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Ranging errors are inevitable in all local positioning systems, including those based on Time-of-Flight (ToF) technique. Results of experiments show that the major cause for these errors is a signal degradation from multipath propagation. This effect is especially critical in case of Non-Light-of-Sight (NLOS) conditions. This paper describes causes that affects ranging errors for nanoLOC™-TOF-technology and presents estimations for the probability density functions of such errors under different NLOS conditions. The provided estimations allow the improvement of the accuracy of the localization through the subsequent mitigation of the ranging errors from the measurements. Additionally, it is proposed to increase the number of cases of NLOS-conditions for the improvement of the accuracy.
The low cost and small size of MEMS inertial sensors allows their combination into a multi sensor module in order to improve performance. However the different linear accelerations measured on different places on a rotating rigid body have to be considered for the proper fusion of the measurements. The errors in measurement of MEMS inertial sensors include deterministic imperfection, but also random noise. The gain in accuracy of using multiple sensors depends strongly on the correlation between these errors from the different sensors. Although for sensor fusion it usually assumed that the measurement errors of different sensors are uncorrelated, estimation theory shows that for the combination of the same type of sensors actually a negative correlation will be more beneficial. Therefore we describe some important and often neglected considerations for the combination of several sensors and also present some preliminary results with regard to the correlation of measurements from a simple multi sensor setup.
The paper describes the methodology and experimental results for revealing similarities in thermal dependencies of biases of accelerometers and gyroscopes from 250 inertial MEMS chips (MPU-9250). Temperature profiles were measured on an experimental setup with a Peltier element for temperature control. Classification of temperature curves was carried out with machine learning approach.
A perfect sensor should not have thermal dependency at all. Thus, only sensors inside the clusters with smaller dependency (smaller total temperature slopes) might be pre-selected for production of high accuracy inertial navigation modules. It was found that no unified thermal profile (“family” curve) exists for all sensors in a production batch. However, obviously, sensors might be grouped according to their parameters. Therefore, the temperature compensation profiles might be regressed for each group. 12 slope coefficients on 5 degrees temperature intervals from 0°C to +60°C were used as the features for the k-means++ clustering algorithm.
The minimum number of clusters for all sensors to be well separated from each other by bias thermal profiles in our case is 6. It was found by applying the elbow method. For each cluster a regression curve can be obtained.