JP6300922B2 - Wireless positioning method and apparatus - Google Patents
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- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000012897 Levenberg–Marquardt algorithm Methods 0.000 description 1
- 238000011960 computer-aided design Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
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Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
- G01S5/0278—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves involving statistical or probabilistic considerations
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
- G01S5/14—Determining absolute distances from a plurality of spaced points of known location
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
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Description
本発明は無線測位の技術分野に関し、特に無線測位方法及び装置に関する。 The present invention relates to the technical field of wireless positioning, and more particularly to a wireless positioning method and apparatus.
受信信号強度（ＲＳＳ）情報は取得しやすく、コストが低いので、ＲＳＳに基づく無線測位は注目を集めて幅広く適用されている。通常、ＲＳＳに基づく測位は、位置指紋認識測位及び三角形測位に大きく分けられる。位置指紋認識測位は、データベースを予め作成し、且つ環境の変化につれてデータベースを更新する必要があり、データベース作成とメンテナンスのコストが高いので、現在、ほとんど実験室、ビル等内に使用され、普及されていない。三角形測位は、経路損失モデルを用いて測位対象点と既知基準点との間の距離を算出し、次に既知基準点の位置と推定距離に基づき三角形測位を行う。この三角形測位に基づく手段は簡単で、商業、科学研究等の分野に幅広く適用されている。しかしながら、無線信号が環境変化の影響を受けやすく、未知経路損失モデルの推定が極めて難しいので、三角形測位手段の使用を損なう。 Received signal strength (RSS) information is easy to obtain and has a low cost, so radio positioning based on RSS has attracted attention and has been widely applied. Usually, positioning based on RSS is roughly divided into position fingerprint recognition positioning and triangle positioning. Positional fingerprint recognition positioning requires the database to be created in advance and the database to be updated as the environment changes, and the cost of database creation and maintenance is high, so it is currently used and popularized mostly in laboratories and buildings. Not. Triangular positioning calculates the distance between a positioning target point and a known reference point using a path loss model, and then performs triangular positioning based on the position of the known reference point and the estimated distance. This means based on triangulation is simple and widely applied in fields such as commerce and scientific research. However, since the radio signal is easily affected by environmental changes and it is extremely difficult to estimate the unknown path loss model, the use of the triangular positioning means is impaired.
未知経路損失モデルでのＲＳＳに基づく三角形測位の問題を解決するために、関連研究によれば、以下の解決手段が提案されている。［１］経路損失指数と位置の組み合わせ推定問題を非線形最適化問題にモデリングし、Ｌｅｖｅｎｂｅｒｇ−Ｍａｒｑｕａｒｄｔ（レーベンバーグ−マーカート法）アルゴリズムに基づき該問題を解決する。［２］根軸に基づき距離互換性を定義し、距離互換性を最大化することにより経路損失指数を動的に推定し、更に経路損失指数を用いて三角形アルゴリズムによる測位を行う。［３］経路損失指数と位置の組み合わせ推定をモデリングして非線形最適化問題を形成した後、Ｇａｕｓｓｉａｎ−Ｓｅｉｄｅｌ（ガウス−ザイデル）アルゴリズムを用いて解く。［４］ヤコビ行列の次元数を削減することにより［１］のＬａｖｅｎｂｅｒｇ−Ｍａｒｑｕａｒｄｔの実現の複雑性を低減させる。［５］経路損失モデルの線形化処理により、３つの変数を含む経路損失指数と位置の組み合わせ推定問題を単一変数の最適化問題に変換して解き、解いた値を初期値として手段［４］に代入して測位精度を更に向上させる。 In order to solve the problem of triangulation based on RSS in the unknown path loss model, the following solutions have been proposed according to related research. [1] The path loss index and position combination estimation problem is modeled as a nonlinear optimization problem, and the problem is solved based on the Levenberg-Marquardt algorithm. [2] Define the distance compatibility based on the root axis, dynamically estimate the path loss index by maximizing the distance compatibility, and perform positioning by the triangle algorithm using the path loss index. [3] After modeling the combined estimation of the path loss index and the position to form a nonlinear optimization problem, it is solved using a Gaussian-Seidel algorithm. [4] By reducing the number of dimensions of the Jacobian matrix, the complexity of realizing [1] Lavenberg-Marquardt is reduced. [5] A path loss model linearization process converts the path loss index and position combination estimation problem including three variables into a single variable optimization problem and solves it, and uses the solved value as an initial value [4] ] To further improve the positioning accuracy.
