Computationally Efficient DOA Estimation Algorithm for MIMO Radar With Imperfect Waveforms

Computationally Efficient DOA Estimation Algorithm for MIMO Radar with Imperfect Waveforms

Abstract: Orthogonal waveforms are often desirable to multiple-input multiple-output (MIMO) radar systems. Unfortunately, the orthogonality may not be always guaranteed in practice. In this Letter, we consider the direction-of-arrival (DOA) estimation problem in colocated MIMO radar with imperfect waveforms, and a new methodology is presented. The noiseless cross-covariance matrix is obtained by utilizing the spatial cross correlation technique. DOAs are obtained via reduceddimension multiple signal classification (RD-MUSIC). In contrast to the state-ofthe-art matrix completion (MC) algorithm, the proposed RD-MUSIC method is computationally more efficient. Also, it may has estimation performance more accuracy than the existing MC approach. Numerical results show the improvement of the proposed scheme.

Existing system: Besides, some interests have been paid on array geometry for improved DOA estimation performance, and more and more attentions have been focused on massive MIMO configuration. It should be noted that the superior performances of

these algorithms can be achieved with the orthogonal waveforms assumption. In practical applications, however, the orthogonality assumption may not be always guaranteed. Non orthogonal waveforms will result in corrupted direction matrix as well as spatially colored noise, thus the existing DOA estimation algorithms may fail to work. To the best of our knowledge, only a few literatures focus on this problem in MIMO radar. In, a pre-processing approach was proposed, in which the waveform correlation matrix is utilized to pre whiting the array measurement.

Unfortunately, this assumption may be occasionally violated and yields decreased DOA estimation performance.

Proposed system: In, an improved ESPRITlike method was presented, in which the tensor structure of the array measurement is explored. However, the tensor decomposition is also inefficient and the ESPRIT idea will loss the effective array aperture, thus the estimation accuracy may decreased. In this Letter, a two-step methodology is proposed for DOA estimation in colocated MIMO radar with non-orthogonal waveforms. In the first step, a spatial cross-correlation framework is presented for de-noising. In the second step, a reduced dimension MUSIC (RD-MUSIC) algorithm is proposed for DOA estimation. The proposed method is much more efficient than the MC algorithm, and it provides more accurate DOA estimation performance with large number of antenna configuration. Besides, the stochastic Cram´er-Rao bound (CRB) on DOA estimation with correlated waveforms is derived. Finally, the effectiveness of the proposed method is verified via numerical simulations.

Advantages: In this Letter, a two-step methodology is proposed for DOA estimation in colocated MIMO radar with non-orthogonal waveforms. In the first step, a spatial cross-correlation framework is presented for de-noising. In the second step, a reduced dimension MUSIC (RD-MUSIC) algorithm is proposed for DOA estimation.

The proposed method is much more efficient than the MC algorithm, and it provides more accurate DOA estimation performance with large number of antenna configuration. Besides, the stochastic Cram´er-Rao bound (CRB) on DOA

estimation with correlated waveforms is derived. Finally, the effectiveness of the proposed method is verified via numerical simulations.

Disadvantages: It should be noted that the superior performances of these algorithms can be achieved with the orthogonal waveforms assumption. In practical applications, however, the orthogonality assumption may not be always guaranteed. Non orthogonal waveforms will result in corrupted direction matrix as well as spatially colored noise, thus the existing DOA estimation algorithms may fail to work.

To the best of our knowledge, only a few literatures focus on this problem in MIMO radar. In, a pre-processing approach was proposed, in which the waveform correlation matrix is utilized to prewriting the array measurement. Unfortunately, this assumption may be occasionally violated and yields decreased DOA estimation performance.

Modules: DIRECTION-of-arrival (DOA): Estimation is one of the most important tasks in colocated multiple-input multipleoutput (MIMO) radar that has drawn massive attention. Many efforts have been devoted and various excellent estimators have been put forward, e.g., subspacebased algorithms (such as multiple signal classification (MUSIC), estimation of signal parameters via rotational invariance techniques (ESPRIT)), sparsity-aware estimators and tensor-based estimators. Besides, some interests have been paid on array geometry for improved DOA estimation performance, and more and more attentions have been focused on massive MIMO configuration. It should be noted that the superior performances of these algorithms can be achieved with the orthogonal waveforms assumption. In practical applications, however, the orthogonality assumption may not be always guaranteed. Non orthogonal waveforms will result in corrupted direction matrix as well as spatially colored noise, thus the existing DOA estimation algorithms may fail to work. To the best of our knowledge, only a few literatures focus on this problem in MIMO radar.

Matrix complex:

For instance, the matching processing is usually carried out via fast Fourier transform, which requires the received signal be sampled and matched with digital sequences. Usually, there exists fitting error between analogy waveform and its digital form. Matching the received signal with such sequences will incur such case. In order to avoid the drawback in, a matrix completion (MC) algorithm was presented. The noisy-free covariance matrix is estimated via MC and the DOAs are obtained by exploiting the ESPRIT-like strategy. Although the MC algorithm does not require the prior information of the transmitted waveforms, the de-noising process is computationally costly. In, an improved ESPRITlike method was presented, in which the tensor structure of the array measurement is explored.

However, the tensor decomposition is also inefficient and the ESPRIT idea will loss the effective array aperture, thus the estimation accuracy may decreased.

Cram er – rao bound : In this Letter, a two-step methodology is proposed for DOA estimation in colocated MIMO radar with non-orthogonal waveforms. In the first step, a spatial cross-correlation framework is presented for de-noising. In the second step, a reduced dimension MUSIC (RD-MUSIC) algorithm is proposed for DOA estimation. The proposed method is much more efficient than the MC algorithm, and it provides more accurate DOA estimation performance with large number of antenna configuration. Besides, the stochastic Cram´er-Rao bound (CRB) on DOA estimation with correlated waveforms is derived. Finally, the effectiveness of the proposed method is verified via numerical simulations. The following notations will be used in this Letter. Vectors and matrices are denoted with lower case and capital letters in bold, respectively.