjoys low computational complexity due to only1D search, and has high estimation accuracy. And the CRB of joint DOD, DOA and polarization estimation is derived.4、Angle glint suppression for statistical MIMO radar(1) Angle glint suppression method. The target glint deterministic model for the MIMO radar system is set up. And glint probability distribution density is derived. The relationship between the number of transmit antennas and glint suppression performance is discussed. The probability of angle measurement error that falls beyond the target region with fixed number of antennas is deduced. Based on the corresponding theoretics, the angle glint suppression ability of MIMO radar is demonstrated. The negative correlation between the target glint deviation and RCS is proved. Four angle glint suppression methods are proposed. The probability density function of angle glint after RCS linear weighting is deduced.(2) Target tracking with glint noise. An interacting multiple model (IMM) estimator based on extended Kalman filter (EKF) according to glint feature is designed based on the glint statistical model for MIMO radar. The algorithm can take advantage of the MIMO radar spatial diversity gain. Simulation results show that MIMO radar with only2more transmit elements can reduce the tracking RMSE by30.2%compared with conventional radar. Finally, a maneuvering target tracking method named adaptive weighted unscented Kalman filter IMM is presented. The UKF-IMM filters based on the diversity of maneuvering target trajectory with the UKF filter are employed. An adaptive weighted fusion method of multi-observed signal based on Lagrange multiplier rule is derived.