To mitigate the impacts of non-line-of-sight（NLOS） errors on location accuracy, a non-parametric belief propagation（NBP）-based localization algorithm in the NLOS environment for wireless sensor networks is *** to ...
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To mitigate the impacts of non-line-of-sight（NLOS） errors on location accuracy, a non-parametric belief propagation（NBP）-based localization algorithm in the NLOS environment for wireless sensor networks is *** to the amount of prior information known about the probabilities and distribution parameters of the NLOS error distribution, three different cases of the maximum a posterior（MAP） localization problems are introduced. The first case is the idealized case, i. e., the range measurements in the NLOS conditions and the corresponding distribution parameters of the NLOS errors are known. The probability of a communication of a pair of nodes in the NLOS conditions and the corresponding distribution parameters of the NLOS errors are known in the second case. The third case is the worst case, in which only knowledge about noise measurement power is obtained. The proposed algorithm is compared with the maximum likelihood-simulated annealing（ML-SA）-based localization algorithm. Simulation results demonstrate that the proposed algorithm provides good location accuracy and considerably outperforms the ML-SA-based localization algorithm for every case. The root mean square error（RMSE）of the location estimate of the NBP-based localization algorithm is reduced by about 1. 6 m in Case 1, 1. 8 m in Case 2 and 2. 3 m in Case 3 compared with the ML-SA-based localization algorithm. Therefore, in the NLOS environments,the localization algorithms can obtain the location estimates with high accuracy by using the NBP method.
为了提高大规模MIMO系统的分集增益、降低译码复杂度,构建了一种码率为1的满分集贝尔实验室垂直分层空时码,并采用最大比合并算法(MRC)检测接收信号.分别计算了MRC算法的平均输出信干噪比(SINR)和传统迫零算法(ZF)的平均信噪比(SNR),分析了性能相等时应满足的条件,并且比较了2种算法的计算复杂度和BER性能.结果表明,当BER=10-5,收发天线数为400和40、调制方式分别为BPSK和QPSK时,最大比合并算法的BER性能较迫零算法分别存在0.4和0.3 d B的增益.采用所提算法对接收信号进行检测,不但能够降低系统的计算复杂度,而且能保证系统的误比特率性能.
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