Abstract
The problem of direction-of-arrival (DOA) estimation is investigated for co-prime array, where the co-prime array consists of two uniform sparse linear subarrays with extended inter-element spacing. For each sparse subarray, true DOAs are mapped into several equivalent angles impinging on the traditional uniform linear array with half-wavelength spacing. Then, by applying the estimation of signal parameters via rotational invariance technique (ESPRIT), the equivalent DOAs are estimated, and the candidate DOAs are recovered according to the relationship among equivalent and true DOAs. Finally, the true DOAs are estimated by combining the results of the two subarrays. The proposed method achieves a better complexity–performance tradeoff as compared to other existing methods.
Highlights
Direction of arrival (DOA) estimation is a crucial problem in various applications, such as radar, sonar, and wireless communications [1]
The Cramer-Rao bound (CRB) for the co-prime array geometry is given as a benchmark [21]
We have proposed a low-complexity ESPRIT-based DOA estimation method for co-prime arrays
Summary
Direction of arrival (DOA) estimation is a crucial problem in various applications, such as radar, sonar, and wireless communications [1]. Various DOA estimation methods have been studied in uniform linear arrays (ULAs), including multiple signal classification (MUSIC) [2] and estimation of signal parameters via rotational invariance technique (ESPRIT) [3]. In [4], a Khatri-Rao product-based real-valued sparse estimation method is proposed. With respect to the random errors of sensor position, a stochastic framework is established to find the probability density function of the DOA-estimates [5]. Most of the traditional DOA estimation schemes have focused on ULA structure [6], which, is not an optimal array geometry. Sparse array geometry has drawn lots of attention due to its high resolution [7,8,9]
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