TWI260868B - Method for describing a propagation behavior of a communication signal which is transmitted by a base station in a communication network and computer-readable data media including program code means for computer program - Google Patents
Method for describing a propagation behavior of a communication signal which is transmitted by a base station in a communication network and computer-readable data media including program code means for computer program Download PDFInfo
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Description
1260868 九、發明說明: 【發明所屬之技術領域】 本發明是有關於一種在通信網路中由一基地台所發出 之一通信訊號之傳送行爲之模型產生。 無線電通訊系統,譬如以無線區域網路,藍芽’泛歐數 位式行動通訊(GSM),全球行動通訊系統(UMTS)或泛歐數位 無線電話系統(DECT)等爲主者’被使用在最多的領域上。在 工業生產機構及辦公室環境或健康產業上,這些都是到處存 在的。 輻射通訊信號之通訊設備所產生之電磁場之傳送特性 實質決定該通訊設備關於涵蓋區域,可用性及傳輸速率等之 效能特徵。 首先,無線電網路業者關心的是確認場傳送特性或信號 特徵之分布情形,譬如電磁場強度,相位,傳送時間,波向 量,位元錯誤率,信號雜訊比等,俾能設計最佳之無線電網 路,於網路安裝後能確認關於品保方面所要求之系統特性, 或是在設備的操作當中,診斷錯誤狀態。 再者’網路服務提供者關心的是如何能提供與場地相關 之服務給他們的客戶。 關於此,接收裝置之位置必須知道。由於僅有在一般正 常網路操作期間產生之資料應使用於位置之估側,此處,亦 適合於考慮信號之特徵。 【先前技術】 先前技術所揭露之方法及程序主要係關於終端機之定 -6 - 1260868 位,例如DECT行動部件或PDAs以及筆記型電腦等配備有 無線區域電腦網路者。 在某些方法中,例如於[1 ]中所示者,區域化僅根據網 路拓撲。在此情形下,終端機之位置係參考其所連接之基地 台以及該終端機之連接紀錄而決定。 此一方法之精確度將受限制,因爲環境終端機所連接之 基地台,將有一非常大的區域得以被認定爲一可能之場地。 其他已知的方法嘗試估測位置,其係根據所有可獲得之 發射器之接收場強度。於此情形下,有時會使用一詳細之實 體模型於電波傳送上面。然而,爲此必須瞭解關於環境之詳 細資訊。 [2]揭露了關於建築物之內各種牆壁之電磁傳送特性之 知識運用。然而,此種知識一般不易獲得。因此正常情況下, 一場強度圖會先根據波傳送之傳送模型而製成,然後再接續 使用於區域侷限化。 關於接收器位置之一點估測通常係根據場強度圖[3], [4]而實施。 [5]描述了用於估測DECT行動電話之位置所使用之一 遞廻式,隨機非線性濾波器方法。一種傳送模型,在此情形 下爲一統計非線性模型,亦產生或在此使用作爲估測位置之 基礎。 在先前技術所揭露之大部分的模型爲基礎之程序中,建 構傳送行爲之模型均參考校正量測而發生,其中構成傳送行 爲之特徵的物理變數,例如前述之場強度,係在預定之位置 1260868 (校正位置)加以量測。 用來描述傳送行爲之模型係藉利用校正位置以及在這 些位置所量測之傳送變數而決定。 上'述以模型爲基礎之程序有一缺點在於必須量測許多 校正位置,始能獲得有關傳送行爲之一足夠精確的模型,以 及據此之一足夠精確的位置規格。 【發明內容】 因此,本發明係爲處理在產生傳送行爲之模型時,有關 規範相關程序之問題,其中該程序需要較少校正位置之量 測。 達到此一目的之方法及電腦程式包含程式碼裝置及電 腦程式產品,用來描述一通信訊號之傳送行爲,該信號在一 通信網路中是由一基地台加以傳輸,並參照根據相關獨立項 之特性。 用來描述通信訊號之傳送行爲的方法中,該通信訊號之 一物理性質係在通信網路中之選擇的位置進行量測,該物理 性質在各種情況下係與相關之位置相結合。 依此方式,該物理性質構成通信訊號傳送行爲之特徵。 使用選擇之位置或使用與選擇位置相關之對應位置資 訊或場所資訊以及通信訊號之相關聯量測物理性質,即可決 定傳送行爲之一模型,此模型描述了傳送行爲。 在此情況下,該模型係使用一高斯程序而產生,顯示了 相依於場所資訊或位置資訊之量測物理性質(“前向模型”)。 爲了區分各種模型,請注意在所謂的前向模型的情形 1260868 下,通信訊號之物理性質係依據通信網路中之一位置或是一 距離而加以描述。 一回歸或反向模型根據通信訊號之物理性質,描述通信 網路中之位置。 本發明之一主要優點在於使用高斯程序製作傳送行爲 之模型可大量減少校正量測,且在精確度方面僅有小誤差。 這些可使用例如一“設計方法” [2]加以規定。 電腦程式包含程式碼裝置,其結構使得當程式於電腦上 面執行時’能根據請求項所示方法之所有步驟,藉以描述傳 送行爲。 電腦程式產品包含程式碼裝置,儲存於機器可讀取之媒 體上面,該產品之結構係使得當程式於電腦上執行時,能根 據本發明之方法所示之所有步驟,藉以描述傳送行爲。 電腦程式包含程式碼裝置,其結構使得當程式在一電腦 上執行時,能根據本發明之方法所示之全部步驟,俾以建構 該模型,且電腦程式產品包含程式碼裝置,儲存於機器可讀 取之媒體上面,其結構使得當程式在一電腦上執行時,能根 據本發明之方法所示之全部步驟,俾以建構該模型,此二者 均特別適合於執行本發明之方法或其推導措施之一,如以下 之解釋。 本發明之最佳推導措施係由附屬請求項衍生而來。 以下所描述之推導具與該方法及軟體爲基礎之實施相 關連。 本發明及下述之推導可以軟體及硬體加以實施,例如利 1260868 用一特殊之電路。 以下所述之本發明或其推導亦可藉由一 儲存媒體加以實施,在其上儲存有電腦程式, 本發明或其推導之程式碼裝置。 本發明或各項推導如後述亦可藉由一電 實施,其包含一儲存媒體,於其上儲存電腦程 本發明或各項推導之程式碼裝置。 於一通信網路,例如一無線電網路之通信 信訊號之資料係以信號封包方式傳輸,或者是 信裝置(移動站),例如一行動電話,以及一基 傳送或接收用之全方向式天線或一多扇形天線 爆發。 根據傳輸或輻射通信訊號或信號封包之 理性質,即可決定數種與距離相關之參數,其 爲決定(信號)發射器之輻射或信號特徵之基礎 其中一種距離相關,亦即距離相依之參數 通信訊號或信號封包之一場強度’ 一相位,一 波向量,一位元誤差率或訊號雜訊比率。 一輻射通信訊號之場強度對於一發射器, 地台之距離具有一自然之依存性’因此提供了 之傳送行爲(傳送特性)的資訊’並且特別適合 發明及使用一高斯程序之模型。 於一最佳推導情況中,校正量測係在預 施,其中在這些點之物理性質數値被量測出來 電腦可讀取之 包含用來執行 腦程式產品而 式,包含執行 期間,含有通 所謂的行動通 地台,例如一 [,彼此之間的 (可量測的)物 可接續使用作 〇 係舉例而言一 傳輸時間,一 亦即(傳呼)基 有關於發射器 於建構依據本 定之校正點實 。選擇校正點 -10- 1260868 可藉由一最佳“設計”方法或網格法加以使用,例如在[2] 所揭露之方法用來規範一六角之網格。 較大的通信網路於正常情況下,具有多種或是複合之基 地台,各輻射一通信訊號。 在此背景下,相當適合爲各基地台或是各基地台之通信 訊號產生一分別之傳送模型。 在超過一個以基地台之情況下,該模型係根據本發明之 程序而製成,可作爲通信網路中之數種應用之基礎。 這些模型可因此使用在規劃及/或裝置及/或運轉及 /或錯誤狀態之診斷及/或通信網路品質保證等之目的。 根據本發明而製作之傳送模型亦可使用在通信網路中 至少一行動通信裝置之區域侷限化/定位上面,該行動通信 裝置係規劃爲接收通信訊號及/或接收數個通信訊號。 含有數個基地台之通信網路中,當一行動通信裝置之區 域化或指明位置於執行時,必須指定之位置處量測之物理性 質’例如場強度,其近似度可於於相關基地台之高斯程序模 型中加以指定。有關行動裝置必須指定之位置係藉由指定具 有最大可能性之點/位置而產生。 當指定位置時,基本的觀念是在此情況下反轉高斯程序 模型。當製作傳送行爲之模型時,前向模型之產生如上所 述。在位置指定之過程中,模型將予以反轉,使得在反轉之 模型中,位置可依據物理性質之可能性而加以顯示。 本發明,或依據本發明之一或多個基地台之傳送行爲製 作之模型,特別適合於使用在數位蜂巢式行動無線電系統之 1260868 環境,例如GSM/UMTS網路,並用於將其中GSM/UMTS電 話(行動電話)予以區域化。 當應用本發明時,僅有行動電話可獲得之資料使用於此 情況中,因而避免需高費用於G S Μ網路或G S Μ網路中之行 動站之改變。 本發明亦適於使用在其他數位蜂巢式行動無線電系統 之環境,例如WLAN,依據藍芽之網路或是一 DECT網路, 以及用於將其中例如一 DECT行動電話予以區域化。 本發明特別適合於不利於信號傳送條件之環境,例如重 大雜訊或反射信號,屏蔽或障礙基地台,內部房間趨勢。在 諸如此類之限制性條件下,創造實體上準確之模型將是不可 能或極端困難的事。 本發明之一典型實施將詳述如下,並以圖示加以說明, 其中: 【實施方式】 槪述/過程 定位系統(GPPS)係根據使用校正量測之一校正,以下描 述有關通信網路(例如此處之DECT網路)之中之一行動通信 裝置,並且係來自高斯程序模型(GPM),其中來自於通信網 路之基地台的信號以及在預定位置處,例如校正位置之場強 度,是在通信網路中加以量測(第1圖,1 1 〇)。 高斯程序模型(GPM)針對校正量測而調整(參考點1)(第 1圖,1 00)。就此而言,適當的選擇關於高斯程序模型之核 心函數相當重要。在此使用Matern級之核心函數。 1260868 調整後之高斯程序模型接著使用於定位(參考點2) °此 時之情況係決定高斯程序模型之近似度(第1圖’ 13〇)以及 其最佳化(第1圖,140)。 同時敘述的是有關校正位置之最佳選擇或排列之程序 (參考點3)(第1圖,100)。 1 .根據所量測之場強度,決定高斯程序模型。 列入說明之GPP S係根據有關通信訊號之傳送特徵或是 個別基地台之信號強度/場強度之機率模型或統計模型。 此處使用之模型係根據高斯模型或是高斯程序迴歸 (GPR),並且經常使用來解決貝氏系統中之非線性迴歸問題 [12, 9]。 以下所述之程序或模型產生係關於(選定之)基地台之 訊號或場強度。此一程序類推適用於所有的基地台。 列入考量的是一組N個校正量測,其中選定之基地台的 場強度yi(正常情況係以dB)係在通信網路中之已知位置Xi, i = l,…,N處加以量測。GPR —般要求產生一未知函數 f: /1經由y^fUd + ei映射之標的,具有獨立的高斯雜訊 ei,變異量爲a 2。 此處之基本模型假設爲f(Xi)是根據一高斯程序,其係指 在均値爲0且共變異矩陣爲K之條件下,函數f(xi)之函數 値在點Xi處呈現高斯分佈。 