發(fā)布時間：2018-06-06 23:58

  本文選題：室內(nèi)定位 + WIFI　； 參考：《電子科技大學(xué)》2014年碩士論文

【摘要】：隨著移動互聯(lián)網(wǎng)時代的到來和移動技術(shù)的不斷發(fā)展,LBS(Location-based services)即基于位置的服務(wù)越來越被普羅大眾所熟悉。在移動互聯(lián)網(wǎng)浪潮普及的今天,LBS通過用戶使用智能手機(jī)和智能平板訪問互聯(lián)移動網(wǎng)絡(luò)的過程中提供位置信息而被廣泛服務(wù)應(yīng)用于社交娛樂,而隨著移動手機(jī)和移動平板的普及,室內(nèi)定位服務(wù)將成為移動互聯(lián)網(wǎng)時代的核心功能,變得越來越重要。本文針對這一熱點方向,在基于蘋果的IOS系統(tǒng)和MAC OS系統(tǒng)上設(shè)計并實現(xiàn)了一套基于WIFI的室內(nèi)定位系統(tǒng)。創(chuàng)新性的在基于位置指紋的定位算法上做出了優(yōu)化和改進(jìn)研究。提出了對采集到得RSSI信號進(jìn)行靜態(tài)和動態(tài)優(yōu)化處理的兩種方法,一種是靜態(tài)處理方法,對采集到作為數(shù)據(jù)指紋的信號進(jìn)行高斯分布濾波;另一種是動態(tài)處理方法,采用修正加權(quán)濾波的方法,在實時定位階段,采樣的RSSI信號需要在短時間內(nèi)完成處理,減小了定位中出現(xiàn)大幅偏差的概率。在NNSS-AVG算法和高斯概率估計兩種算法的基礎(chǔ)上,提出了一種混合改進(jìn)算法,第一階段利用NNSS算法快速選取一定數(shù)量的預(yù)定位點。第二階段利用高斯概率分布算法對預(yù)定位點進(jìn)行位置概率估計計算,最后通過加權(quán)系數(shù)的方法得出最終的定位計算結(jié)果,該方法有效地綜合了兩類算法的優(yōu)點,在龐大的定位數(shù)據(jù)對比計算量的前提下提升了定位系統(tǒng)的定位響應(yīng)時間和定位精準(zhǔn)度。測試表明,基于NNSS-AVG算法和高斯概率估計的混合算法能夠有效的提升最終定位的精準(zhǔn)度,定位響應(yīng)時間也能夠得到一定程度的提高。
[Abstract]:With the advent of the mobile Internet era and the continuous development of mobile technology, the location based services are becoming more and more familiar to the general public. Today, with the popularity of mobile Internet, LBS is widely used in social entertainment by providing location information through the use of smartphones and smart tablets to access the interconnected mobile network, and with the popularity of mobile phones and mobile tablets, Indoor positioning service will become the core function of the mobile Internet era, becoming more and more important. Aiming at this hot spot, this paper designs and implements a set of indoor positioning system based on WIFI on IOS system and MAC OS system based on Apple. The innovative location algorithm based on location fingerprint is optimized and improved. Two methods for static and dynamic optimal processing of collected RSSI signals are proposed, one is static processing method, the other is dynamic processing method. In the real time localization stage, the sampled RSSI signal needs to be processed in a short time by using the modified weighted filtering method, which reduces the probability of large deviation in the localization. On the basis of NNSS-AVG algorithm and Gao Si probability estimation algorithm, a hybrid improved algorithm is proposed. In the first stage, NNSS algorithm is used to quickly select a certain number of predetermined sites. In the second stage, the Gao Si probability distribution algorithm is used to estimate the location probability of the predetermined site. Finally, the final location result is obtained by the method of weighting coefficient. The method effectively integrates the advantages of the two kinds of algorithms. The positioning response time and positioning accuracy of the positioning system are improved under the premise of a large amount of comparison and calculation of the positioning data. The test results show that the hybrid algorithm based on NNSS-AVG algorithm and Gao Si probability estimation can effectively improve the accuracy of the final location, and the localization response time can be improved to a certain extent.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP311.52;TN92

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

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本文編號：1988697