The direction of arrival estimation algorithm for low-angle targets with MIMO radar is discussed, and a novel peaks searching algorithm according to the multipath received signal model is derived. The proposed algorithm, which uses the multipath echo signal power and MIMO radar spatial diversity, can avoid the effects of complex multipath transmitter variable and targets glint. It is illustrated that the algorithm has a better performance with the increase of multipath variable or the transmit antennas. Simulation results verify the usefulness of the algorithm.
Degrees of freedom and mutual coupling are two important factors to determine the ability of direction of arrival (DOA) estimation. Although coprime planar array (CPPA) has less mutual coupling than hourglass array (HA) and ladder array (LA), its positive and negative covariance corresponding lags are discontinuous, resulting in low degrees of freedom. In this study, we propose a generalized and symmetric coprime planar array (GSCA) by employing the symmetric property of the difference co-array. By moving one subarray and introducing a supplementary subarray, we construct a connected virtual uniform rectangular array (URA). Through further optimizing the sensor number, the redundant lags are reduced and the array structure is more general. We show that the degrees of freedom are much higher than those of CPPA, HA and LA. Besides, the mutual coupling is close to CPPA, but less than HA and LA. The superiority of GSCA is verified by simulations.
Here, a high-precision mutual coupling coefficient estimation method is proposed that is more suitable for adaptive beamforming than traditional algorithms. According to the relationship between the designed transition matrix and the signal, the proposed algorithm selects the transition matrix corresponding to the high-power signal. The high-precision estimation of the mutual coupling coefficient is obtained by using the selected transition matrix estimation, which yields relatively good estimation accuracy for the mutual coupling coefficient when the desired signal-to-noise ratio (SNR) is low and relatively robust adaptive beamforming with unknown mutual coupling. Simulation results demonstrate the validity of the proposed method.
This paper addresses the problem of the two-dimensional direction-of-arrival (2D DOA) estimation of low-elevation or non-low-elevation targets using L-shaped uniform and sparse arrays by analyzing the signal models’ features and their mapping to 2D DOA. This paper proposes a 2D DOA estimation algorithm based on the dilated convolutional network model, which consists of two components: a dilated convolutional autoencoder and a dilated convolutional neural network. If there are targets at low elevation, the dilated convolutional autoencoder suppresses the multipath signal and outputs a new signal covariance matrix as the input of the dilated convolutional neural network to directly perform 2D DOA estimation in the absence of a low-elevation target. The algorithm employs 3D convolution to fully retain and extract features. The simulation experiments and the analysis of their results revealed that for both L-shaped uniform and L-shaped sparse arrays, the dilated convolutional autoencoder could effectively suppress the multipath signals without affecting the direct wave and non-low-elevation targets, whereas the dilated convolutional neural network could effectively achieve 2D DOA estimation with a matching rate and an effective ratio of pitch and azimuth angles close to 100% without the need for additional parameter matching. Under the condition of a low signal-to-noise ratio, the estimation accuracy of the proposed algorithm was significantly higher than that of the traditional DOA estimation.
Aiming at the problem of the small aperture of the traditional MIMO radar with virtual degrees of freedom, this paper designs a high degree of freedom space-limited MIMO radar. Both the transmitting and receiving elements of this radar adopt a sparse array structure. Array composition, the receiving array element is composed of a single array element and a uniform linear array. The number of virtual array elements can be realized by using array elements. Compared with the traditional sparse array MIMO radar with the same number of elements, the designed space-limited sparse array MIMO radar has a larger aperture. Experimental simulations verify the superiority of the space-limited MIMO radar angle estimation.
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