K本身則由核心(共變異)函數 K(·,·)而得,其中1260868 IX. Description of the Invention: [Technical Field] The present invention relates to a model for generating a communication signal transmitted by a base station in a communication network. Radio communication systems, such as wireless local area networks, Bluetooth's Pan-European Digital Mobile Communications (GSM), Global System for Mobile Communications (UMTS) or Pan-European Digital Radiotelephone System (DECT) are the most used On the field. These are everywhere in industrial production facilities and office environments or in the health industry. The transmission characteristics of the electromagnetic field generated by the communication device radiating the communication signal substantially determine the performance characteristics of the communication device with respect to the coverage area, availability and transmission rate. First, the radio network operators are concerned with confirming the distribution of field transmission characteristics or signal characteristics, such as electromagnetic field strength, phase, transmission time, wave vector, bit error rate, signal noise ratio, etc. The network can confirm the system characteristics required for quality assurance after the network is installed, or diagnose the error status during the operation of the equipment. Furthermore, 'network service providers are concerned with how they can provide venue-related services to their customers. In this regard, the location of the receiving device must be known. Since only the data generated during normal normal network operation should be used for the estimated side of the location, it is also suitable here to consider the characteristics of the signal. [Prior Art] The methods and procedures disclosed in the prior art mainly relate to terminals -6 - 1260868, such as DECT mobile parts or PDAs, and notebook computers equipped with wireless area computer networks. In some methods, such as those shown in [1], regionalization is based only on the network topology. In this case, the location of the terminal is determined by reference to the base station to which it is connected and the connection record of the terminal. The accuracy of this method will be limited because the base station to which the environmental terminal is connected will have a very large area to be identified as a possible venue. Other known methods attempt to estimate the position based on the received field strength of all available transmitters. In this case, a detailed physical model is sometimes used for radio wave transmission. However, detailed information about the environment must be known for this purpose. [2] revealed the use of knowledge about the electromagnetic transmission characteristics of various walls within buildings. However, such knowledge is generally not readily available. Therefore, under normal circumstances, an intensity map will be made based on the transmission model of the wave transmission, and then used in the regional limitation. One point estimation of the receiver position is usually implemented according to the field intensity map [3], [4]. [5] describes a one-way, stochastic nonlinear filter method used to estimate the position of a DECT mobile phone. A transfer model, in this case a statistical nonlinear model, is also generated or used here as the basis for estimating the position. In most of the model-based procedures disclosed in the prior art, models for constructing transmission behavior occur with reference to calibration measurements, wherein the physical variables that characterize the transmission behavior, such as the aforementioned field strength, are at predetermined locations. 1260868 (corrected position) is measured. The model used to describe the transfer behavior is determined by using the corrected positions and the transmitted variables measured at these locations. One of the disadvantages of the above-described model-based procedure is that many correction positions must be measured to obtain a model that is sufficiently accurate about one of the transmission behaviors, and one of the positional specifications that is sufficiently accurate. SUMMARY OF THE INVENTION Accordingly, the present invention is directed to the problem of relating to a specification-related procedure when generating a model of transmission behavior, wherein the procedure requires less calibration of the corrected position. The method and computer program for achieving the purpose include a code device and a computer program product for describing a communication signal transmission behavior, the signal is transmitted by a base station in a communication network, and reference is made according to related independent items. Characteristics. In the method for describing the transmission behavior of a communication signal, a physical property of the communication signal is measured at a selected location in the communication network, the physical property being combined with the associated location in each case. In this way, the physical properties constitute a characteristic of the communication signal transmission behavior. One of the transmission behaviors can be determined using the selected location or using the corresponding location information or location information associated with the