上記５つの手段はいずれも未知経路損失モデルでの測位問題を解決することができるが、それぞれの欠陥がある。［１］については、計算が複雑であり、結果が初期値の選択に依存する。［２］については、距離互換性はノイズチャネルでエラーが生じやすくて、経路損失指数及び位置の誤推定につながる。［３］については、非線形Ｇａｕｓｓｉａｎ−Ｓｅｉｄｅｌアルゴリズムは非凸最適化問題における大域的最適解の出力を確保できないので、結果が依然として適切な初期値の選択に依存する。［４］については、［１］の応用を簡略化したとしても、複雑性が高く、また［１］のような結果が初期値に依存する問題が解決されていない。［５］については、非線形経路損失モデルを線形化処理することにより、細部を犠牲にして誤差が生じてしまい、また複雑性も高い。 Any of the above five methods can solve the positioning problem in the unknown path loss model, but each has its own defects. For [1], the calculation is complicated and the result depends on the selection of the initial value. For [2], distance compatibility is prone to error in the noise channel, leading to incorrect estimation of the path loss index and location. For [3], the nonlinear Gaussian-Seidel algorithm cannot ensure the output of the global optimal solution in the non-convex optimization problem, so the results still depend on the selection of appropriate initial values. Regarding [4], even if the application of [1] is simplified, the complexity is high, and the problem that the result such as [1] depends on the initial value is not solved. With respect to [5], the nonlinear path loss model is linearized so that errors occur at the expense of details and the complexity is high.
本発明の実施例が解決しようとする技術的問題は、低い計算複雑性で高精度の無線測位を実現することができる無線測位方法及び装置を提供することである。 A technical problem to be solved by an embodiment of the present invention is to provide a wireless positioning method and apparatus capable of realizing highly accurate wireless positioning with low computational complexity.
上記技術的問題を解決するために、以下の方法、構成を利用する。 In order to solve the technical problem, the following method and configuration are used.
上記した発明に係る無線測位方法及び装置によれば、低い計算複雑性で高精度の無線測位を実現することができる。 According to the wireless positioning method and apparatus according to the above-described invention, highly accurate wireless positioning can be realized with low computational complexity.
以下、図面を参照しながら本発明の実施例について詳細に説明する。なお、矛盾が生じない限り、本願の実施例及び実施例の特徴は任意に組み合わせることができる。 Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. As long as no contradiction arises, the embodiments of the present application and the features of the embodiments can be arbitrarily combined.
上記モデルを作成した上で、本発明の実施例は簡単なアルゴリズムを提供して上記最小化問題を解く。該アルゴリズムは段階的処理の考案により反復して測位対象点の位置を求める。具体的には、まず、測位空間を複数の測位領域に離散化し、ピアソンの積率相関係数の計算により測位空間をある領域に小さくし、次に、反復終了条件を満たすまで、選択した小領域内で前のステップを繰り返す。 Having created the model, embodiments of the present invention provide a simple algorithm to solve the minimization problem. The algorithm is repeated by devising stepwise processing to obtain the position of the positioning target point. Specifically, the positioning space is first discretized into a plurality of positioning areas, the positioning space is reduced to a certain area by calculating Pearson's product moment correlation coefficient, and then the selected small area is satisfied until the iteration end condition is satisfied. Repeat the previous step in the region.
本発明の実施例は、未知経路損失モデルでのＲＳＳに基づく無線測位問題を、ピアソンの積率相関係数を最小化する最適化問題にモデリングし、且つ簡単な反復アルゴリズムを提供することにより、従来技術と比べて、経路損失指数を推定せずに低い複雑性で正確な測位を行うことができる。本発明の実施例に係るアルゴリズムは経路損失モデルのパラメータを同時に推定していないが、位置を推定した後、線形回帰に基づき経路損失モデルのパラメータを容易に直接算出できる。 Embodiments of the present invention model an RSS-based radio positioning problem with an unknown path loss model into an optimization problem that minimizes the Pearson product moment correlation coefficient and provides a simple iterative algorithm, Compared with the prior art, accurate positioning can be performed with low complexity without estimating the path loss index. Although the algorithm according to the embodiment of the present invention does not simultaneously estimate the parameters of the path loss model, after estimating the position, the parameters of the path loss model can be easily and directly calculated based on linear regression.
