融合地磁與RSSI的室內(nèi)定位粒子濾波改進算法研究
發(fā)布時間：2018-07-27 12:00
【摘要】：隨著移動互聯(lián)網(wǎng)技術的迅速發(fā)展,基于位置的服務(Location Based Service,LBS)得到人們更多的關注。在室外環(huán)境中,蜂窩網(wǎng)絡技術配合全球定位系統(tǒng)(Global Positioning System,GPS)可以實現(xiàn)快速準確的定位效果,已經(jīng)廣泛應用在各行業(yè)中。而現(xiàn)代社會中,調(diào)查表明普通人平均每天至少有80%的時間處于室內(nèi)環(huán)境,如何提供室內(nèi)環(huán)境下的便捷定位服務成為亟待解決的問題。相關研究人員已經(jīng)提出過初見成效的室內(nèi)定位方案:藍牙、超寬帶、射頻、WiFi信號及光跟蹤等,但是不同技術的定位性能都需要外在條件支持。比如,無線定位技術需要部署成本較高的額外設施,同時在信號傳播時會受到室內(nèi)復雜因素的干擾,產(chǎn)生反射,折射、多路徑效應等,無法保證高精度。因此,單一的定位方案并不能很好地滿足大眾對于各種室內(nèi)場景的定位服務要求。近年來,研究人員發(fā)現(xiàn)現(xiàn)代建筑物內(nèi)部鋼筋混凝土結(jié)構在局部范圍產(chǎn)生地磁異常場,其特征信息長時間內(nèi)保持穩(wěn)定。理論上,可利用不同位置的磁場特征矢量唯一性進行室內(nèi)定位,但在大范圍內(nèi)實現(xiàn)室內(nèi)地磁匹配時,會出現(xiàn)初始濾波收斂緩慢,相似磁場區(qū)域造成定位漂移等問題。另一方面,在無線通信技術快速發(fā)展的推動下,大型室內(nèi)場所遍布WiFi熱點。如果采用基于傳統(tǒng)信號傳播模型的定位技術,研究者可獲得較高的定位精度,但硬件設施昂貴,僅適合理想條件下室內(nèi)場景。而基于WiFi信號強度(RSSI)的位置指紋算法適用性強,操作簡便,但定位精度較低。充分考慮到地磁與WiFi定位方案的優(yōu)缺點,本文提出一種融合室內(nèi)地磁和WiFi信號強度的定位算法,在大范圍定位區(qū)域中先通過無線信號強度粗定位到小區(qū)域,再通過地磁濾波進行下一步精匹配。本文先闡述地磁場特點和室內(nèi)地磁匹配原理,然后介紹了幾種主流室內(nèi)定位技術原理和方法。接下來通過實驗總結(jié)出鋼筋混凝土結(jié)構建筑物內(nèi)部的磁場環(huán)境特點。隨后為了減少磁場測量誤差,提出一種改進限幅濾波與均值濾波算法相結(jié)合的思路,對原始觀測數(shù)據(jù)進行預處理。又對磁傳感器進行硬鐵補償來減少測量噪聲影響。通過實驗證明了克里金插值算法在室內(nèi)局部地磁圖構建中具有較高精度。本文提出的融合地磁與RSSI的定位算法共分為數(shù)據(jù)建庫和匹配定位兩個階段。建庫階段,提前收集定位區(qū)域的磁場強度和RSSI觀測值并建立數(shù)據(jù)庫。定位階段,先通過RSSI觀測指紋,采用所提加分法判定出目標所在小區(qū)域,隨后進行下一步的地磁匹配。融合算法縮小了地磁匹配中隨機粒子的初始搜索范圍,減少粒子數(shù)量和收斂時間,提高系統(tǒng)定位效率。對于經(jīng)典地磁粒子濾波算法匹配中出現(xiàn)的目標估計位置漂移現(xiàn)象,提出利用誤差圓約束思路,來避免地磁圖中相似野值點帶來的干擾,從而提高定位成功率。將改進后的融合算法與經(jīng)典地磁濾波算法進行實驗對比,結(jié)果證明,本文算法有效地提高系統(tǒng)定位精度、降低匹配中濾波收斂時間,增強系統(tǒng)定位穩(wěn)定性,可作為一種室內(nèi)定位方案加以發(fā)展。
[Abstract]:With the rapid development of mobile Internet technology, Location Based Service (LBS) has received more attention. In outdoor environment, the cellular network technology combined with the global positioning system (Global Positioning System, GPS) can achieve rapid and accurate positioning effect, and has been widely used in various industries. In the meeting, the survey showed that average people were in the indoor environment at least 80% time per day. How to provide convenient location services in the indoor environment has become an urgent problem. The relevant researchers have put forward the initial results of indoor positioning schemes: Bluetooth, UWB, radio frequency, WiFi signal and light tracking, but different technology The bit performance needs external support. For example, the wireless positioning technology needs to deploy high cost additional facilities, while the signal transmission will be disturbed by the complex factors in the room, produce reflection, refraction, multipath effect and so on, which can not guarantee high precision. Therefore, a single positioning scheme can not satisfy the public for all kinds of rooms. In recent years, researchers have found that the structure of the reinforced concrete structure inside the modern building produces geomagnetic anomaly in a local range, and its characteristic information remains stable for a long time. In theory, the interior location of the magnetic field feature vectors in different positions can be used, but the indoor geomagnetic match is realized in a large range. When matching, the initial filtering convergence is slow and the similar magnetic field region causes the positioning drift. On the other hand, the large indoor places are all over the WiFi hot spots under the rapid development of wireless communication technology. If the positioning technology based on the traditional signal propagation model is adopted, the researchers can obtain higher positioning accuracy, but the hardware is set up. The location fingerprint algorithm based on WiFi signal intensity (RSSI) is very applicable and easy to operate, but the positioning accuracy is low. Taking full account of the advantages and disadvantages of geomagnetic and WiFi positioning schemes, this paper proposes a localization algorithm for integrating indoor geomagnetic and WiFi signal intensity, which is first connected in a large range location area. In this paper, the characteristics of the geomagnetic field and the principle of indoor geomagnetic matching are introduced. Then the principles and methods of several mainstream indoor positioning techniques are introduced. The following experiments are made to summarize the characteristics of the magnetic field in the structure of the reinforced concrete structure. In order to reduce the error of magnetic field measurement, a method of improving the combination of the limiting filter and the mean filtering algorithm is proposed. The original observation data is pretreated. The magnetic sensor is compensated by hard iron to reduce the influence of measurement noise. It is proved that the Kriging interpolation algorithm has a high precision in the construction of the local geomagnetic map. The fusion geomagnetism and RSSI localization algorithm proposed in this paper is divided into two stages: data building and matching location. In the building phase, the magnetic field intensity and the RSSI observation value of the location area are collected and the database is set up in advance. The location stage is first through the RSSI observation fingerprint, and the proposed addition method is used to determine the small area of the target, and then the next step is carried out. The fusion algorithm reduces the initial search range of the random particles in the geomagnetic matching, reduces the number of particles and the time of convergence, and improves the system location efficiency. The error circle constraint method is proposed to avoid the similar field values in the geomagnetic map. The results of the improved fusion algorithm are compared with the classical geomagnetic filtering algorithm. The results show that the algorithm can effectively improve the positioning accuracy of the system, reduce the convergence time in the matching and enhance the stability of the system, and can be developed as an indoor positioning scheme.