selected location and the associated physical properties of the communication signal, which describes the transmission behavior. In this case, the model is generated using a Gaussian program that displays the measured physical properties ("forward model") that are dependent on the location information or location information. In order to distinguish between various models, note that in the case of the so-called forward model 1260868, the physical properties of the communication signal are described in terms of a location or a distance in the communication network. A regression or inverse model describes the location in the communication network based on the physical nature of the communication signal. One of the main advantages of the present invention is that the use of a Gaussian program to model the transmission behavior can substantially reduce the correction measurement with only minor errors in accuracy. These can be specified using, for example, a "design method" [2]. The computer program contains a code device that is structured such that when the program is executed on a computer, it can describe the transfer behavior according to all the steps of the method shown in the request. The computer program product contains code means stored on a machine readable medium which is structured such that when executed on a computer, all of the steps shown in the method of the present invention can be used to describe the transfer behavior. The computer program includes a code device having a structure such that when the program is executed on a computer, the model can be constructed according to all the steps shown in the method of the present invention, and the computer program product includes the code device and can be stored in the machine. The read medium is structured such that when the program is executed on a computer, the model can be constructed in accordance with all of the steps shown in the method of the present invention, both of which are particularly suitable for performing the method of the present invention or One of the derivation measures, as explained below. The preferred derivation of the invention is derived from the accompanying claims. The derivation described below is related to the implementation of the method and software. The invention and the following derivation can be implemented in software and hardware, for example, a special circuit is used for the 1260868. The invention or its derivation described below can also be implemented by a storage medium having stored thereon a computer program, the invention or a coded device derived therefrom. The present invention or various derivations may also be implemented by an electric power, which includes a storage medium on which the computer program or the derivation code device is stored. In a communication network, for example, a communication signal of a radio network is transmitted by signal packet, or a signaling device (mobile station), such as a mobile phone, and an omnidirectional antenna for transmitting or receiving. Or a multi-sector antenna burst. Depending on the nature of the transmitted or radiated communication signal or signal packet, several distance-dependent parameters can be determined, which are one of the distance-dependent, or distance-dependent, parameters that determine the radiation or signal characteristics of the (signal) transmitter. A field strength of a communication signal or signal envelope 'one phase, one wave vector, one bit error rate or signal noise ratio. The field strength of a radiated communication signal has a natural dependence on the distance between a transmitter and a platform, thus providing information on the transmission behavior (transmission characteristics) and is particularly suitable for inventing and using a model of a Gaussian procedure. In a best derivation case, the calibration measurement is pre-applied, wherein the physical properties of the points are measured and the computer readable is included to execute the brain program product, including during execution, including The so-called mobile platform, for example, a [measured] between each other can be used as a transmission system, for example, a transmission time, that is, (pushing) based on the transmitter in accordance with the construction Set the correction point. The choice of calibration point -10- 1260868 can be used by an optimal "design" method or grid method, such as the method disclosed in [2] to specify a hexagonal grid. Larger communication networks have a variety of or composite base stations under normal conditions, each radiating a communication signal. In this context, it is quite suitable to generate a separate transmission model for the communication signals of each base station or each base station. In the case of more than one base station, the model is made in accordance with the procedures of the present invention and can serve as a basis for several applications in a communication network. These models may therefore be used for planning and/or device and/or operational and/or error state diagnostics