【摘要】：傳感器技術(shù)及移動(dòng)互聯(lián)網(wǎng)的飛速發(fā)展促進(jìn)位置服務(wù)逐漸滲透到了人類(lèi)活動(dòng)的各個(gè)方面,位置信息由此成為生活中至關(guān)重要的組成部分。幾十年來(lái),隨著室外定位技術(shù)的日趨成熟,人們對(duì)于定位技術(shù)的探索逐漸轉(zhuǎn)到室內(nèi)。盡管出現(xiàn)了多種定位技術(shù),但至今仍然缺乏一種高精度、高可靠的解決方案。目前使用規(guī)模最廣的室內(nèi)定位方法主要為指紋定位和PDR(Pedestrian Dead Reckoning)定位,但是無(wú)線信號(hào)容易受周?chē)h(huán)境的影響,導(dǎo)致指紋定位的精度較低,性能不穩(wěn)定;而PDR定位由于無(wú)法獲知起始位置及誤差累積的影響也難當(dāng)大任。本文針對(duì)指紋定位精度低、性能差兩大問(wèn)題,研究融合RSSI(Received Signal Strength Indicator)及IMU(Inertial Measurement Unit)數(shù)據(jù)的高可靠性定位方法,獲得了性能穩(wěn)定的高精度定位體驗(yàn)。本文的研究成果主要如下:優(yōu)化了指紋定位算法。通過(guò)研究設(shè)備兼容性及最佳掃描間隔的設(shè)定優(yōu)化了數(shù)據(jù)采集,減輕了定位數(shù)據(jù)異常對(duì)指紋匹配的影響;然后根據(jù)RSSI的統(tǒng)計(jì)特性提出了加權(quán)距離模型,成功提高了指紋匹配的成功率;并利用信號(hào)源位置實(shí)現(xiàn)了定位結(jié)果的自動(dòng)校正。實(shí)驗(yàn)證明優(yōu)化后的指紋定位算法顯著提高了定位精度。設(shè)計(jì)了一種粒子濾波融合定位算法。使用優(yōu)化后的指紋定位輸出生成粒子群,通過(guò)計(jì)算粒子位移與PDR位移的相似度求得粒子權(quán)值,從而限制定位結(jié)果的異常跳動(dòng),動(dòng)態(tài)提升了定位算法的穩(wěn)定性;并提出了一種誤差反饋機(jī)制,避免了誤差累積的影響。實(shí)際環(huán)境下的實(shí)驗(yàn)結(jié)果表明融合定位算法較好地反映了真實(shí)的運(yùn)動(dòng)軌跡。同時(shí),定位精度及穩(wěn)定性都得到了改善。
[Abstract]:With the rapid development of sensor technology and mobile Internet, location services have gradually penetrated into all aspects of human activities, so location information has become a vital part of life. In recent decades, with the maturation of outdoor positioning technology, the exploration of positioning technology has gradually shifted to indoor. Despite the emergence of a variety of positioning technology, but still lack of a high-precision, high-reliable solution. At present, the most widely used indoor positioning methods are fingerprint location and PDR (Pedestrian Dead Reckoning) location, but the wireless signal is easily affected by the surrounding environment, which leads to the low precision and unstable performance of fingerprint location. However, it is difficult for PDR location to know the starting position and the effect of error accumulation. Aiming at the problems of low precision and poor performance of fingerprint location, this paper studies a high reliability localization method combining RSSI (Received Signal Strength Indicator) and IMU (Inertial Measurement Unit) data, and obtains a high precision localization experience with stable performance. The main results of this paper are as follows: the fingerprint location algorithm is optimized. By studying the compatibility of the equipment and the setting of the best scanning interval, the data acquisition is optimized, and the influence of the abnormal location data on the fingerprint matching is reduced. Then according to the statistical characteristics of RSSI, a weighted distance model is proposed, which successfully improves the success rate of fingerprint matching, and realizes the automatic correction of location results by using the location of the signal source. The experimental results show that the optimized fingerprint location algorithm improves the location accuracy significantly. A particle filter fusion localization algorithm is designed. Using the optimized fingerprint location output to generate particle swarm, the particle weight value is obtained by calculating the similarity between particle displacement and PDR displacement, which limits the abnormal runout of the localization results and dynamically improves the stability of the localization algorithm. An error feedback mechanism is proposed to avoid the effect of error accumulation. The experimental results show that the fusion algorithm can well reflect the real motion trajectory. At the same time, the positioning accuracy and stability are improved.
【學(xué)位授予單位】：武漢大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TN713

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本文編號(hào)：2333586