1243255 玖、發明說明： 【發明所屬之技術領域】 本發明係關於一種定位方法’尤指一種使用混亂樣本策 略之室内定位方法及系統。 5 【先前技術】 隨著無線行動設備的日漸普及’越來越多使用者透過行 動裝置完成生活上的各種活動’而各種無線上網環境也與之 遽增。而由於無線環境的成熟，各種無線環境的加值應用便 10 因應而生，其中一項就是以提供位置資訊的位置感知服務， 透過此種位置感知服務，使用者可以獲得在其附近的各種資 訊與服務，例如，使用者可以查詢「最近的餐廳」、「最近 的大眾交通工具」，而服務提供者就可以根據使用者的位置 提供最適合的資訊。1243255 发明 Description of the invention: [Technical field to which the invention belongs] The present invention relates to a positioning method ', especially an indoor positioning method and system using a chaotic sample strategy. 5 [Previous Technology] With the increasing popularity of wireless mobile devices, 'more and more users complete various activities in their lives through mobile devices', and various wireless Internet environments have also increased. Due to the maturity of the wireless environment, various value-added applications of the wireless environment have emerged in response to the 10, one of which is a location awareness service that provides location information. With this location awareness service, users can obtain a variety of information in their vicinity. And services, for example, users can query "the nearest restaurant", "the nearest public transportation", and the service provider can provide the most suitable information based on the user's location.
15 在戶外，最常利用的位置感知裝置為全球定位系統GPS (Global Position System)，透過GPS可以提供方便且正確 的使用者位置資訊，而依硬體設備的不同而有數公尺到數十 公尺的正確度。然而，另一方面，在室内的環境下則尚無一 套廣為大眾所使用之定位系統，其原因之一為誤差度太大， 20因為室内相對於室外是一個比較小範圍的空間，且室内的格 局也相對比較稠密，所以定位系統的準確度就直接考驗整個 系統的可行性，目前室内定位方法主要可分為兩個步驟，步 驟一主要以 ToA(Time of Arrival)、TdoA(Time Difference of Arrival)、AoA(Angle of Arrival)及 RSSI(Received Signal 6 124325515 In the outdoors, the most commonly used position sensing device is the Global Positioning System GPS (Global Position System). GPS can provide convenient and accurate user location information. Depending on the hardware device, there are several meters to tens of meters. Ruler accuracy. However, on the other hand, in the indoor environment, there is not a positioning system widely used by the public. One of the reasons is that the error is too large. 20 Because indoor is a relatively small space compared to outdoor, and The indoor pattern is relatively dense, so the accuracy of the positioning system directly tests the feasibility of the entire system. At present, the indoor positioning method can be divided into two steps. The first step is mainly based on ToA (Time of Arrival) and TdoA (Time Difference). of Arrival), AoA (Angle of Arrival), and RSSI (Received Signal 6 1243255
Strength Indicator)等技術來獲得基本的位置資訊，例如距離 或是角度等，然這些技術都存在一定程度的誤差；再利用這 些位置資訊，在步驟二中配合三角定位演算法或者樣本比對 演异法就可以達到定位的目的，而這個過程中，因為各種因 5素所造成步驟一所得到資訊的誤差會直接反映到步驟二的 定位結果。另一造成無法在室内的環境下提供為大眾所使用 之定位系統的原因在於額外硬體的成本，因為如果使用者進 入一個特定的室内環境，要先配戴一個額外的硬體設備，則 相對的就增加使用者的不便，另—方面也增加系統的成本。 10因此，室内定位一直都無法有一個很有效的解決方法。 【發明内容】 ^本發明之主要目的係在提供一種使用混亂樣本策略之 室内定位方法及系統，以透過無線訊號強度的特性達到在室 15内環境能夠準確的定位出使用者的位置。 —為達成上述目的，本發明提出一種使用混亂樣本策略之 室内定位方法，此室内空間之不同處設置有多個無線 存取點’每一行動裝置偵測無線存取點之訊號強 度^此方法包括一訊號累積步驟、一混亂處理步驟、 20 一定位步驟及一位置選擇步驟，訊號累積步驟用以 累積多組訊號強度向量，其中一組訊號強度向量代 ^仃動裝置在一時間點偵測得到之所有無線存取 占的Λ唬強度；混亂處理步驟用以--將所有訊號 強度向量之每一訊號強度與其餘訊號強度向量之 7 1243255 其他訊號強度任意組合而產生多組混亂後訊號強 度向量；定位步驟依據前述之多組混亂後訊號強度 向量而分別產生多組位置資訊；位置選擇步驟由前 述多組位置資訊選出一組最佳位置資訊作為最後 5 預測的位置。 本發明亦提出一種使用混亂樣本策略之室内定位系 統，其包括多個無線存取點、一訊號累積模組、一混 亂處理模組、一定位模組、及一位置選擇模組，無 線存取點係設置於一室内空間之不同處，一行動裝 10 置偵測每一無線存取點之訊號強度；訊號累積模組 用以累積多組訊號強度向量，其中一組訊號強度向 量代表行動裝置在一時間點偵測得到之所有無線 存取點的訊號強度；混亂處理模組用以——將所有 訊號強度向量之每一訊號強度與其餘訊號強度向 15 量之其他訊號強度任意組合而產生多組混亂後訊 號強度向量；定位模組依據前述之多組混亂後訊號 強度向量而分別產生多組位置資訊；位置選擇模組 由前述多組位置資訊選出一組最佳位置資訊作為 最後預測的位置。 