and/or communication network quality assurance purposes. The transmission model made in accordance with the present invention may also be used in the localization/positioning of at least one mobile communication device in the communication network, the mobile communication device being programmed to receive communication signals and/or receive a plurality of communication signals. In a communication network containing several base stations, when a mobile communication device is regionalized or specified to be executed, the physical properties of the measurement must be specified, such as the field strength, which can be approximated at the relevant base station. It is specified in the Gaussian program model. The location that the mobile device must specify is generated by specifying the point/location that has the greatest likelihood. When specifying a location, the basic idea is to reverse the Gaussian program model in this case. When making a model of the transfer behavior, the forward model is generated as described above. In the process of position specification, the model will be reversed so that in the inverse model, the position can be displayed according to the possibility of physical properties. The invention, or a model made in accordance with the transmission behavior of one or more of the base stations of the present invention, is particularly suitable for use in a 1260868 environment of a digital cellular mobile radio system, such as a GSM/UMTS network, and for use in GSM/UMTS The telephone (mobile phone) is regionalized. When the present invention is applied, only the data available to the mobile phone is used in this case, thereby avoiding the need for high cost changes to the mobile station in the G S network or the G S network. The invention is also suitable for use in the context of other digital cellular radio systems, such as WLAN, Bluetooth based networks or a DECT network, and for regionalizing, for example, a DECT mobile phone. The invention is particularly well-suited for environments that are detrimental to signal transmission conditions, such as heavy noise or reflected signals, shielding or obstructing base stations, and internal room trends. Under such restrictive conditions, creating a physically accurate model would be impossible or extremely difficult. A typical implementation of the present invention will be described in detail below and illustrated by way of illustration, in which: [Embodiment] The description/process positioning system (GPPS) is based on the correction using one of the calibration measurements, and the following description relates to the communication network ( One of the mobile communication devices, such as the DECT network here, and is from the Gaussian Program Model (GPM), where the signal from the base station of the communication network and the field strength at a predetermined location, such as a corrected location, It is measured in the communication network (Fig. 1, 1 1 〇). The Gaussian Program Model (GPM) is adjusted for calibration measurements (Reference Point 1) (Figure 1, 000). In this regard, proper selection of the core function of the Gaussian program model is quite important. The core function of the Matern level is used here. 1260868 The adjusted Gaussian program model is then used for positioning (reference point 2). At this point, the approximation of the Gaussian program model (Fig. 1 '13〇) and its optimization (Fig. 1, 140) are determined. Also described is the procedure for the optimal selection or alignment of the corrected position (Reference Point 3) (Fig. 1, 100). 1. Determine the Gaussian program model based on the measured field strength. The GPP S included in the description is based on the transmission characteristics of the communication signals or the probability model or statistical model of the signal strength/field strength of the individual base stations. The models used here are based on Gaussian models or Gaussian program regression (GPR) and are often used to solve nonlinear regression problems in Bayesian systems [12, 9]. The procedure or model described below produces a signal or field strength for the (selected) base station. This program is analogous to all base stations. The consideration is a set of N calibration measurements, where the field strength yi of the selected base station (normally in dB) is at the known position in the communication network Xi, i = l, ..., N Measure. GPR generally requires an unknown function f: /1 is mapped by y^fUd + ei, with independent Gaussian noise ei, and the variation is a 2 . The basic model here assumes that f(Xi) is a Gaussian program, which means that the function f(xi) has a Gaussian distribution at point Xi under the condition that the mean is 0 and the covariation matrix is K. . K itself is derived from the core (covariation) function K(·, ·), where
