Wnlab

In the following table, the VRTI dist. Error is the locallization error misured by the only Maximum Variance Image estimation,instead, Kalman dist. Error is the locallization error misured supporting the VRTI algorithm by the Kalman filter. Noise and No-Noise are the localization errors measured changing the parameters to calculate the weights of the regularization matrix.

Scenario	Parameters	Anchor positions	VRTI error dist. Noise/No-Noise .	KALMAN error dist. Noise/No-Noise	Metrics VRTI / KALMAN	Notes and video
C 40	area: 6.4 X 4.5 [m] anchors: 25 speed: 0.5[step/s] channels: 1 origin: WnLab				RMSE: 1.60 [m] / 1.59 [m] 75th pc: 1.95 [m] / 1.93 [m] 90th pc: 2.67 [m] / 2.62 [m]	Error with noise model in [0.5, 1.75] m. Error without Noise model in [0.4, 2] m. No improvement from tracking. video
					RMSE: 2.55 [m] / 2.55 [m] 75th pc: 3.28 [m] / 3.27 [m] 90th pc: 3.78 [m] / 3.77 [m]

In the following table, the VRTI dist. Error is the locallization error misured by the only Maximum Variance Image estimation,instead, Kalman dist. Error is the locallization error misured supporting the VRTI algorithm by the Kalman filter. Noise and No-Noise are the localization errors measured changing the parameters to calculate the weights of the regularization matrix.

Scenario	Parameters	Anchor positions	VRTI error dist. Noise/No-Noise .	KALMAN error dist. Noise/No-Noise	Metrics VRTI / KALMAN	Notes and video
Pat_1	area: 8.4 X 6.5 [m] anchors: 33 speed: 1[step/s] channels: 5 origin: SPAN Lab.				RMSE: 0.62 [m] / 0.62 [m] 75th pc: 0.68 [m] / 0.67 [m] 90th pc: 1.04 [m] / 1.02 [m]	Error with noise model in [0.2, 0.6] m. Error without noise model in [1.1, 1.5] m. No improvement from tracking. video
					RMSE: 1.64 [m] / 1.62 [m] 75th pc: 1.81 [m] / 1.80 [m] 90th pc: 2.23 [m] / 2.23 [m]

In the following table, the VRTI dist. Error is the locallization error misured by the only Maximum Variance Image estimation,instead, Kalman dist. Error is the locallization error misured supporting the VRTI algorithm by the Kalman filter. Noise and No-Noise are the localization errors measured changing the parameters to calculate the weights of the regularization matrix.

Scenario	Parameters	Anchor positions	Raw error dist.	Tracking error dist.	Metrics raw / trk	Notes and video
Pat_2	area: 6.5×4.5[m] anchors: 28 speed: 1[step/s] channels: 1 origin: SPAN Lab.				RMSE: 1.26 [m]/ 1.26[m] 75th pc: 1.55 [m]/ 1.55 [m] 90th pc: 2.0 [m] / 1.97 [m]	Error with noise model in [0.35, 1.7]m. Error without Noise model in [0.7, 1.7] m. No improvement from tracking. video
					RMSE: 1.67 [m] / 1.66 [m] 75th pc: 1.85 [m] / 1.83 [m] 90th pc: 2.53[m] / 2.53 [m]

In the following table, the VRTI dist. Error is the locallization error misured by the only Maximum Variance Image estimation,instead, Kalman dist. Error is the locallization error misured supporting the VRTI algorithm by the Kalman filter. Noise and No-Noise are the localization errors measured changing the parameters to calculate the weights of the regularization matrix.

Scenario	Parameters	Anchor positions	Raw error dist.	Tracking error dist.	Metrics raw / trk	Notes and video
Runner	area: 6.5×4.5[m] anchors: 28 speed: 0.5[step/s] channels: 1 origin: SPAN Lab.				RMSE: 3.056 [m]/ 3.051 [m] 75th pc: 4.24 [m]/ 4.23 [m] 90th pc: 5.60 [m] / 5.59 [m]	Error with noise model in [0.4, 1.8]m. Error without Noise model in [1.2, 2] m. No improvement from tracking. video
					RMSE: 3.52 [m] / 3.51 [m] 75th pc: 5.07 [m] / 5.09 [m] 90th pc: 5.78[m] / 5.78 [m]

In the following tables, the VRTI dist. Error is the locallization error misured by the only Maximum Variance Image estimation. The metrics parameters are evaluated in both two cases in presence and not of an AP WiFi. Precisely, in the column “Metrics” the first value refears to the measure with AP and the second one without AP.The same formatting is used for the column of videos. Note that in our measure the channels 17 and 24 were the “high quality channels”, instead the channels 14 and 21 were “low quality channels”.

The following three figures show the RMSE, the 75-th and 90-th Percentile's resilience toward the noise that affects the cannel, respectivelly. Moreover, the figures show as the multi-channels improve the algorithm's performance rather than in the case of single-channel.

In brief, the three figures show the comparison about the localization performance in term of number of channels used to acquire the data, link quality and scenario taken into account.

RMSE	75° Percentile	90° Percentile