20 【實施方式】 為能讓貴審查委員能更暸解本發明之技術内 容，特舉一較佳具體實施例說明如下。 8 1243255 圖1顯示應用太 内定位方法的聲户發明之使用混礼樣本策略之室 設置有多個其中，在-室内空間之不同處 當攜帶有行動裂置子〗取點U(ACCeSS P〇int’ AP)’而 此室内空間時(MobUe Nod^之使用者進入 線訊息交換，例:存取點"與行動裝置13進行無 號並二存取點11會持續發出訊標 度，收集所得之：：動裝置13接收以得其訊號強 旎將透過無線網路送回一伺服端 1 2並儲存為訊號转 10 服端12可依昭二徵貝：庫(此為訓練階段)，而飼 則行動裝置13在不同位置對每一 無線存取點1 1收隹+ #吐 〜之汛娩強度，比對目前行動裝置 ;士、立置對每—無線存取點11收集之訊號強 估測該行動裝置13之位置，因而定位使用者所 在之地點。 15 圖2 ”、、員示鈿述伺服端1 2進行定位之運作圖，其·首 先由几遽累積模組21(Signal Accumulation M〇dule)累積行動裝置13在某固定位置與時間點收 集每一無線存取點11之訊號強度，如圖3所示，SSxy 代表一行動裝置13在時間點tx(x==1〜h，h為正整數） 20所偵測到無線存取點y(y==1〜n，n為正整數）之訊號 強度，而行動裝置1 3在時間點χ偵測得到之所有無 線存取點11的訊號強度則以一訊號強度向量sVx表 不。圖中顯不伺服端丨2共累積了 h組訊號強度向量。 9 1243255 再請參照圖2所示，此h組訊號強度向量進一步 由訊號混亂模組22(Signal Scramble Module)進行 混亂處理（Scramble)，以--將所有訊號強度向量 SVX之每一訊號強度與其餘訊號強度向量之其他訊 5 號強度任意組合而產生hn組訊號強度向量SSVX (x=l〜hn)，舉例而言，2組訊號強度向量SVfCSS〗1， SSj)及SViSSS/，SS22)經混亂處理後將產生22 = 4 組訊號強度向量 ssvepSi1，ssj)、ssvees!1， SS22)、SSVHSS〗1，SS22)、SSVHSS、1，ssj)。 10 而定位模組23(Location Estimation Module)則 依據前述之hn組訊號強度向量SSVX而分別產生hn 組位置資訊Lx(x=l〜hn)，其中，對每一組訊號強度 向量SSVX，可採用比對此訊號強度向量SSVx之各個 訊號強度SSxy和訊號特徵資料庫而判定其位置資 15 訊。 位置選擇模組24(Location Selector Module)則 依據移動預測2 5、歷史記錄2 6與地圖知識2 7，而由 前述hn組位置資訊Lx選出一組作為最後預測的位 置，其中，移動預測25用以由使用者之前一位置及 20 移動方向所預測之可能位置，而賦予每一位置資訊 一個位置權重值Wp，歷史記錄26用以由長時間觀察 使用者在此室内空間之移動記錄比對目前位置所 預測之可能位置，而賦予每一位置資訊一個歷史權 重值wh，地圖知識27係描述此室内空間的地圖，例 10 1243255 如，可行走路徑及阻擋物位置等，以避免定位出不 合理的結果，藉此，位置選擇模組可地圖知識27而 去除不可能之位置資訊，並依據移動預測2 5、歷史 記錄2 6、或移動預測2 5與歷史記錄2 6之組合而求出 5 具有最大權重值之位置資訊，以作為最後預測的位 置。 前述移動預測25係基於當使用者移動的時候， 大部分時間都會有一個固定的模式，例如傾向等速 前進，而不會時快時慢或者短時間内作方向上的大 10 幅度改變。所以，以此特性可以增加使用者對於混 亂後的訊號樣本傾向選擇某個特定的方向和距 離。舉例而言，可以藉由短時間内使用者的位置 l(t-l)以及l(t-2)來判斷出移動向量，並且預測下一 點的位置為l(t) + [ l(t-l) - l(t-2)]，就可以簡單的達 15 到預測的目的。除此之外，並可以利用一個對整·個 地圖的加權函數來描述每個位置的可能性，詳細言 之，假設使用者目前位置為（x〇，y〇 )，距離函示 為d ( X，y ) = - ar〇)2 + (2/- Vo)2，歷史平均移動距離 _ 1 """2p2 為，則任一點的機率p ( x，y ) = 丽p ， 20 利用高斯（Gaussian)分佈來加權每個位置出現的機 率，而高斯函式中的變異數p ( variance )可以依使 用者的歷史速度變化程度來做調整，圖4即顯示此 範例之移動預測的示意圖。 11 1243255 前述歷史記錄2 6係且古$ 使用者的移動模式所歸：出t長時間的觀察所有 新的使用者在移動時見樣板。以當一個 5 10 15 以及歷史軌跡，判定使用者和 =用者目前的位置 板最相似，從而預測下一歷史記錄中的哪個樣 資訊中，挎知！裡：^ 罝’.並在混亂出的位置 中曰加選擇預測位置中的機率。 由以上之說明可知，土 度的特性達到在室内環境能確透二無：:：強 者的位置，並且利用混雜訊'公的疋位出使用 位樣本空間，另輔以蒋叙猫&本的技術來增加定 述來提升整/ 、歷史經驗及環境描 干擾效應減到最低，俾有效施夠將環境的 定性與正確性。丨有^進既有定位系統之穩 上述實施例僅係為了方便說明而舉例而已，本 =所主張之權利範圍自應以巾請專利範圍所述 為準’而非僅限於上述實施例。 【圖式簡單說明】 圖1係應用本發明之使用混亂樣本策略之室内定位 20 方法的環境示意圖。 圖2係本發明之使用混亂樣本策略之室内定位系統 之伺服端進行定位之運作圖。 12 1243255 圖3係本發明之使用混亂樣本策略之室内定位系統 之伺服端的訊號累積模組所累積行動裝置在無線 存取點之訊號強度示意圖。 圖4係依據本發明之移動預測的示意圖。 5 【圖號說明】 (1 1) 無線存取點 (12) 伺服端 (13) 行動裝置 10 (2 1)訊號累積模組 (22) 訊號混亂模組 (23) 定位模組 (24) 位置選擇模組 (25) 移動預測 15 (26)歷史記錄 (27) 地圖知識 13Strength Indicator) to obtain basic position information, such as distance or angle. However, these technologies have a certain degree of error. Then, using these position information, in step 2, cooperate with the triangular positioning algorithm or sample comparison. Method can achieve the purpose of positioning, and in this process, because of various factors caused by 5 factors, the error of the information obtained in step 1 will be directly reflected in the positioning result of step 2. Another reason that it is impossible to provide a positioning system for the public in an indoor environment is the cost of additional hardware, because if a user enters a specific indoor environment, he must first wear an additional hardware device. It will increase the user's inconvenience, and also increase the cost of the system. 