Ki,j = k(Xi,Xj)。 僅根據數個校正或校正位置t之一高斯程序(GP)的假設 1260868 一高斯分佈。 使用以下之關係式: v(t) = (k(t, χχ), . . . 7 k(fc/ χΝ))Τ (2.1) y ~ (yir · · · / υν)τ (2.2) q = k + a2i (2,3) 則GPM之預設均値可針對數個校正位置t而產生: E(f(t)|D) = V(t)TQ-ly (2.4) 變異量爲: var(f(t)[D) = k(t, t) - v(t)TQ一1v(t) · (2,5〉 這些關係式可在關於高斯程序之基本入門著作中找到 [1 1,8,12,9]。 要 定Ki,j = k(Xi, Xj). Gaussian distribution of 1260868 based only on a number of corrected or corrected positions t Gaussian program (GP). Use the following relationship: v(t) = (k(t, χχ), . . . 7 k(fc/ χΝ))Τ (2.1) y ~ (yir · · · / υν)τ (2.2) q = k + a2i (2,3) The preset GCM can be generated for several correction positions t: E(f(t)|D) = V(t)TQ-ly (2.4) The variation is: var (f(t)[D) = k(t, t) - v(t)TQ-1v(t) · (2,5> These relations can be found in the basic introductory work on Gaussian procedures [1. 8,12,9]. To be fixed
選取雜訊變異量σ 2及核心函數K之參數0因此相當重 這些可藉由訓練/校正資料之對數近似度之最大化而決 同時根據模型參數: 62, Θ = arg ma:x(- logdet Q - yTQ一1y)· (2.6) σ2,θThe parameter 0 of the noise variation and the parameter 0 of the kernel function K are therefore quite heavy. These can be maximized by the logarithmic approximation of the training/correction data according to the model parameters: 62, Θ = arg ma:x(- logdet Q - yTQ - 1y) · (2.6) σ2, θ
Matern核心函數 當使用GPM時,適當選用核心(共變異量)函數相當重 核心函數敘述兩個點之函數値之間的相關型態。 一種普通的選擇包括G P s具有平方核心,結構爲: k(x, xT) = exp (-v|x - xl 然而,由[1 0, 6]得知,此種核心函數之結構就隨機程序 之環境而論係非自然的,結果例示路徑爲無限平滑,亦即, 若共變異函數最初具有一無限數目之導函數。 -14- 1260868 在此情況下,因此利用Matern級之共變異/核心函數 [1 〇],藉由參數v而允許對於例示路徑之平滑度之連續性的 參數化。 實驗例子顯示具有核心函數之GPM對於來自於等式 (2 · 5 )之假定變異量能作出一實際之估測。Matern core functions When using GPM, the appropriate choice of core (common variation) function is quite heavy. The core function describes the correlation between the functions of two points. A common choice consists of a GP s with a squared kernel and a structure of: k(x, xT) = exp (-v|x - xl However, from [1 0, 6], the structure of such a core function is a random program. The environment is unnatural, and the result exemplifies the path as infinite smoothing, that is, if the covariation function initially has an infinite number of derivatives. -14- 1260868 In this case, therefore, the Matern-level covariation/core is utilized. The function [1 〇] allows parameterization of the continuity of the smoothness of the exemplified path by the parameter v. The experimental example shows that the GPM with the kernel function can make a hypothetical variation from the equation (2·5). Actual estimate.
Matern核心之函數形式爲: k(xfX〇 = Mv(z) = Kv(2Vvz)· (2.7) • Γ(ν) 其中Γ (ν)係gamma函數,Kv(r)係二次ν之修正貝氏函 數,且z2 = Σ私1 wj(xj - x»j)2具有輸入標尺長度Wi。 參數ν指明例示路徑之平滑度(“碎形維度”),並且可由 公式(2.6)加以估計。 使用一 Matern核心,調整GPs 有效求解公式(2.6)需要Matern核心函數式(2.7)關於所 有參數v,w之導函數。 數値化梯度之相關應用揭露於例如Π 〇]中,需要對於貝 氏函數作多重評估,因此導致大量的計算過程。 此處使用之導函數之解析計算如下: = Γ(ν)ψ(ν) (2.8) ~f~ =-全(κν一l(z) + Kv+l(z))' (2.9) 其中Ψ (ν)係零階之多gamma函數(所謂P si函數)。由於 目前尙不知貝氏函數kv(z)關於級次ν之導函數的封閉形 式,此處係以近似方式DKv(z) = ef1 (Kv+e(z) 一 Kv㈣。 1260868 由此估測,公式(2.7)之梯度可指示如下: M = ^ Mv(2) - (Kv_l(2^) + KV+1(2V^)) θΜ ^ = Μν(ζ)^ + log(Vvz) - ψ(ν) ( ζ Γ(νΓ Γ (κν_1(2Λ/νζ) + Kv+1(2Vvz)) + DKv(2>/vzjj . (2.10) 根據以上之等式,公式(2.6)之導函數關於模型參數σ2, ν,w可使用標準的矩陣幾何加以求算出來。 在基本的入門著作高斯程序[11,8,12,9]之中敘述所需 之關係式。 指定場強度模型 以下說明有關選定基地台之訊號傳送之GPM如何產生。 假設由此基地台傳輸之信號已在校正點Xi,i = l,…,Ν處 量測N個校正測量結果。 就此而論,量測點在二度空間加以考慮,應注意所敘述 之程序亦同理適用於三度空間之點。The function form of the Matern core is: k(xfX〇= Mv(z) = Kv(2Vvz)· (2.7) • Γ(ν) where Γ (ν) is the gamma function, and Kv(r) is the correction of the quadratic ν The function, and z2 = Σ 1 wj(xj - x»j) 2 has an input scale length Wi. The parameter ν indicates the smoothness of the illustrated path ("fractal dimension") and can be estimated by equation (2.6). A Matern core, adjusting the GPs effective solution formula (2.6) requires the Matern core function formula (2.7) for all the parameters v, w. The correlation application of the numerical gradient is disclosed in, for example, Π 〇], which requires a Bayesian function. Multiple evaluations result in a large number of calculations. The analytical function of the derivative function used here is as follows: = Γ(ν)ψ(ν) (2.8) ~f~ =-all (κν一l(z) + Kv+ l(z))' (2.9) where Ψ (ν) is a multi-gamma gamma function (so-called P si function). Since there is no known closed form of the Bayesian function kv(z) with respect to the derivative function of the order ν, Here, in the approximate manner DKv(z) = ef1 (Kv+e(z) - Kv(4). 1260868 From this estimate, the gradient of equation (2.7) can be indicated as follows: M = ^ Mv(2) - (Kv_l(2) ^) + KV+ 1(2V^)) θΜ ^ = Μν(ζ)^ + log(Vvz) - ψ(ν) ( ζ Γ(νΓ Γ (κν_1(2Λ/νζ) + Kv+1(2Vvz)) + DKv(2> /vzjj . (2.10) According to the above equation, the derivative function of equation (2.6) can be calculated using the standard matrix geometry with respect to the model parameters σ2, ν, w. In the basic introductory work Gaussian program [11, 8, The required relationship is described in 12, 9]. Specifying the field strength model The following describes how the GPM for the signal transmission of the selected base station is generated. It is assumed that the signal transmitted by this base station is already at the correction point Xi, i = l,... N measurements are measured at the 。. In this case, the measurement points are considered in the second dimension, and it should be noted that the described procedure applies equally to the point of the third dimension.
