CAJ | 학술논문

针对室内多目标基于无线信号强度定位中的数据采集和精确度问题，引入基于贝叶斯压缩感知和拉普拉斯先验模型算法，从而满足在达到所需定位精确度的同时降低网络系统开销。所提出的方法是基于接收信号强度来感知位置变化，各移动设备上利用随机投影对接收到的信号强度进行压缩并传输，在采集中心通过基于拉普拉斯先验的贝叶斯压缩感知重构算法并结合最大似然函数法和迭代逼近法计算出各移动设备的位置。仿真结果表明了利用贝叶斯压缩感知重构算法实现室内多个移动设备的定位具有较高精确度，与orthogonal matching pursuit （ OMP ）重构算法相比较其定位精度至少提高了52．2％，与basis pursuit（BP）重构算法相比较至少提高了13．7％。
침대실내다목표기우무선신호강도정위중적수거채집화정학도문제，인입기우패협사압축감지화랍보랍사선험모형산법，종이만족재체도소수정위정학도적동시강저망락계통개소。소제출적방법시기우접수신호강도래감지위치변화，각이동설비상이용수궤투영대접수도적신호강도진행압축병전수，재채집중심통과기우랍보랍사선험적패협사압축감지중구산법병결합최대사연함수법화질대핍근법계산출각이동설비적위치。방진결과표명료이용패협사압축감지중구산법실현실내다개이동설비적정위구유교고정학도，여orthogonal matching pursuit （ OMP ）중구산법상비교기정위정도지소제고료52．2％，여basis pursuit（BP）중구산법상비교지소제고료13．7％。
In order to reduce the overhead of the network system while maintaining the sufficient accuracy of indoor localization, Bayesian compressed sensing and Laplace prior model were explored to solve indoor localization and data compressing of multiple wireless devices. The proposed indoor positioning system was based on received signal strength ( RSS) measurement. It was followed by compressing the RSS with random projection on the multiple wire?less devices and making accumulation after transmitting them to the center server. The locations of these targets were determined by collecting RSS based on the algorithm of Bayesian compressive sensing using Laplace priors, by com?bining the maximum likelihood procedure and iterative approximation algorithm. Simulation results showed that the multiple targets localization using Bayesian compressive sensing had at least 52.2% more accuracy compared to the orthogonal matching pursuit (OMP) algorithm and had at least 13.7% more accuracy compared to the basis pursuit ( BP ) algorithm.