10 Therefore, indoor positioning has not been able to have a very effective solution. [Summary of the Invention] ^ The main purpose of the present invention is to provide an indoor positioning method and system using a chaotic sample strategy, so as to reach the position of the user in the environment in the room 15 accurately through the characteristics of wireless signal strength. —In order to achieve the above-mentioned object, the present invention proposes an indoor positioning method using a chaotic sample strategy. Multiple wireless access points are set at different places in the indoor space. 'Each mobile device detects the signal strength of the wireless access point ^ This method Including a signal accumulation step, a chaotic processing step, a positioning step and a position selection step, the signal accumulation step is used to accumulate multiple sets of signal strength vectors, where one set of signal strength vectors is detected by the mobile device at a point in time Obtained Λ strength of all wireless access; the chaos processing step is to combine any signal strength of all signal strength vectors with 7 1243255 of other signal strength vectors in any combination to generate multiple sets of chaotic signal strength Vector; the positioning step generates multiple sets of position information respectively according to the aforementioned multiple sets of chaotic signal strength vectors; the position selection step selects a set of best position information from the aforementioned multiple sets of position information as the last 5 predicted positions. The invention also proposes an indoor positioning system using a chaotic sample strategy, which includes a plurality of wireless access points, a signal accumulation module, a chaos processing module, a positioning module, and a position selection module for wireless access. The points are set at different places in an indoor space. A mobile device detects the signal strength of each wireless access point. The signal accumulation module is used to accumulate multiple sets of signal strength vectors, one of which represents the mobile device. The signal strengths of all wireless access points detected at a point in time; the chaos processing module is used to-arbitrarily combine each signal strength of all signal strength vectors with the rest of the signal strength to 15 other signal strengths and generate Multiple sets of signal strength vectors after chaos; the positioning module generates multiple sets of location information according to the aforementioned multiple sets of signal strength vectors after chaos; the location selection module selects a set of best location information from the aforementioned multiple sets of location information as the final prediction position. [Embodiment] In order to allow your review committee to better understand the technical content of the present invention, a preferred specific embodiment is described below. 8 1243255 Figure 1 shows that there are multiple rooms using the mixed sample strategy invented by the sound user who applied the internal positioning method. Among them, there are mobile crackers in different places in the indoor space. Take the point U (ACCeSS P. int 'AP)' and when this indoor space (MobUe Nod ^ users enter the line message exchange, for example: access point " numberless and mobile device 13 and access point 11 will continue to send signal scales, collect The result: the mobile device 13 receives the