從有關選定之基地台之校正資料 D = {xi, yj N i = l 開始,以下步驟將予施行: 1 .有關基地台之一估測位置之獲得方法爲藉由選擇3個校正 點Xi,其具有最高的場強度數値Yl,並且構成其焦點。 基地台之位置通常無法記錄得知。然而在很少的情況下, 此等位置資料存在並且可加以使用以替代上述之估測。 2 ·爲獲得GPM之均値函數,一線性模型將加以調整以適應 量測値,其中一對數等級被選擇作爲歐幾里得距離對基地 -16- 1260868 台之一函數。 場強度値若以dB値指示’將可直接使用,如同已在對數 等級一般。因此,以下符合傳送法則將納入於模型當中: 在基地台具有強度爲1之信號於接收時,在離基地台-距 離d處之接收強度爲e X p (- d)’且原始量測之均値函數値 加以減除。 3.公式(2.6)提供最佳模型參數,例如變異量σ2,Mat ern平 滑參數v以及輸入標尺長度Wi。 第2a圖及第2b圖顯示一 GPM之實例,具有原始校正 資料以及可能GPM獲得之平滑資料。該平滑資料呈現出某 種結構,由原始的量測資料無法看見,例如向基地台之左側 及右側延伸之2地帶。 2.決定使用GPM之一位置 根據前述GPM以決定一位置將說明如下。 以下假設經由校正爲已知: - C校正測量Starting from the correction data D = {xi, yj N i = l of the selected base station, the following steps will be implemented: 1. The estimated position of one of the base stations is obtained by selecting 3 correction points Xi, It has the highest field strength number 値Yl and constitutes its focus. The location of the base station is usually not recorded. In rare cases, however, such location data exists and can be used in place of the above estimates. 2 · To obtain the GPM's uniform function, a linear model will be adjusted to accommodate the measurement, where the one-to-five level is chosen as a function of the Euclidean distance to the base -16-1260868. The field strength 値 if indicated in dB値 will be used directly as if it were already in logarithmic scale. Therefore, the following conforming transmission rules will be included in the model: When the signal with intensity 1 is received at the base station, the receiving strength at the base station-distance d is e X p (-d)' and the original measurement is The uniform function is decremented. 3. Equation (2.6) provides the best model parameters, such as the variation σ2, the Matern smoothing parameter v, and the input scale length Wi. Figures 2a and 2b show an example of a GPM with raw calibration data and smooth data obtained by possible GPM. The smoothed data presents a structure that is not visible from the original measurement data, such as 2 zones extending to the left and right sides of the base station. 2. Decide to use one of the GPM locations. Determining a location based on the aforementioned GPM will be explained below. The following assumptions are known by correction: - C correction measurement
-在校正點Xi,i = l,…,C -對於B基地台 -具有接收場強度c i,j於基地台j,j = 1,…,B之場所X j。 - j = 〇 ’若基地台j之信號無法在場所Xi加以接收。- at the correction point Xi, i = l, ..., C - for the B base station - with the receiving field strength c i, j at the base station j, j = 1, ..., B where X j. - j = 〇 ‘If the signal of base station j cannot be received at location Xi.
