روش محلی سازی مربعات حداقل محدود
ترجمه نشده

روش محلی سازی مربعات حداقل محدود

عنوان فارسی مقاله: یک روش محلی سازی مربعات حداقل محدود درجه دوم کارآمد برای فضای باریک با اندازه گیری متغیر
عنوان انگلیسی مقاله: An Efficient Quadratic Constrained Least Squares Localization Method for Narrow Space With Ranging Measurement
مجله/کنفرانس: دسترسی – IEEE Access
رشته های تحصیلی مرتبط: مهندسی کامپیوتر
گرایش های تحصیلی مرتبط: مهندسی الگوریتم و محاسبات
کلمات کلیدی فارسی: مربعات حداقل محدود، فضای باریک، محلی سازی مبتنی بر دامنه
کلمات کلیدی انگلیسی: Constrained least-square, narrow space, range-based localization
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus – Master Journals List – JCR
شناسه دیجیتال (DOI): https://doi.org/10.1109/ACCESS.2019.2957402
دانشگاه: School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
ناشر: آی تریپل ای - IEEE
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 4.641 در سال 2018
شاخص H_index: 56 در سال 2019
شاخص SJR: 0.609 در سال 2018
شناسه ISSN: 2169-3536
شاخص Quartile (چارک): Q2 در سال 2018
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 10
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E14083
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

ABSTRACT


I. INTRODUCTION


II. PROBLEM STATEMENT


III. MODEL AND ALGORITHM


IV. NUMERICAL SIMULATIONS AND EXPERIMENTAL TESTS


V. CONCLUSION


REFERENCES

نمونه متن انگلیسی مقاله

ABSTRACT


The localization algorithm for mobile robots working in narrow space needs to handle the scenario that the geometric shape of reference nodes tends to a line, which results in the matrix of least squares localization approaches ill-conditioned. Estimator bias becomes an important factor that can degrade the localization performance. In this paper, we present a fast unbiased range-based localization algorithm to resist the ill-conditioned problem. The main strategy is to augment objective function in the resultant optimization formulations via introducing a measurement distance into the locating model, which forms a least squares problem with cone constrained. The proposed model decouples the measurement distances from the matrix of least squares, which avoids the ill-conditioned problem when the target is around the geometric center. The closed-form expression of locating position ensures that the proposed algorithm is unbiased and low computation burden in the presence of zero-mean disturbance. Moreover, the robustness improvement of the augmented objective function is analyzed. Numerical simulations are used to corroborate the analytic results which demonstrate the good performance, robustness, and fastness of the proposed method.


INTRODUCTION


Indoor localization for autonomous robots becomes an attractive subject with the rapid development and application of the autonomous robots technology [1], [2]. The robots need localization systems to provide position information which is critical and fundamental for the control algorithm. Since the distance information can be sourced from various physical signals, such as, laser, ultrasound, ultrawideband (UWB), time-of-arrival (TOA), time-difference-of-arrival (TDOA), received signal strength (RSS), channel state information (CSI) and in various combinations [3]–[9], the robots can flexibly equip the suitable ranging device to adapt working circumstance. Therefore, range-based localization algorithms, which estimate the target position by using distance information, are widely used for robot navigation. To pursue efficient localization performance, methods are proposed, such as using more sensitive ranging sensors, optimizing calculation methods, cooperating localization among the target nodes, etc. Since cooperative localization [10], which utilizes the information among the target nodes, significantly improves the localization accuracy, it has become the current lines of research. However, only a single robot is deployed in some applications, such as inspecting the safety of the underground tunnel. In this paper, we focus on the range-based localization algorithm for a single target node.

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