【 SIMULATION 】RMSE Comparison of Linear Approaches for TOA

前面的博文对TOA定位的线性方法给予了仿真实验，这里将这些RMSE仿真结果放到一起，比较它们的定位性能。

Repeat the test of the linear approaches; that is, compare the MSPE performance of LLS, WLLS, two - step WLS, and subspace methods for SNR ∈ [ − 10, 60] dB.

Figure 1 shows the RMSEs of different linear schemes at SNR ∈ [ − 10, 60] dB. It is seen that the two - step WLS scheme, which exploits the constraint of Equation 2.76 , gives the highest localization performance, followed by WLLS, LLS, and subspace estimators.

注：the two - step WLS scheme和WLLS在博文：WLLS Algorithm of TOA - Based Positioning (include the two - step WLS estimator)

响应的仿真可见我的其他博文，这里就不贴出来了。

至于式2.76的限制为： $R = \sqrt{x^2+y^2}$

下面是这四种线性算法的定位均方根误差仿真图：

从图中可以看出，在信噪比为10之前，孰优孰劣，一看便知，至于信噪比为10dB之后，就不太能分清楚了。

下面分别给出这四条曲线，并给出信噪比为20以及30dB时候的RMSE曲线值，仅比较这两个点：

LLS：

WLLS：

two step WLLS:

subspace:

可见，当信噪比在20dB时候，two_step WLLS算法的定位精度最高，30dB的时候，也是two_step WLLS定位精度最高。

最后我们不得不说，总体来说，two_step WLLS算法的定位精度在这四种定位算法中是最优的。

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【 SIMULATION 】RMSE Comparison of Linear Approaches for TOA - Based Positioning

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