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matlab

于 2019-03-04 发布文件大小:3024KB

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下载积分: 1 下载次数: 4

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代码说明：

仿真信号进行卡尔曼滤波去除噪声并用KNN算法实现定位(Kalman filter is used to remove noise from simulated signals and KNN algorithm is used to locate them.)

文件列表：

IP_raytracing, 0 , 2018-11-28
IP_raytracing\generate_radio_map.m, 1004 , 2018-11-28
IP_raytracing\get_offline_data.m, 157 , 2017-05-26
IP_raytracing\get_offline_data_random.m, 518 , 2017-05-26
IP_raytracing\get_offline_data_uniform.m, 488 , 2017-05-26
IP_raytracing\get_online_data.m, 825 , 2017-05-26
IP_raytracing\get_random_trace.m, 980 , 2017-05-26
IP_raytracing\get_rss_by_ray_tracing.m, 6909 , 2018-11-27
IP_raytracing\main_raytracing.m, 1191 , 2017-05-26
IP_raytracing\readme.txt, 1263 , 2018-11-12
Localization_algorithms, 0 , 2018-11-12
Localization_algorithms\loc_knn_cls.m, 499 , 2017-05-26
Localization_algorithms\loc_knn_reg.m, 471 , 2017-05-26
accuracy.m, 178 , 2017-05-26
filters, 0 , 2018-11-12
filters\kf_init.m, 1337 , 2018-12-04
filters\kf_update.m, 442 , 2017-05-26
main_KF_test.m, 1309 , 2018-11-27
main_loc_test.m, 344 , 2017-05-26
offline_data_random.mat, 1440906 , 2018-11-27
offline_data_uniform.mat, 1187235 , 2018-11-27
online_data.mat, 478730 , 2018-11-27
online_loc.m, 1013 , 2017-05-26

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NLMS
若不希望用与估计输入信号矢量有关的相关矩阵来加快LMS算法的收敛速度，那么可用变步长方法来缩短其自适应收敛过程，其中一个主要的方法是归一化LMS算法（NLMS算法），变步长的更新公式可写成 W（n+1）=w（n）+ e（n）x（n） =w（n）+ （3.1）式中， = e（n）x（n）表示滤波权矢量迭代更新的调整量。为了达到快速收敛的目的，必须合适的选择变步长的值，一个可能策略是尽可能多地减少瞬时平方误差，即用瞬时平方误差作为均方误差的MSE简单估计，这也是LMS算法的基本思想。 (Want to estimate if the input signal vector and the relevant matrix to speed up the convergence rate of LMS algorithm, then the variable step size method can be used to shorten its adaptive convergence process, one of the main method is normalized LMS algorithm (NLMS algorithm) , variable step-size update formula can be written W (n+ 1) = w (n)+ e (n) x (n) = w (n)+ (3.1) where, = e (n) x (n) the right to express filter update vector iterative adjust the volume. In order to achieve the purpose of fast convergence, we must choose the appropriate value of variable step size, a possible strategy is as much as possible to reduce the instantaneous squared error, which uses the instantaneous squared error as the mean square error MSE of the simple estimate, which is the basic LMS algorithm思想.)

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