本発明の実施例は未知経路損失モデルでのＲＳＳに基づく無線測位手段を提供する。まず、ＲＳＳを利用し、未知経路損失モデルでの無線測位問題をピアソンの積率相関係数の最小化問題にモデリングし、次に、簡単なアルゴリズムを提供する。具体的な実施プロセスは図１に示され、以下のステップ１０１〜１０４を含む。 Embodiments of the present invention provide a radio positioning means based on RSS with an unknown path loss model. First, using the RSS, the wireless positioning problem in the unknown path loss model is modeled as a Pearson product moment correlation coefficient minimization problem, and then a simple algorithm is provided. A specific implementation process is shown in FIG. 1 and includes the following steps 101-104.
基準点の位置は、ＧＰＳ（全地球衛星測位システム）、手動推定、地図、ＣＡＤソフトウェア（コンピュータ支援設計）などの様々な手段により取得でき、ＲＳＳサンプルは、無線ＬＡＮカードを搭載したノートパソコン、ＰＤＡ（パーソナルデジタルアシスタント）、スマートフォンなどにより収集できる。 The position of the reference point can be obtained by various means such as GPS (Global Satellite Positioning System), manual estimation, map, CAD software (computer-aided design), and RSS samples can be obtained from a notebook computer or PDA equipped with a wireless LAN card. (Personal digital assistant), can be collected by smartphones.
Ｗｉ−Ｆｉ ＲＳＳ信号の収集を例とし、Ｗｉｎｄｏｗオペレーティングシステムを実行するノートパソコンに無線ＬＡＮ監視ソフトウェアをインストールすると、ソフトウェアを実行して周辺のＷｉ−ＦｉアクセスポイントのＲＳＳを収集できる。 Taking the collection of Wi-Fi RSS signals as an example, if the wireless LAN monitoring software is installed in a notebook computer that executes the Windows operating system, the software can be executed to collect RSS of neighboring Wi-Fi access points.
１０２、 測位対象点の位置の値空間を推定する。 102. Estimate the value space of the position of the positioning target point.
１０３、 測位対象点の位置の値空間を離散化し、推定した測位対象点の位置の値空間を複数の同一サイズの格子に区画する。 103. Discretize the value space of the position of the positioning target point, and partition the estimated value space of the position of the positioning target point into a plurality of grids of the same size.
ここまで、未知経路損失モデルでのＲＳＳに基づく測位問題を解決する。 So far, the positioning problem based on RSS in the unknown path loss model is solved.
当業者は、上記方法の全部又は一部のステップをプログラムによって関連ハードウェアに命令を出して完成させることができ、前記プログラムはコンピュータ読取り可能な記憶媒体、例えば読取り専用メモリ、磁気ディスクや光ディスクなどに記憶できることを理解できる。選択的に、上記実施例の全部又は一部のステップを１つ又は複数の集積回路により実現してもよい。それに対して、上記実施例における各モジュール／ユニットはハードウェアの形態で実現されてもよく、ソフトウェア機能モジュールの形態で実現されてもよい。本発明はいずれの特定形態のハードウェアとソフトウェアの組合せにも限らない。 A person skilled in the art can complete all or part of the steps of the above method by issuing instructions to the relevant hardware by means of a program, which is a computer-readable storage medium, such as a read-only memory, a magnetic disk, an optical disk, etc. Can understand that Optionally, all or some of the steps of the above embodiments may be implemented by one or more integrated circuits. On the other hand, each module / unit in the above embodiment may be realized in the form of hardware or may be realized in the form of a software function module. The present invention is not limited to any specific combination of hardware and software.
上記は本発明の好ましい実施例に過ぎず、本発明は様々な他の実施例を有してもよく、本発明の趣旨及びその実質を逸脱せずに、当業者は本発明に基づき様々な変更や変形を行うことができ、それらの変更や変形は本発明の保護範囲に属する。 The above are only preferred embodiments of the present invention, and the present invention may have various other embodiments, and those skilled in the art will be able to Changes and modifications can be made, and these changes and modifications belong to the protection scope of the present invention.
