Beam space weighted subspace fitting algorithm

The sensor space high resolution Weighted Subspace Fitting (WSF) algorithm is expanded into beam space in this paper. Beam space WSF algorithm uses beam outputs of array which can be regarded as the outputs of an virtual array having the same number of elements as the beam number to estimate target directions. In most underwater acoustic systems, the number of beams used for determining the direction of arrival is usually considerably less than that of the sensors, so the computation burdensome is decedent. Computer simulation results show that the beam space WSF algorithm retains the super performance of the sensor space WSF algorithm when applied to the beam outputs of some practical acoustic-receiving array.

A subspace fitting algorithm of acoustic vector sensor array and corresponding matrix pre-filter design

In order to ease the pass-band response distortion of the matrix pre-filter,a simple approach for designing matrix spatial filter is proposed,which minimizes the sum of the k maximal distortion norm（k is the number of the constraint points）within the pass-band,while constraining the filter response within the stop-band.Considering the costly amount of calculation of the high-resolution methods,an algorithm with small amount of calculation based on matrix pre-filtering and subspace fitting using acoustic vector array（MF-VSSF）is proposed.Through joint processing of signal subspace of both pressure and particle velocity,the pre-filtering matrix and the signal subspace is decreased to M-dimensional（M is the number of array-element）,hence reduces the time-consumption of the matrix pre-filter design and DOA searching.Simulation results show that,the method offers the same performance as MUSIC with pre-filtering,but has much lesser amount of calculation.Moreover,the designed prefilter can efficiently suppress the interference in the stop-band and improve the estimation and resolution performance of successive DOA estimators.

Target parameter estimation for OTFS-integrated radar and communications based on sparse reconstruction preprocessing

Orthogonal time-frequency space(OTFS)is a new modulation technique proposed in recent years for high Doppler wireless scenes.To solve the parameter estimation problem of the OTFS-integrated radar and communications system,we propose a parameter estimation method based on sparse reconstruction preprocessing to reduce the computational effort of the traditional weighted subspace fitting(WSF)algorithm.First,an OTFS-integrated echo signal model is constructed.Then,the echo signal is transformed to the time domain to separate the target angle from the range,and the range and angle of the detected target are coarsely estimated by using the sparse reconstruction algorithm.Finally,the WSF algorithm is used to refine the search with the coarse estimate at the center to obtain an accurate estimate.The simulations demonstrate the effectiveness and superiority of the proposed parameterestimation algorithm.

Doppler distributed clutter model of airborne radar and its parameters estimation

To characterize the clutter spectrum center-shift and spread of airborne radar caused by the platform motion, a novel Doppler Distributed Clutter (DDC) model is proposed to describe the clutter covariance matrix in temporal domain. Based on this parametric model, maximum likelihood, subspace based method and other super- resolution methods are introduced into the Doppler parameters estimation, and more excellent performance is obtained than with the conventional approaches in frequency domain. The theoretical derivation and real experimental results are also provided to validate this novel model and methods of parameter estimating.