Abstract
The existing angle-based localization methods are mainly suitable for the single source. Actually, there often exists situation which contains multiple target sources. To solve the problem of localization of multitarget sources, this paper presents a K-means clustering method based on multiple screening, which can effectively realize the localization of multiple sources based on DOA (direction of arrival) parameters. The method firstly establishes a cost function of position coordinates by using DOA parameters from the measuring position coordinates and then solves the cost function to obtain a complete set of real position coordinates and fuzzy position coordinates. As the distribution of real target coordinates is concentrated and the fuzzy target positions are scattered, the K-means clustering method is adopted to classify the coordinate set. In order to improve the positioning accuracy, a multiscreening process is introduced to screen the input samples before each clustering, and it can be finally concluded that clustering centers are the position coordinates of the target sources. Meanwhile, the complexity analysis and performance verification of this method are proposed. Simulation experiments show that this method can efficiently realize ambiguity-free, highly precise localization of multitarget sources.
Highlights
E commonly used parameters mainly include time of arrival (TOA), time delay of arrival (TDOA), direction of arrival (DOA), and Doppler shift parameters [1,2,3]. e DOA parameter estimation is mainly based on array signal processing methods. e array signal processing methods have high accuracy, and they are simple to implement [4,5,6,7]
In order to improve the least square method, [16,17] give a double least square position method, namely, the Chan method, which can obtain noniterative closed-form solutions, but it is sensitive to parameter errors and has poor performance under a low signal-to-noise ratio (SNR). e Taylor-series expansion method uses the Taylor series criterion to convert the nonlinear cost function into the linear form, and the target source coordinates can be obtained through the iterative processes. e authors of [18,19,20,21] introduced the general steps of Taylor series expansion position methods and analyzed the performance. e Taylor series expansion method can obtain better positioning accuracy through approximation and iteration, but it is sensitive to the initial values
In order to solve the DOA-based multisource location, this paper presents a multiscreening K-means clustering localization method. is method firstly establishes a cost function of the source position according to the coordinates of each observation position and DOA parameters and solves the cost function to obtain a complete set of the source position coordinates
Summary
As each of the two observation positions determines a true source position; that is, C2Q common positions in the complete set are the true locations of the target sources, so each cluster center can only retain C2Q closest coordinate positions and discard the rest. In this way, the final results of the output clustering centers are the D target source coordinates. Recalculate the Euclidean distances between each sample coordinate in the complete set and the new clustering centers, reserving the coordinate positions of the smallest C2Q Euclidean distances as the new input data and reclustering with K-means method. For the sake of measuring the computational complexity conveniently, the number of iterations is set to Υ. e complexity of the iteration process is mainly concentrated on the calculation of Euclidean
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