signal to obtain a strong signal. It will be sent back to a server 12 through the wireless network and stored as a signal 10. The server 12 can be collected according to Zhao Erzheng: Library (this is the training stage), and Feed the mobile device 13 to each wireless access point 11 at a different position + 1 吐 + # spit ~ the intensity of the delivery, compared with the current mobile device; Shi, stand for each — the wireless access point 11 signal is stronger The position of the mobile device 13 is estimated, so the user's location is located. 15 Figure 2 ”, the instructions show the operation of the server 12 positioning, which first consists of a few accumulation modules 21 (Signal Accumulation M〇dule) Cumulative mobile device 13 at a fixed location and time Set the signal strength of each wireless access point 11, as shown in Figure 3, SSxy represents a mobile device 13 at time tx (x == 1 ~ h, h is a positive integer) 20 detected wireless access points The signal strength of y (y == 1 ~ n, n is a positive integer), and the signal strengths of all the wireless access points 11 detected by the mobile device 13 at time χ are represented by a signal strength vector sVx. In the figure, the servo end 丨 2 has accumulated a total of h signal strength vectors. 9 1243255 Please refer to FIG. 2 again, this h signal strength vector is further scrambled by the Signal Scramble Module 22 (Scramble ) To --- combine each signal strength of all signal strength vectors SVX with the other signal strength vectors of the remaining signal strength vectors to generate an hn group of signal strength vectors SSVX (x = 1-hn), for example, 2 Group signal strength vector SVfCSS〗 1, SSj) and SViSSS /, SS22) After chaos processing, 22 = 4 groups of signal strength vectors ssvepSi1, ssj), ssvees! 1, SS22), SSVHSS [1, SS22), SSVHSS, 1, ssj). 10 And Location Estimation Module 23 is based on the above hn The signal strength vector SSVX is used to generate hn group position information Lx (x = 1 ~ hn). Among them, for each signal strength vector SSVX, each signal strength SSxy and signal characteristic data of the signal strength vector SSVx can be compared. Library and determined its location information. The Location Selector Module 24 is based on the mobile prediction 25, the historical record 26, and the map knowledge 27, and the last hn group of location information Lx selects a group as the last predicted position. Among them, the mobile prediction 25 uses Based on the user's previous position and the possible position predicted by the 20 movement direction, each position information is given a position weight value Wp, and the history record 26 is used to compare the current movement record of the user in this indoor space by comparing the current Location predicted possible location, and each location information is given a historical weight value wh, map knowledge 27 is a map describing this indoor space, such as 10 1243255, such as walkable paths and obstacle locations, to avoid unreasonable positioning As a result, the location selection module can map knowledge 27 to remove impossible location information, and obtain 5 based on the combination of mobile prediction 25, historical record 2 or mobile prediction 2 5 and historical record 2 6 Location information with the highest weight value as the last predicted location. The aforementioned movement prediction 25 is based on the fact that when the user moves, most