本文選題：地磁場(chǎng)　切入點(diǎn)：粒子濾波　出處：《東華理工大學(xué)》2017年碩士論文

【摘要】：現(xiàn)如今,人們的日常活動(dòng)大多在室內(nèi)進(jìn)行,導(dǎo)致基于位置感知的應(yīng)用的激增,對(duì)于位置服務(wù)的需求也與日俱增。室內(nèi)定位主要存在三方面的難點(diǎn):首先,障礙物,干擾源較多,室內(nèi)環(huán)境呈現(xiàn)出較強(qiáng)的動(dòng)態(tài)性。其次,測(cè)量誤差。最后,定位往往需要依賴于其他輔助設(shè)備,設(shè)備本身的局限性。針對(duì)以上定位的三大難點(diǎn),綜合室內(nèi)地磁場(chǎng)的分布特點(diǎn)。在無(wú)干擾源的條件下,室內(nèi)地磁場(chǎng)變化微弱。但真實(shí)室內(nèi)空間,隨機(jī)分布的諸多干擾源形成地磁場(chǎng)的獨(dú)特分布。為此本文利用干擾源對(duì)磁場(chǎng)特征的影響完成定位過(guò)程,首先利用特征梯度去建立特征指紋庫(kù),以此來(lái)消除輔助設(shè)備干擾。但利用地磁場(chǎng)作為定位載體依然面臨著一大問(wèn)題。復(fù)雜的室內(nèi)分布也會(huì)存在局部的特征分布平滑區(qū)域,稱之為“盲區(qū)”,例如,走廊,過(guò)道等區(qū)域。為解決此類問(wèn)題,基于數(shù)理統(tǒng)計(jì)方法的粒子濾波,以總體的樣本分布去估測(cè)真實(shí)位置點(diǎn)分布,利用粒子濾波匹配明顯的特征分布區(qū)域。另外,本文通過(guò)建立預(yù)處理過(guò)程進(jìn)行區(qū)域劃分,待區(qū)域識(shí)別完成后,建立自適應(yīng)粒子濾波,從而解決“盲區(qū)”定位問(wèn)題。本文選取一個(gè)400平方米樣本空間,通過(guò)移動(dòng)設(shè)備采集室內(nèi)空間樣本點(diǎn)的地磁場(chǎng)強(qiáng)度和三分量值,經(jīng)最臨近,三次樣條,雙線性,雙三次四種插值方法分別建立室內(nèi)特征分布指紋庫(kù)。之后,進(jìn)行特征區(qū)域劃分,并且利用觀測(cè)值去尋找最佳匹配方案。最后,將感知信息融入粒子濾波去建立自適應(yīng)過(guò)程,自適應(yīng)優(yōu)化濾波參數(shù)。此預(yù)處理過(guò)程建立測(cè)試點(diǎn)和指紋庫(kù)之間的關(guān)聯(lián),以便于更好的去識(shí)別測(cè)試點(diǎn)。本文主要解決以下三個(gè)方面的問(wèn)題。首先,通過(guò)有限樣本點(diǎn)建立室內(nèi)地磁特征指紋庫(kù)。進(jìn)而,利用室內(nèi)地磁場(chǎng)特征分布的獨(dú)特性和特征指紋庫(kù)與測(cè)試點(diǎn)之間的關(guān)聯(lián)度,對(duì)室內(nèi)空間進(jìn)行敏感度區(qū)域劃分。最后,將室內(nèi)特征分布與測(cè)試點(diǎn)之間的關(guān)聯(lián)信息融入粒子濾波,自適應(yīng)匹配環(huán)境特征,自動(dòng)優(yōu)化濾波參數(shù)。通過(guò)測(cè)試,最臨近插值所建立的指紋庫(kù)效果最差。自適應(yīng)粒子濾波較傳統(tǒng)粒子濾波算法,定位誤差穩(wěn)定,精度高。在不同分辨率條件下,191*191,381*381,951*951,經(jīng)過(guò)100次隨機(jī)定位測(cè)試,定位誤差分別平均減小0.41m,1.25m,1.71m。
[Abstract]:Nowadays, most of people's daily activities are carried out indoors, which leads to the proliferation of location-aware applications and the increasing demand for location-based services. There are three main difficulties in indoor positioning: first, there are many obstacles and sources of interference. Indoor environment shows strong dynamic. Secondly, measurement error. Finally, positioning often depends on other auxiliary equipment, the limitations of the equipment itself. The distribution of indoor geomagnetic field is synthesised. The change of indoor geomagnetic field is weak under the condition of no interference source. But the real indoor space, The random distribution of many interference sources forms the unique distribution of the geomagnetic field. In this paper, the influence of the interference source on the magnetic field features is used to complete the localization process. Firstly, the characteristic fingerprint database is established by using the characteristic gradient. But using the geomagnetic field as a positioning carrier still faces a major problem. Complex indoor distributions can also have local feature distribution smoothing areas, known as "blind areas," such as corridors. In order to solve this problem, particle filter based on mathematical statistics is used to estimate the distribution of real location points with the total sample distribution, and the particle filter is used to match the obvious feature distribution area. In this paper, the preprocessing process is established to divide the area, and the adaptive particle filter is set up to solve the problem of "blind zone" location after the region recognition is completed. In this paper, a sample space of 400 square meters is selected. The geomagnetic field intensity and three component values of indoor spatial sample points were collected by mobile devices. The fingerprint database of indoor feature distribution was established by the nearest, cubic spline, bilinear and bicubic interpolation methods, and then the feature regions were divided. Finally, the perceptual information is incorporated into the particle filter to establish the adaptive process and the filter parameters are adaptively optimized. The preprocessing process establishes the correlation between the test points and the fingerprint database. In order to better identify the test points. This paper mainly solves the following three problems. Firstly, the indoor geomagnetic feature fingerprint database is built through limited sample points. By using the uniqueness of the indoor geomagnetic field characteristic distribution and the correlation degree between the characteristic fingerprint database and the test points, the sensitivity region of indoor space is divided. Finally, the correlation information between indoor characteristic distribution and test point is incorporated into particle filter. Adaptive matching of environmental features, automatic optimization of filtering parameters. By testing, the closest interpolation fingerprint database is the worst. The adaptive particle filter is more stable than the traditional particle filter algorithm, and the localization error is stable. The accuracy is high. After 100 random positioning tests, the positioning error is reduced by 0.41mt / 1.25mt / 1.71mrespectively under different resolution conditions.
【學(xué)位授予單位】：東華理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN713;P318

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