Rotational parameters estimation of maneuvering target in ISAR imaging

The rotational parameters estimation of maneuvering target is the key of cross-range scaling of ISAR （inverse synthetic aperture radar）, which can be used in the target feature extraction. The cross-range signal model of rotating target with fixed acceleration is presented and the weighted linear least squares estimation of rotational parameters with fixed velocity or acceleration is proposed via the relationship of cross-range FM （frequency modulation） parameter, scatterers coordinates and rotational parameters. The FM parameter is calculated via RWT （Radon-Wigner transform）. The ISAR imaging and cross-range scaling based on scaled RWT imaging method are implemented after obtaining rotational parameters. The rotational parameters estimation and cross-range scaling are validated by the ISAR processing of experimental radar data, and the method presents good application foreground to the ISAR imaging and scaling of maneuvering target.

Temporal-spatial subspaces modern combination method for 2D-DOA estimation in MIMO radar

A 2D-direction of arrival estimation (DOAE) for multi input and multi-output (MIMO) radar using improved multiple temporal-spatial subspaces in estimating signal parameters via rotational invariance techniques method (TS-ESPRIT) is introduced. In order to realize the improved TS-ESPRIT, the proposed algorithm divides the planar array into multiple uniform sub-planar arrays with common reference point to get a unified phase shifts measurement point for all sub-arrays. The TS-ESPRIT is applied to each sub-array separately, and in the same time with the others to realize the parallelly temporal and spatial processing, so that it reduces the non-linearity effect of model and decreases the computational time. Then, the time difference of arrival (TDOA) technique is applied to combine the multiple sub-arrays in order to form the improved TS-ESPRIT. It is found that the proposed method achieves high accuracy at a low signal to noise ratio (SNR) with low computational complexity, leading to enhancement of the estimators performance.

Low-complexity method for DOA estimation based on ESPRIT

A low-complexity method for direction of arrival（DOA） estimation based on estimation signal parameters via rotational invariance technique（ESPRIT） is proposed.Instead of using the cross-correlation vectors in multistage Wiener filter（MSWF）,the orthogonal residual vectors obtained in conjugate gradient（CG） method span the signal subspace used by ESPRIT.The computational complexity of the proposed method is significantly reduced,since the signal subspace estimation mainly needs two matrixvector complex multiplications at the iteration of data level.Furthermore,the prior training data are not needed in the proposed method.To overcome performance degradation at low signal to noise ratio（SNR）,the expanded signal subspace spanned by more basis vectors is used and simultaneously renders ESPRIT yield redundant DOAs,which can be excluded by performing ESPRIT once more using the unexpanded signal subspace.Compared with the traditional ESPRIT methods by MSWF and eigenvalue decomposition（EVD）,numerical results demonstrate the satisfactory performance of the proposed method.

High Resolution Multipath Time Delay Estimation Based on FLOCCS-ESPRIT

The non-Gaussian α-stable distribution is introduced to model impulsive noise. Based on the theory of fractional lower order statistics (FLOS), the fractional lower order cross-covariance (FLOCC) sequence for two received signals is obtained and the fractional lower order cross-covariance spectrum (FLOCCS) can be approached by taking a Fourier transform for the FLOCC sequence. When the FLOCCS is treated as a sequence in the time domain, the problem of multipath time delay estimation (TDE) may be converted into one on multi-frequencies estimation or directions of arrival estimation. Accordingly, the high resolution multipath TDE can be realized with the ESPRIT technology. This idea on multipath TDE is referred to as FLOCCS-ESPRIT in this paper. Computer simulations show that this method has good performance both in a Gaussian noise and in an impulsive noise environment.

MULTI-INVARIANCE ESPRIT-LIKE ALGORITHMS FOR COHERENT DOA ESTIMATION

Estimation of Signal Parameters via Rotational Invariance Technique(ESPRIT) algorithm can estimate Direction-Of-Arrival(DOA) of coherent signal,but its performance can not reach full satisfaction.We reconstruct the received signal to form data model with multi-invariance property,and multi-invariance ESPRIT algorithm for coherent DOA estimation is proposed in this paper.The proposed algorithm can resolve the DOAs of coherent signals and performs better in DOA estimation than that of ESPRIT-like algorithm.Meanwhile,it identifies more DOAs than ESPRIT-like algorithm.The simulation results demonstrate its validity.

COMPENSATION FOR THE MUTUAL COUPLING EFFECT FOR THE ESPRIT ALGORITHM IN SINGLE SNAPSHOT ARRAY PROCESSING

An effective method is introduced to compensate the effects of mutual coupling for the Estimation of Signal Parameter via Rotational Invariance Techniques (ESPRIT) direction finding algorithm in application of signal snapshot array processing.Changing the covariance matrix into a Teoplitz matrix can achieve high resolution in the Direction Of Arrive (DOA) estimation.How the mutual coupling affects the array antennas has been discussed and a new definition of mutual im- pedance has been used to characterize the mutual coupling effects between the array elements.Based on the new mutual impedance matrix,a practical method is presented to eliminate the effects of mutual coupling for ESPRIT in the single snapshot data processing.The simulation results show that, this new method not only properly reduces the effects of mutual coupling,but also maintains its steady performance even for weak signals.

Angle Estimation in Monostatic MIMO Radar with Low-Complexity Beamspace Unitary ESPRIT

A low-complexity angle estimation method for multiple-input multiple-output(MIMO) radar using beamspace unitary estimation of signal parameters via rotational invariance techniques(ESPRIT) is presented.Reduced-dimensional transformation is firstly utilized as a pre-processing to obtain the reduced-dimensional data matrix, and then a conjugate centrosymmetric discrete Fourier transform(DFT) matrix is employed to map the received data into lower-dimensional beamspace and transforms the complex covariance matrix into a realvalued one. At last, the rotational invariance structure of the real-valued signal subspace is constructed in the beamspace to obtain the estimation of direction of arrival(DOA). Compared with the other ESPRIT algorithms,the proposed method can achieve improved estimation performance with a significantly reduced computational complexity. Simulation results are presented to demonstrate the effectiveness of the proposed method.