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In this article, we focus on addressing the problem of DOD, DOA, and Doppler Frequency estimation with co-prime MIMO radar. Traditional spatial-temporal smoothing methods suffer from high operational complexity and poor algorithm timeliness when overcoming the inability of a single snapshot virtual signal to be eigenvalue decomposed due to the rank loss problem. This drawback is particularly evident in the process of joint multi-dimensional parameter estimation. We first perform co-prime sampling on the received signal based on the co-prime array and then vectorize the covariance matrix to obtain a single snapshot virtual signal. After removing the redundant information from the individual snapshot signals, we obtain equivalent multi-sampled snapshot signals by means of three iterative Toeplitz matrix reconstructions. Finally, the improved multi-dimensional estimation of signal parameters via rotational invariance technique (ESPRIT) algorithm is applied to obtain the directions of departure (DODs), directions of arrival (DOAs) and Doppler frequency of the targets. Through various simulations, it is confirmed that the algorithm in this paper has the advantages of low complexity, good timeliness and high accuracy of parameter estimation.

In this study, the authors consider the problem of direction of arrival (DOA) estimation in compressive sensing multiple-input and multiple-output (CS-MIMO) radars with co-prime receive arrays. A sparse aperture completion scheme is proposed to fill the ‘holes’ that in the difference co-array, achieving the full virtual receive aperture. Structured measurement matrices are devised in order that the output data of match filters can be seen as space–time compressed signal of a virtual uniform linear array. By employing the space–time recovery scheme, the sparse target scene can be accurately recovered while the amount of samples is further reduced. Numerical results demonstrate that the proposed scheme can achieve accurate DOA estimation with space–time compressed data and outperform the CS-based difference co-array methods.

In this study, the authors consider the problem of direction of arrival (DOA) estimation in compressive sensing multiple-input and multiple-output (CS-MIMO) radars with co-prime receive arrays. A sparse aperture completion scheme is proposed to fill the 'holes' that in the difference co-array, achieving the full virtual receive aperture. Structured measurement matrices are devised in order that the output data of match filters can be seen as space–time compressed signal of a virtual uniform linear array. By employing the space–time recovery scheme, the sparse target scene can be accurately recovered while the amount of samples is further reduced. Numerical results demonstrate that the proposed scheme can achieve accurate DOA estimation with space–time compressed data and outperform the CS-based difference co-array methods. [ABSTRACT FROM AUTHOR]

• The proposed co-prime MIMO radar model is based on the transmit energy focusing technique, which improves the signal-to-noise ratio (SNR) gain in the desired sector. • The transmit beamspace matrix with a specific structure is designed to ensure the number of DOFs is not sacrificed. • The CRB of the DOA estimation for the TEF-based co-prime MIMO radar has a lower bound than the traditional co-prime MIMO radar. In this paper, we propose a novel co-prime multiple-input multiple-output (MIMO) radar model based on the transmit energy focusing (TEF) technique for direction of arrival (DOA) estimation. Compared with omnidirectional radiation of the transmit energy of the traditional co-prime MIMO radar, the proposed model can focus the transmit energy in the desired spatial sector. To obtain more degrees of freedom (DOFs), we design the transmit beamspace matrix with a specific structure. Then, the sum and difference co-array (SDC) is given by vectorizing the sampling covariance matrix of the received data. Subsequently, the maximum number of consecutive lags and unique lags for SDC is derived. Besides, we also derive the Cramer–Rao bound (CRB) of our model. Moreover, the DOA estimation performance of the TEF-based co-prime MIMO radar and traditional co-prime MIMO radar is compared by using forward-backward spatial smoothing (FBSS), sparse recovery (SR), and fast iterative interpolated beamformer (FIIB) algorithms. Simulation results show the superiority of the proposed model. [ABSTRACT FROM AUTHOR]