CAJ | 학술논문

针对A*正交匹配追踪(A*OMP)算法计算复杂高,且不能利用信号的结构稀疏性这一缺陷,该文提出了块A*OMP算法并将其用于解决分布式压缩感知中的信号联合重构问题.该算法用原子块取代单个原子作为搜索树中的节点,在计算路径代价时用搜索树中所有路径的最大长度取代信号的稀疏度.然后在块A*OMP算法的基础上,选择与残差矩阵投影误差最小的原子块作为新的节点,得到了一种用于解决 MMV(Multiple Measurement Vector, MMV)问题的块A*OMP算法,并利用该算法对相邻区域内的多个传感器所测的温度信号进行了联合重构.实验结果表明,该算法的重构性能优于MMV正交匹配追踪(OMPMMV)算法.
침대A*정교필배추종(A*OMP)산법계산복잡고,차불능이용신호적결구희소성저일결함,해문제출료괴A*OMP산법병장기용우해결분포식압축감지중적신호연합중구문제.해산법용원자괴취대단개원자작위수색수중적절점,재계산로경대개시용수색수중소유로경적최대장도취대신호적희소도.연후재괴A*OMP산법적기출상,선택여잔차구진투영오차최소적원자괴작위신적절점,득도료일충용우해결 MMV(Multiple Measurement Vector, MMV)문제적괴A*OMP산법,병이용해산법대상린구역내적다개전감기소측적온도신호진행료연합중구.실험결과표명,해산법적중구성능우우MMV정교필배추종(OMPMMV)산법.
Considering the disadvantage of the high complexity and ignoring signal’s structural sparsity in A*Orthogonal Matching Pursuit (A*OMP) algorithm, a block A*OMP algorithm is proposed for block-sparse signals, and it is improved to solve the joint reconstruction problem for multiple signals in distributed compressed sensing. In the proposed algorithm, the single atom is replaced by a block that is composed of several atoms, and the sparsity is replaced by the maximum length of all the paths on the search tree when calculating the path cost. Then, on the basis of block A*OMP algorithm, a block A*OMP algorithm for Multiple Measurement Vector (MMV) problem is presented by projecting all blocks onto the residual matrix and selecting the block with the smallest projection error as a new node. With this algorithm, the temperature signals which are measured by sensors in the adjacent region are jointly reconstructed perfectly. Experiments show that the reconstruction performance of this algorithm outperform Orthogonal Matching Pursuit for MMV (OMPMMV) algorithm.