上記技術案に係る無線測位方法及び装置によれば、低い計算複雑性で高精度の無線測位を実現することができる。従って、本発明は産業上の利用可能性が高い。 According to the wireless positioning method and apparatus according to the above technical solution, it is possible to realize highly accurate wireless positioning with low calculation complexity. Therefore, the present invention has high industrial applicability.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310571669.1A CN104635206B (en) | 2013-11-14 | 2013-11-14 | A kind of method and device of wireless location |
| CN201310571669.1 | 2013-11-14 | ||
| PCT/CN2014/080718 WO2015070613A1 (en) | 2013-11-14 | 2014-06-25 | Wireless positioning method and device |
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| JP2017501389A JP2017501389A (en) | 2017-01-12 |
| JP6300922B2 true JP6300922B2 (en) | 2018-03-28 |
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| JP (1) | JP6300922B2 (en) |
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| WO (1) | WO2015070613A1 (en) |
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| CN107454669A (en) * | 2016-05-31 | 2017-12-08 | 中国人民解放军理工大学 | A kind of gunz localization method based on signal intensity correlation analysis |
| CN108716918B (en) * | 2018-04-24 | 2021-08-24 | 合肥工业大学 | RSSI indoor positioning algorithm based on grid clustering |
| CN110673181B (en) * | 2019-08-19 | 2022-03-22 | 中国电波传播研究所（中国电子科技集团公司第二十二研究所） | GNSS interference source positioning method based on grid energy traversal search |
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| JP2004020392A (en) * | 2002-06-17 | 2004-01-22 | Nippon Signal Co Ltd:The | Device, method, and program for calculating moving distance |
| US7319877B2 (en) * | 2003-07-22 | 2008-01-15 | Microsoft Corporation | Methods for determining the approximate location of a device from ambient signals |
| JP4499617B2 (en) * | 2005-06-06 | 2010-07-07 | 日本電信電話株式会社 | Lightning position limiting system and method |
| JP2007028100A (en) * | 2005-07-14 | 2007-02-01 | Sanyo Electric Co Ltd | Diversity system receiver, method of controlling the same, and program |
| US8554478B2 (en) * | 2007-02-23 | 2013-10-08 | Honeywell International Inc. | Correlation position determination |
| WO2008124316A1 (en) * | 2007-04-05 | 2008-10-16 | Skyhook Wireless, Inc. | Time difference of arrival based estimation of speed and direction of travel in a wlan positioning system |
| CN101620267B (en) * | 2007-12-07 | 2011-10-26 | 中国移动通信集团广东有限公司 | Indoor wireless locating method |
| JP5537284B2 (en) * | 2010-06-24 | 2014-07-02 | 日本電信電話株式会社 | Current position grasp method, current position grasp system, current position grasp program |
| GB2481851A (en) * | 2010-07-09 | 2012-01-11 | Univ Antwerpen | Object location |
| KR20120053941A (en) * | 2010-11-17 | 2012-05-29 | 엘지전자 주식회사 | Method and apparatus ofpositioning in a wireless communication system |
| CN102098780B (en) * | 2010-12-14 | 2013-12-11 | 北京智慧图科技发展有限责任公司 | Positioning method and device |
| CN102131290B (en) * | 2011-04-26 | 2013-06-05 | 哈尔滨工业大学 | WLAN (Wireless Local Area Network) indoor neighbourhood matching positioning method based on autocorrelation filtering |
| KR20130094967A (en) * | 2012-02-17 | 2013-08-27 | 성균관대학교산학협력단 | A method and an apparatus for inferring data considering user preferences |
| WO2013130067A1 (en) * | 2012-02-29 | 2013-09-06 | Intel Corporation | Detection of device motion and nearby object motion |
| CN102802260B (en) * | 2012-08-15 | 2015-05-13 | 哈尔滨工业大学 | WLAN indoor positioning method based on matrix correlation |
| CN103197280B (en) * | 2013-04-02 | 2014-12-10 | 中国科学院计算技术研究所 | Access point (AP) location estimation method based on radio-frequency signal strength |
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| CN104635206A (en) | 2015-05-20 |
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