We study the problem of angle estimation for a bistatic multiple-input multiple-output (MIMO) radar with unknown mutual coupling and proposed a joint algorithm for angles and mutual coupling estimation with the characteristics of uniform linear arrays and subspaces exploitation. We primarily obtain an initial estimate of DOA and DOD, then employ the local one-dimensional searching to estimate exactly DOA and DOD, and finally evaluate the parameters of mutual coupling coefficients via the estimated angles. Exploiting twice of the one-dimensional local searching, our method has much lower computational cost than the algorithm in (Liu and Liao (2012)), and automatically obtains the paired two-dimensional angle estimation. Slightly better performance for angle estimation has been achieved via our scheme in contrast to (Liu and Liao (2012)), while the two methods indicate very close performance of mutual coupling estimation. The simulation results verify the algorithmic effectiveness of our scheme.
Since multiple-input multiple-output (MIMO) radars exploit multiple antennas to manipulate parallel processing for transmitting diverse waveforms and receiving the reflected signals, several potential advantages over conventional phased-array radars such as angular diversity, flexible beampattern, and more degrees of freedom have been demonstrated in [
In our study, we present a low-complexity joint algorithm for the angles and mutual coupling coefficients estimation in bistatic MIMO radars. By employing the transformation of special matrix, we combine the signal subspace and noise subspace of the received data together to achieve the joint estimation of DOD and DOA. The angles can be automatically obtained via two local one-dimensional searching without the knowledge of mutual coupling, whose coefficients can be easily estimated after the angle estimation.
The remainder of this paper is structured as follows. Section
Bold lower (upper) case symbols denote column vectors or matrices;
We consider a MIMO radar system equipped with
The receive data of the
When each signal received by the
The covariance matrix of the received signal
According to the MUSIC method, the joint estimation of DOD and DOA can be achieved by the peak searching function as follows:
With respect to the (
Taking the
The diagonal elements of
The following matrix
However, the estimation obtained by (
Referring to [
Since
According to ( Estimate the covariance matrix of the received data through Perform eigenvalue decomposition of Compute Obtain the estimation of DOD Obtain the estimation of DOA Obtain the estimation of
The proposed algorithm has lower complexity than the algorithm in [
According to [
By compares and contrasts to [ Our approach can achieve the estimation for automatically paired two-dimensional angles, while the algorithm in [ The proposed algorithm only requires the local one-dimensional searching and hence has much lower complexity, while the algorithm in [ For the performance of angle and mutual coupling estimation, slightly improvement can be verified in our method than the one in [ By fully exploiting both the signal subspace and the noise subspace, our method displays more validity and effectiveness, while the algorithm in [
The Root mean square error (RMSE) of the angle and the RMSE of the mutual coupling coefficients [
Figure
Angle estimation performance of the proposed algorithm with SNR = 10 dB.
We compare the proposed algorithm against the algorithm in [
Angle and mutual coupling estimation performance comparison (
Angle and mutual coupling estimation performance comparison (
Figures
Angle estimation performance with
Angle estimation performance with
Figure
Angle estimation performance with different values of
In this paper, we have presented a low-complexity algorithm for joint angles and mutual coupling estimation in bistatic MIMO radar with uniform linear arrays (ULAs). This method first obtains an initial estimate of DOA and DOD, then employs the local one-dimensional searching to achieve exact estimates for DOA and DOD, and finally estimates mutual coupling coefficients via the estimated angles. Automatically paired two-dimensional angle estimation can be also performed by our proposed algorithm. We have reduced the algorithmic complexity in comparison with that of [
This work is supported by China NSF Grants (60801052) and the Fundamental Research Funds for the Central Universities (NZ2012010, kfjj120115, kfjj20110215).