Ci指明所有可在場所Xi加以接收之信號的場強度向量。 在測試階段或應用階段,行動通信使用者位於一未知而 必須指出之場所時,將量測在該場所可接收之基地台的場強 度。 -17- 1260868 S指出在必須指明之場所處之可接收場強度之向量,且 該向量S之s j成分爲由基地台h接收之場強度。 使用“最近鄰居”以決定一位置(NNLoc) 在NNLoc之情況下,必須指明之位置的向量s與校正測 量c i,i = 1,…,i相比較。該位置之各“鄰居”或校正點將 根據該測量與相關校正測量c i相符合之程度而作一權重。這 些權値將會就所有可接收基地台及目前場強度列入考慮。根 據已知以及最佳匹配校正位置及相關聯之權重値,對於行動 通信使用者而言必須指明之位置t即可藉由內插法加以估 測。 局斯程序定位系統(G P P S ) 藉由GPPS以決定一位置係根據上述之GPM構成。在此 情況下,使用個別基地台之GPM,即可在必須指明場所t處 形成基地台之接收場強度之近似度。 使用校正資料 拉i' ci,ji i s 位'· · · / C}, j e {1, · · ♦ , B} 相關GMP Mj爲個別基地台而形成,模型Mj係根據來 自於這些校正點j之資料Dj,在此處,該基地台j可被接收:Ci indicates the field strength vector of all signals that can be received at location Xi. During the test phase or application phase, when the mobile communication user is located at an unknown location that must be indicated, the field strength of the base station receivable at the site will be measured. -17- 1260868 S indicates the vector of the receivable field strength at the location that must be specified, and the s j component of the vector S is the field strength received by the base station h. Use "nearest neighbor" to determine a position (NNLoc) In the case of NNLoc, the vector s of the position that must be specified is compared with the corrected measurement c i,i = 1,...,i. Each "neighbor" or correction point for that location will be weighted based on the extent to which the measurement is consistent with the associated correction measurement c i . These rights will be considered for all receivable base stations and current field strengths. Based on the known and best match correction position and associated weights, the location t that must be specified for the mobile communication user can be estimated by interpolation. The POS program positioning system (G P P S ) is determined by the GPPS to determine a location based on the GPM described above. In this case, using the GPM of the individual base station, the proximity of the receiving field strength of the base station can be formed at the location t must be specified. Use the correction data to pull i' ci, ji is bit '· · · / C}, je {1, · · ♦ , B} The relevant GMP Mj is formed for individual base stations, and the model Mj is based on these correction points j. Data Dj, where the base station j can be received:
Dj= { (Xi,Cj,j) ·· Ci,j 关 〇 }。 在應用階段’納入考慮的模型僅有可在必須指明之場所 處能被接收之基地台的模型。 在必須指明之場所t處之可接收場強度之近似度導出等 式如下: (3.1) L(t) = Π p(sj|^j f t). j:Sj 关0 1260868 在點t之資料Dj的GPM之假定分佈係藉由 ptj|Dj, 而指定。此一假定之分佈係一次元高斯分佈,具有根據(2.4) 等式之均値及變異量。 現在說明搜尋GPPS點,在此處之接收場強度之共同近 似度爲最大値。必須指明之位置t係藉L (t)關於t之最佳化 或最大化而獲得。 t = arg max L(t) = arg max Σ log p(sj|Djt). (3·2) t t j:Sj 关o 此一最佳化作業係L(t)關於t之梯度之前向解答。 公式(3.2)聯合梯度資訊可藉由標準的數値最佳化方 法,例如“標度化共軛梯度”等加以求解,且搜尋中的位置 t可因此而估測。第3圖說明相關公式。 另一種作法是藉由網格法以求解L(t),其中有關之計算 係在網格點L(t)執行,且最大値已指出。 GPPS可清楚說明如下: 接收有關一特定基地台之信號之一高場強度大約顯示 出行動通信之使用者係位於該基地台之一非常小的半徑範 圍內。因此可知一非常低的場強度顯示出該使用者係位於非 常大的半徑或範圍內。這些個別場所估測之疊加提供了估測 結果之最後位置t。 校正點之最佳選擇(第1圖,100) 一適當之定位系統,例如GPPS,應使用最少數量之校 正點加以實施,藉以使校正所需之工作愈少愈好。 同時,校正點應最佳化地涵蓋可能區域化之相關面積。 -19- 1260868 數種作法可由先前技術得知[7]。 如[7]所述之方法在此被選擇用於校正點之最佳選擇, 其中該方法之結果係校正點之一六角形網格。 本說明書引用以下出版物: [1] Peyrard,F,Soutou,C·,Mercier,J.J.:在一 WLAN 中之 行動站區域化,Proceedings of the 25th Annual IEEE Conference on Local Computer Networks (LCN’00), Tampa, Florida (2000) 1 36- 1 42 [2] Hassan-Ali,M·,Pahlavan,K·:有關場地特定之室內無線 電傳送預測之一新統計模型,根據幾何光學及幾何機 率,IEEE Transactions on Wireless Communications 1 (2002) 112-124 [3] Howard,A.,Siddigi,S.,Sukhatme,G.S.:使用無線太網 路之區域化的實驗硏究,Proceedings of the 4th International Conference on Field and Service Robotics, Japan (2003) [4] Bahl,P·,Padmanabhan,V.N.: RADAR: — 個建築物內以 射頻爲基礎之使用者定位及追蹤系統,Proceedings of IEEE INFOCOM 2000. Volume 2., Tel Aviv, Israel (2000) 775-784 [5] Rauh ? A., Briechle,K·,Hanebeck, U.D.? Bamberger, J.5 Η o f fm an n,C · : D E C T行動電話之區域化,根據一新非線 性濾波技術,Proceedings of SPIE Bd. 5084,Aero Sense Symposium,Orlando, Florida (2003) -20- 1260868 [6] Gneiting,T. “小型基礎之相關函數”,journal of Multivariate Analysis, 8 3 (2) : 493 - 5 08,2002 [7] Hamprecht,F.A· and Agrell,Ε· “開發材料領域:空間取 樣設計及子集合選擇”,《Τ·Ν· Cawse,ed·,有關於組合式 及高生產量之材料發展之實驗設計,John Wiley & Sons, 2002 [8] Mackay,D.J· “局斯程序簡介” ’ C.M. Bishop,ed·,神經 網路及機器學習,ν〇1· 168, NATO Asi Series. Series F, 計算機及系統科學,Springer Verlag,1998 [9] Rasmussen,C.E. “高斯程序評估及非線性遞迴之其他方 法 ’’,P h. D. thesis,University of Toronto, 19 9 6 [10] Stein,M. “空間資料之內插,有關克利金法之理論”, Springer Verlag,1999 [11] Williams,C.K· “高斯程序”,Μ· Arbib,ed·,腦之理論及 神經網路手冊,MIT Press,2nd edn.,2002 [12] Williams,C.K. and Rasmussen,C.E. “遞迴之高斯程 序 ”,D.S. Tour etzky, Μ. C. Mozer,and M.E. Hasselmo, eds·,神經資訊處理系統之發展8,MIT Press,1 996。 【圖示簡單說明】 第1圖顯示根據一典型實施例,用來決定使用高斯程序 定位系統(GPPS)之一位置的過程; 第2a及2b圖顯示一 GP Μ(第2a圖)之圖表,已使用原 始的校正資料加以調整,以及GMP圖表(第2b圖),其已利 用來自於GPM之資料加以平滑處理; 1260868 第3圖顯示用來載明關於必須指定位置t之導函數的等 式。 典型實施例:含有數個基地台之一通信網路(D E C T網 路)之中的高斯程序定位系統(GPP s)。 