مقاله انگلیسی رایگان در مورد روش محلی سازی مربعات حداقل محدود – IEEE 2019

 

مشخصات مقاله
ترجمه عنوان 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
انتشار مقاله سال 2019
تعداد صفحات مقاله انگلیسی 10 صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
پایگاه داده نشریه IEEE
نوع نگارش مقاله
مقاله پژوهشی (Research Article)
مقاله بیس این مقاله بیس نمیباشد
نمایه (index) Scopus – Master Journals List – JCR
نوع مقاله ISI
فرمت مقاله انگلیسی  PDF
ایمپکت فاکتور(IF)
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
مدل مفهومی ندارد
پرسشنامه ندارد
متغیر ندارد
رفرنس دارد
رشته های مرتبط مهندسی کامپیوتر
گرایش های مرتبط مهندسی الگوریتم و محاسبات
نوع ارائه مقاله
ژورنال
مجله / کنفرانس دسترسی – IEEE Access
دانشگاه  School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
کلمات کلیدی مربعات حداقل محدود، فضای باریک، محلی سازی مبتنی بر دامنه
کلمات کلیدی انگلیسی  Constrained least-square, narrow space, range-based localization
شناسه دیجیتال – doi
https://doi.org/10.1109/ACCESS.2019.2957402
کد محصول  E14083
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
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فهرست مطالب مقاله:
ABSTRACT

I. INTRODUCTION

II. PROBLEM STATEMENT

III. MODEL AND ALGORITHM

IV. NUMERICAL SIMULATIONS AND EXPERIMENTAL TESTS

V. CONCLUSION

REFERENCES

 

بخشی از متن 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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