|
Localization method | Band | RMSE (mm) | No. of sensors | Dimension | In vitroor in vivo measurements |
|
Hybrid methods |
Hybrid localization using vision and RF [5] | MICS | 23 | 16 | 3 | Partially - image dataset from pillcam |
Fusion based hybrid localization using vision and IMU sensors [6] | MICS | 9.5 | NO | 3 | Yes - in vitro validation |
|
TOA and RSSI based |
TOA, path loss, and spatial sparsity-based convex optimization and -norm minimization [9] | MICS | 8.0 | 16 | 2 | No - implant to the body surface model using electromagnetic field simulation |
|
RSSI based |
RSSI based triangulation [24] | MICS | 34 | 64 | 3 | No - implant to the body surface model using electromagnetic field simulation |
Nonparametric path loss-based WCL [12] | MICS | 24.53 | 64 | 3 | No - implant to the body surface model using electromagnetic field simulation |
RSSI-based ML localization using the particle filter [11] | MICS | 7.0 | 8 | 3 | No - FDTD simulation in the small intestine |
RSSI-based compressive sensing and VNL Kalman filter [18] | UWB | 35 | 24 | 2 | No - field simulation based |
GWA filter and MIMO-based path loss estimation and position-bounded calibrated WCL [19] | UWB | 8.24 | 56 | 3 | No - FIT simulation of human torso with electrical field probes inside the chest |
7.49 |
ML estimated path loss-bounded WCL [20] | UWB | 8.14 | 56 | 3 | No - FIT simulation of human torso with electrical field probes inside the chest |
Proposed SPLD-WCL | UWB | 8.7 | 8 | 3 | Yes - in-body to on-body in vivo measurements |
6.83 | 48 |
|