Ten years ago low-cost but high-precision positioning options did not exist. But this has changed and the trend will continue. Outlined in this article are 3 options for satisfying positioning requirements using GNSS-based sensors.
In this post, we will discuss the high-level steps involved in both developing and utilizing a machine learning application.
One of the luxuries of using an open source programming language like Python [https://www.python.org] is that you have a lot of options for running your code. Compare this with proprietary software: * xlsx spreadsheets: open with Microsoft Excel
Accelerometer: Zero-g offset Temperature Coefficient Other Names bias thermal drift, offset temperature slope, offset or bias thermal response Examples DeviceNameValueHoneywell HG1120BA50 [https://onav.link/honeywell-hg1120]Bias Repeatability is given over both time and thermal conditionsNACTi Sensors CS-IM100 [https://onav.link/
Other Names zero-g or 0g offset, bias, bias repeatability, turn-on to turn-on bias, zero-g output Examples DeviceNameValueHoneywell HG1120BA50 [https://onav.link/honeywell-hg1120]Bias Repeatability, at any given time or thermal condition16mg, 1$\sigma$CTi Sensors CS-IM100 [https://onav.link/cti-cs-im100]