基于LANDMARC與壓縮感知的雙段式室內(nèi)定位算法
doi: 10.11999/JEIT151050
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1.
(遼寧大學(xué)物理學(xué)院 沈陽(yáng) 110036) ②(國(guó)網(wǎng)山東省電力公司電力科學(xué)研究院 濟(jì)南 250002)
國(guó)家自然科學(xué)基金(61403176),遼寧省教育廳科學(xué)技術(shù)研究項(xiàng)目(L2013003)
Double Stage Indoor Localization Algorithm Based on LANDMARC and Compressive Sensing
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1.
(College of Physics, Liaoning University, Shenyang 110036, China)
The National Natural Science Foundation of China (61403176), Science and Technology Research Project of Educational Commission of Liaoning Province of China (L2013003)
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摘要: 鑒于已有室內(nèi)定位算法定位精度與運(yùn)算效率之間的矛盾,該文提出一種將LANDMARC區(qū)域定位與基于模擬退火優(yōu)化正則化正交匹配追蹤(SROMP)的壓縮感知位置估計(jì)相結(jié)合的雙段式定位算法(LANDMARC- SROMP CS)。首先,利用LANDMARC定位算法快速鎖定目標(biāo)所在區(qū)域范圍;在鎖定的區(qū)域內(nèi),再引入壓縮感知理論實(shí)現(xiàn)目標(biāo)位置估計(jì)。此部分,首先根據(jù)鎖定區(qū)域范圍建立虛擬參考標(biāo)簽;然后由新型組合核函數(shù)相關(guān)向量機(jī)算法訓(xùn)練得到室內(nèi)傳播損耗模型,計(jì)算獲得虛擬標(biāo)簽處接收信號(hào)強(qiáng)度值,構(gòu)建測(cè)量矩陣;最后利用SROMP壓縮感知重構(gòu)算法求解出目標(biāo)的位置索引矩陣,對(duì)索引矩陣中的位置相關(guān)點(diǎn)加權(quán)平均得到目標(biāo)的位置信息。實(shí)驗(yàn)結(jié)果表明,所提定位算法平均定位誤差為0.6445 m,算法運(yùn)算效率相對(duì)較高,可以較好地滿足室內(nèi)定位的要求。
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關(guān)鍵詞:
- 室內(nèi)定位 /
- 壓縮感知 /
- 模擬退火 /
- 正則化正交匹配追蹤 /
- 相關(guān)向量機(jī)
Abstract: In consideration of the contradiction between the positioning accuracy and computational efficiency of the previous indoor positioning algorithm, a double stage positioning algorithm (LANDMARC- SROMP CS) using LANDMARC combined with Compressive Sensing based on the Regularized Orthogonal Matching Pursuit optimized by the Simulated annealing algorithm (SROMP) is put forward. First of all, LANDMARC location algorithm is used to lock the target area quickly; then in the locked area, Compressive Sensing (CS) theory is introduced to realize the target position estimation. In this part, firstly, the virtual reference tags are constructed according to the scale of the locked area; then, the measurement matrix is constructed by the received signal strength data of the virtual reference tags, and the signal strength data are calculated by the indoor propagation loss model which is trained by a new relevance vector machine algorithm based on mixed kernel functions. At last, the SROMP compressive sensing reconstruction algorithm is used to get the position index matrix, and the position information of the target also can be obtained through a simple weighted average calculation. The experimental results show that the average positioning error of the proposed algorithm is only 0.6445 m, and the computation efficiency of the proposed algorithm is relatively high, which can meet the indoor positioning requirements well. -
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