【元件符號說明】 100 指出最佳校正位置 110 執行校正量測,蒐集校正資料 120 使用高斯程序,將傳送行爲以前向模型製作Dj= { (Xi,Cj,j) ·· Ci,j 关 〇 }. The model considered in the application phase is only a model of the base station that can be received at the location that must be specified. The approximate equation for the acceptable field strength at the location t that must be specified is derived as follows: (3.1) L(t) = Π p(sj|^jft). j:Sj off 0 1260868 at point t of the data Dj The assumed distribution of GPM is specified by ptj|Dj. This assumed distribution is a one-dimensional Gaussian distribution with uniformity and variation according to the equation (2.4). It is now illustrated to search for GPPS points where the common near-field strength of the received field strength is the maximum 値. The position t that must be specified is obtained by L (t) for the optimization or maximization of t. t = arg max L(t) = arg max Σ log p(sj|Djt). (3·2) t t j:Sj off o This optimized operating system L(t) is solved with respect to the gradient of t. The joint gradient information of equation (3.2) can be solved by a standard number optimization method, such as "scaling conjugate gradient", and the position t in the search can be estimated accordingly. Figure 3 illustrates the relevant formula. Another approach is to solve L(t) by the grid method, where the calculation is performed at grid point L(t) and the maximum 値 has been indicated. The GPPS can be clearly stated as follows: The high field strength of one of the signals received for a particular base station indicates that the user of the mobile communication is within a very small radius of one of the base stations. It is therefore known that a very low field strength indicates that the user is located within a very large radius or range. The superposition of these individual site estimates provides the final position t of the estimate. The best choice for calibration points (Fig. 1, 100) A suitable positioning system, such as GPPS, should be implemented with a minimum number of calibration points, so that the less work required for calibration is as good as possible. At the same time, the calibration point should optimally cover the relevant area of possible regionalization. -19- 1260868 Several methods can be known from prior art [7]. The method as described in [7] is selected here for the best choice of correction points, wherein the result of the method is a hexagonal grid of correction points. This manual refers to the following publications: [1] Peyrard, F, Soutou, C., Mercier, JJ: Regionalization of Mobile Stations in a WLAN, Proceedings of the 25th Annual IEEE Conference on Local Computer Networks (LCN'00), Tampa, Florida (2000) 1 36- 1 42 [2] Hassan-Ali, M., Pahlavan, K.: A new statistical model for site-specific indoor radio transmission predictions, based on geometric optics and geometric probabilities, IEEE Transactions on Wireless Communications 1 (2002) 112-124 [3] Howard, A., Siddigi, S., Sukhatme, GS: Regionalization experiments using wireless tera-networks, Proceedings of the 4th International Conference on Field and Service Robotics , Japan (2003) [4] Bahl, P·, Padmanabhan, VN: RADAR: — RF-based user location and tracking system in a building, Proceedings of IEEE INFOCOM 2000. Volume 2., Tel Aviv, Israel (2000) 775-784 [5] Rauh ? A., Briechle, K., Hanebeck, UD? Bamberger, J.5 Η of fm an n, C · : Regionalization of DECT mobile phones, based on a new nonlinear filter Technology , Proceedings of SPIE Bd. 5084, Aero Sense Symposium, Orlando, Florida (2003) -20- 1260868 [6] Gneiting, T. "Small basic correlation function", journal of Multivariate Analysis, 8 3 (2) : 493 - 5 08, 2002 [7] Hamprecht, FA· and Agrell, Ε· “Development of Materials: Spatial Sampling Design and Sub-Collection Selection”, “Τ·Ν·Cawse, ed·, Development of Materials for Combined and High-Production Experimental Design, John Wiley & Sons, 2002 [8] Mackay, DJ· "Introduction to the Bureau of Procedures" 'CM Bishop, ed·, Neural Network and Machine Learning, ν〇1· 168, NATO Asi Series. Series F , Computer and Systems Science, Springer Verlag, 1998 [9] Rasmussen, CE "Gaussian Program Evaluation and Other Methods of Nonlinear Recursion", P h. D. thesis, University of Toronto, 19 9 6 [10] Stein, M. “Interpolation of Spatial Data, Theory of Kriging Method”, Springer Verlag, 1999 [11] Williams, CK· “Gauss Program”, Μ·Arbib, ed·, Brain Theory and Neural Network Handbook, MIT Press, 2nd edn., 2002 [12] W Illiams, C.K. and Rasmussen, C.E. "Recursive Gaussian Procedures", D.S. Tour etzky, Μ. C. Mozer, and M.E. Hasselmo, eds., Development of Neural Information Processing Systems 8, MIT Press, 1996. BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 shows a process for determining the position of one of the GMs using a Gaussian Program Positioning System (GPPS) according to an exemplary embodiment; FIGS. 2a and 2b are diagrams showing a GP Μ (Fig. 2a), It has been adjusted using the original calibration data, as well as the GMP chart (Figure 2b), which has been smoothed using data from the GPM; 1260868 Figure 3 shows the equation used to specify the derivative function for the position t must be specified . Exemplary Embodiment: A Gaussian Program Positioning System (GPP s) among communication networks (D E C T networks) of a plurality of base stations. [Component Symbol Description] 100 Indicates the best correction position. 110 Performing the calibration measurement and collecting the calibration data. 120 Using the Gaussian program, the transmission behavior is made to the previous model.
130 指出近似度 140 解答最佳化問題,決定最大近似度作爲必須指明之位置130 indicates the approximation 140 solves the optimization problem and determines the maximum approximation as the position that must be specified
-22--twenty two-
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