【學位授予單位】：北京建筑大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN713;TN92
本文編號：2147756
[Abstract]:With the rapid development of mobile Internet technology, Location Based Service (LBS) has received more attention. In outdoor environment, the cellular network technology combined with the global positioning system (Global Positioning System, GPS) can achieve rapid and accurate positioning effect, and has been widely used in various industries. In the meeting, the survey showed that average people were in the indoor environment at least 80% time per day. How to provide convenient location services in the indoor environment has become an urgent problem. The relevant researchers have put forward the initial results of indoor positioning schemes: Bluetooth, UWB, radio frequency, WiFi signal and light tracking, but different technology The bit performance needs external support. For example, the wireless positioning technology needs to deploy high cost additional facilities, while the signal transmission will be disturbed by the complex factors in the room, produce reflection, refraction, multipath effect and so on, which can not guarantee high precision. Therefore, a single positioning scheme can not satisfy the public for all kinds of rooms. In recent years, researchers have found that the structure of the reinforced concrete structure inside the modern building produces geomagnetic anomaly in a local range, and its characteristic information remains stable for a long time. In theory, the interior location of the magnetic field feature vectors in different positions can be used, but the indoor geomagnetic match is realized in a large range. When matching, the initial filtering convergence is slow and the similar magnetic field region causes the positioning drift. On the other hand, the large indoor places are all over the WiFi hot spots under the rapid development of wireless communication technology. If the positioning technology based on the traditional signal propagation model is adopted, the researchers can obtain higher positioning accuracy, but the hardware is set up. The location fingerprint algorithm based on WiFi signal intensity (RSSI) is very applicable and easy to operate, but the positioning accuracy is low. Taking full account of the advantages and disadvantages of geomagnetic and WiFi positioning schemes, this paper proposes a localization algorithm for integrating indoor geomagnetic and WiFi signal intensity, which is first connected in a large range location area. In this paper, the characteristics of the geomagnetic field and the principle of indoor geomagnetic matching are introduced. Then the principles and methods of several mainstream indoor positioning techniques are introduced. The following experiments are made to summarize the characteristics of the magnetic field in the structure of the reinforced concrete structure. In order to reduce the error of magnetic field measurement, a method of improving the combination of the limiting filter and the mean filtering algorithm is proposed. The original observation data is pretreated. The magnetic sensor is compensated by hard iron to reduce the influence of measurement noise. It is proved that the Kriging interpolation algorithm has a high precision in the construction of the local geomagnetic map. The fusion geomagnetism and RSSI localization algorithm proposed in this paper is divided into two stages: data building and matching location. In the building phase, the magnetic field intensity and the RSSI observation value of the location area are collected and the database is set up in advance. The location stage is first through the RSSI observation fingerprint, and the proposed addition method is used to determine the small area of the target, and then the next step is carried out. The fusion algorithm reduces the initial search range of the random particles in the geomagnetic matching, reduces the number of particles and the time of convergence, and improves the system location efficiency. The error circle constraint method is proposed to avoid the similar field values in the geomagnetic map. The results of the improved fusion algorithm are compared with the classical geomagnetic filtering algorithm. The results show that the algorithm can effectively improve the positioning accuracy of the system, reduce the convergence time in the matching and enhance the stability of the system, and can be developed as an indoor positioning scheme.
【學位授予單位】：北京建筑大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN713;TN92
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