Changes between Version 25 and Version 26 of expansion/gw16143

Timestamp:

08/28/2024 11:53:38 PM (3 weeks ago)

Author:

Blake Stewart

Comment:

replaced the id for the 16132

expansion/gw16143

@@ -120 +120 @@
 Networked Transport of RTCM via Internet Protocol, or [https://en.wikipedia.org/wiki/Networked_Transport_of_RTCM_via_Internet_Protocol NTRIP], is the protocol that allows us to transmit RTK data between the differential gps basestation which has a fixed known position to any number of GPS clients that can use the RTK correction data. The NTRIP standard is designed and published by [https://www.rtcm.org/ RTCM]. You can read more about the NTRIP protocol [https://igs.bkg.bund.de/root_ftp/NTRIP/documentation/NtripDocumentation.pdf here].
 
-Acquiring RTK correction data for high precision GPS with the GW16143 can be done either by pointing to a preexisting source such as u-blox PointPerfect, the ones posted to [http://www.rtk2go.com/ rtk2go], or setting up a source, server, and caster yourself. Keep in mind that RTK correction data effectiveness is diminished over larger distances. For more specifics, see this great [https://www.researchgate.net/publication/241755023_Exploring_GNSS_RTK_performance_benefits_with_GPS_and_virtual_galileo_measurements publication] of RTK experimental findings by Yanming Feng and Jinling Wang.
+Acquiring RTK correction data for high precision GPS with the GW16143 can be done either by pointing to a preexisting source such as u-blox !PointPerfect, the ones posted to [http://www.rtk2go.com/ rtk2go], or setting up a source, server, and caster yourself. Keep in mind that RTK correction data effectiveness is diminished over larger distances. For more specifics, see this great [https://www.researchgate.net/publication/241755023_Exploring_GNSS_RTK_performance_benefits_with_GPS_and_virtual_galileo_measurements publication] of RTK experimental findings by Yanming Feng and Jinling Wang.
 
 There are a number of open source NTRIP utilities to use for setting up a differential GPS system. Some options and the required configuration steps of the GW16143 are described in the following sections. 
@@ -244 +244 @@
 > Contact Gateworks Sales Department {{{sales@gateworks.com}}} to get a free 1-month access code! 
 
-!PointPerfect can get extremely accurate positioning data extremely quickly with relatively low bandwidth (kilobytes / minute). Combined with the [https://trac.gateworks.com/wiki/expansion/gw16126 GW16126] for LTE/Bluetooth, differential fixes can be easily received in hard-to-reach locations. 
-
-The easiest way to use !PointPerfect with the ZED-F9P is through [https://github.com/semuconsulting/pygnssutils pygnssutils]. Our examples also leverage the GW16126 for cellular connectivity and were able to get very precise data in a moving vehicle, even with relatively low reception, though !PointPerfect has various delivery methods (NTRIP, MQTT, L-Band). For this example, the script works without too much configuration since Linux configures cellular modems to make connections work seamlessly.
+!PointPerfect can get extremely accurate positioning data extremely quickly with relatively low bandwidth (kilobytes / minute). Combined with the [https://www.gateworks.com/products/mini-pcie-expansion-cards/gw16132-mini-pcie-expansion-card/ GW16132] for LTE/Bluetooth, differential fixes can be easily received in hard-to-reach locations. 
+
+The easiest way to use !PointPerfect with the ZED-F9P is through [https://github.com/semuconsulting/pygnssutils pygnssutils]. Our examples also leverage the GW16132 for cellular connectivity and were able to get very precise data in a moving vehicle, even with relatively low reception, though !PointPerfect has various delivery methods (NTRIP, MQTT, L-Band). For this example, the script works without too much configuration since Linux configures cellular modems to make connections work seamlessly.
 
 The bare-minimum python program for a NTRIP client with pygnssutils can be found on their [https://github.com/semuconsulting/pygnssutils/blob/main/examples/rtk_example.py git repo]. The data collected was output to a file containing location data over time, and then converted to KML format to be imported into Google Earth.