Abstract
Direct position determination (DPD) is currently a hot topic in wireless localization research as it is more accurate than traditional two-step positioning. However, current DPD algorithms are all based on uniform arrays, which have an insufficient degree of freedom and limited estimation accuracy. To improve the DPD accuracy, this paper introduces a coprime array to the position model of multiple non-circular sources with a moving array. To maximize the advantages of this coprime array, we reconstruct the covariance matrix by vectorization, apply a spatial smoothing technique, and converge the subspace data from each measuring position to establish the cost function. Finally, we obtain the position coordinates of the multiple non-circular sources. The complexity of the proposed method is computed and compared with that of other methods, and the Cramer–Rao lower bound of DPD for multiple sources with a moving coprime array, is derived. Theoretical analysis and simulation results show that the proposed algorithm is not only applicable to circular sources, but can also improve the positioning accuracy of non-circular sources. Compared with existing two-step positioning algorithms and DPD algorithms based on uniform linear arrays, the proposed technique offers a significant improvement in positioning accuracy with a slight increase in complexity.
Highlights
Passive localization technology applied to wireless signals is widely used in navigation, logistics management, smart homes, and the Internet of Things [1,2]
The first is two-step location determination, which estimates location parameters, such as the angle and time delay, by constructing a mathematical model, and obtains position coordinates using these parameters [3,4]. This is presented in an overview of two-step localization techniques [5], which provides a review of various fundamental methods, current trends, and state-of-the-art systems and algorithms employed in wireless position estimation
This paper mainly focuses on direct position determination (DPD) research, using a coprime omnidirectional antenna array
Summary
Passive localization technology applied to wireless signals is widely used in navigation, logistics management, smart homes, and the Internet of Things [1,2]. Several papers have described the use of Bayesian learning to estimate the delay and angle with a single array based on coprime array models [20,21] These methods demonstrate the idea of two-step location determination, but suffer from high complexity and low estimation precision. To improve the estimation accuracy for a moving array, this paper describes a coprime array technique for DPD, improves the SDF algorithm using the large aperture and high DOF of coprime arrays, and realizes high estimation precision of multiple sources. This algorithm is suitable for CS, but can significantly improve the positioning precision of NS.
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