Abstract
Recently, the design of sparse linear array for direction of arrival (DOA) estimation of non-circular (NC) signals has attracted great attention because the difference and sum co-array offered by non-circularity increases the aperture of virtual linear array. In this paper, we present a coprime array with shifted and flipped sub-array for the DOA of non-circular signals. By shifting one sub-array, the proposed array structure achieves a higher number of consecutive lags than the prototype coprime array with the same number of sensors. Then, through flipping the shifted sub-array with the zero point as the symmetry point, the number of sensor pairs with small separation is significantly reduced, making the resulting structure much sparser. For the proposed array structure, we derive the closed-form expression for the number of consecutive lags, the optimal distribution of two sub-arrays with a given number of sensors and the weight function. Numerical simulations are conducted to verify the superiority of the proposed array over the existing sparse arrays.
Highlights
Direction-Of-Arrival (DOA) estimation is one of the important research fields in array signal processing [1]–[3]
direction of arrival (DOA) ESTIMATION IN THE PRESENCE OF MUTUAL COUPLING In the second set of simulations, we present the multiple signal classification (MUSIC) spectra of the six kinds of sparse linear arrays (SLAs) to compare the capability of distinguishing sources in presence of heavy mutual coupling, and simulate the root mean square error (RMSE) of these arrays against SNR, FIGURE 5
In this paper, a new coprime array structure, termed as coprime array with shifted and flipped sub-array, is proposed which provides a higher number of consecutive lags than the prototype coprime array (PCA) with the same number of sensors by shifting the subarray
Summary
Direction-Of-Arrival (DOA) estimation is one of the important research fields in array signal processing [1]–[3]. Comparing to the SPNA and ANA, it has a sparser structure and a higher DOFs. the above sparse arrays are designed using the covariance matrix without considering the NC characteristic of signal. In [31], the improved coprime array is designed by flipping one sub-array of a coprime array with zero point It reduces mutual coupling for the DOA estimation of NC signals. It does not increase the DOFs compared with the prototype coprime array, so that further improvement is possible. The mutual coupling effect between the sensors with close distance cannot be neglected in practice We incorporate it into the received signal vector as follows, x(t) = CA s(t) + n(t),. We propose the coprime array with shifted and flipped sub-array (CASFS) with a much sparser structure than SCA, reducing the effect of mutual coupling
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