of the time there will be a fixed pattern, such as the tendency to move forward at a constant speed, without changing the direction by a large amount in a short time. Therefore, this feature can increase the user's tendency to choose a specific direction and distance for the scrambled signal samples. For example, the user's position l (tl) and l (t-2) can be used to determine the movement vector in a short time, and the position of the next point is predicted to be l (t) + [l (tl)-l (t-2)], you can easily achieve 15 to the purpose of prediction. In addition, a weighting function for the entire map can be used to describe the possibility of each location. In detail, it is assumed that the user's current location is (x0, y〇) and the distance function is d ( X, y) =-ar〇) 2 + (2 /-Vo) 2, the historical average moving distance _ 1 " " " 2p2 is, then the probability of any point p (x, y) = Li p, 20 Gaussian distribution is used to weight the probability of occurrence at each location, and the number of variances p (variance) in the Gaussian function can be adjusted according to the historical speed change of the user. Figure 4 shows the movement prediction of this example. schematic diagram. 11 1243255 The aforementioned historical record 2 6 series and the ancient user's movement mode are attributed: observe all the new users for a long time to see the template when moving. Based on a 5 10 15 and historical trajectory, it is determined that the current position of the user and the user are the most similar, so as to predict which kind of information in the next historical record, know! Lane: ^ 罝 ’. And in the chaotic position, add the probability of choosing the predicted position. As can be seen from the above description, the soil characteristics can be clearly determined in the indoor environment: the position of the strong, and the use of promiscuous 'public niches' to generate bit sample space, supplemented by Jiang Xu cat & this The technology can be used to increase the description to improve the overall, historical experience, and environmental description interference effects to a minimum, and effectively apply the qualitative and correctness of the environment.丨 The stability of the existing positioning system has been described above. The above embodiments are just examples for the convenience of explanation. The scope of the claimed rights should be based on the scope of the patent claims, and not only the above embodiments. [Brief description of the figure] FIG. 1 is a schematic diagram of an environment in which the indoor positioning method 20 using the chaotic sample strategy of the present invention is applied. FIG. 2 is an operation diagram of positioning by the server end of the indoor positioning system using the chaotic sample strategy of the present invention. 12 1243255 Figure 3 is a schematic diagram of the signal strength of a mobile device at a wireless access point accumulated by the signal accumulation module of the server of the indoor positioning system using the chaotic sample strategy of the present invention. FIG. 4 is a schematic diagram of motion prediction according to the present invention. 5 [Illustration of drawing number] (1 1) Wireless access point (12) Server (13) Mobile device 10 (2 1) Signal accumulation module (22) Signal chaos module (23) Positioning module (24) Location Selection module (25) Mobile prediction 15 (26) History (27) Map knowledge 13