Numerical Optimization Method of Navigation Aid Telemetry System Based on GPS

　　With the development of electronic technology and communication technology, wireless communication and telemetry and remote control systems are widely used in various fields such as industry, agriculture, aviation, and navigation. As an important part of maritime shipping, the management and maintenance methods and management quality of estuaries and inland waterways are crucial to my country's shipping industry. To date, the reliability of electronic communication products has become higher and higher, and the cost has become lower and lower, which makes it possible to realize the automation and digitization of waterway management and maintenance. GPS (Global Positioning System) is a satellite global navigation and positioning system developed by the US Department of Defense in 1973, mainly serving its navy, army and air force. In recent years, it has been gradually applied to civilian facilities and surveying and mapping technology. At the same time, the US military has gradually relaxed restrictions on civilian GPS equipment, so that civilian GPS has achieved a relatively high positioning accuracy. Using GPS to perform position telemetry and monitoring of waterway beacons and other equipment is an ideal method. Based on the specific requirements of beacon monitoring, this paper forms a GPS telemetry network for the targets to be monitored in the entire waterway management area, and uses various filtering methods to eliminate the corresponding errors, thereby improving the accuracy of telemetry data.

　　

　　1 GPS OEM board and channel GPS telemetry network

　　1.1 GPS OEM Board

　　GPS OEM board is an important component of GPS receiver. It has many advantages such as low cost, small size, light weight, wide product variety and high cost performance. Therefore, it is widely used in the field of positioning and navigation. Its positioning accuracy can reach tens of meters, and even reach an accuracy of less than 10 meters. The GPS OEM B12 of Thales Group Navigation and Positioning Company used in this project is a very cost-effective product.

　　1.2 Waterway monitoring

　　The waterway is an important part of the transportation network, and its safety and quality directly affect the entire transportation system. In the past, the waterway department specially installed various navigation lights on the embankments, bridgeheads, and fault objects of the waterway as warning navigation devices. Various ships can determine their own course according to the navigation lights and their flashing frequency. As for the maintenance of navigation lights, the waterway department sends cruise ships to visually and measure each navigation light at regular intervals. Because there are many navigation lights in the waterway, this makes the maintenance method of this cruise waterway cumbersome, with high operating and maintenance costs and low safety quality.

　　1.3 Channel GPS telemetry network

　　The navigation mark telemetry network in the waterway is mainly used to telemeter water marks (navigation marks anchored in the water) in order to monitor their positions in real time (the system schematic diagram is shown in Figure 1);

　　The shore beacon (the beacon fixed on the embankment) does not need GPS telemetry because its position remains unchanged. The actual task of GPS in the beacon telemetry network is to measure the location of the beacon light in real time and compare it with the pre-defined position range. If it drifts out of the calibrated range, an alarm message is sent to the monitoring center through the GSM network so that the monitoring center can take corresponding measures. This will eliminate the possibility of accidents caused by the beacon light drifting away due to ship collision, water flow impact, etc. There are hundreds of water beacons in each waterway management area, so while improving safety and quality, cost investment must also be considered. According to the specific requirements of the waterway, its accuracy does not need to be accurate to less than the meter level, so there is no need for expensive high-precision GPS receivers and measuring instruments. At the same time, bundling the GPS OEM board with the water beacon can achieve high-quality management effects at a relatively low cost. This system uses the B12 GPS OEM board module produced by Thales, France, which has 12 parallel receiving channels (that is, it can simultaneously receive ephemeris information transmitted by 12 positioning satellites).
 

　　2 Error analysis, numerical processing and control process

　　2.1 Error Analysis

　　The errors in GPS measurements mainly include errors caused by satellites, signal propagation, signal reception, etc., but in terms of nature, they can be summarized into two parts: systematic errors and random errors. The systematic errors mainly include satellite ephemeris errors, satellite clock errors, receiver clock errors, and atmospheric refraction errors. Random errors mainly include signal multipath effects, etc. Although the systematic errors are larger than random errors, their elimination mainly depends on the receiver itself [1], but they always follow certain rules, so taking certain measures to deal with them is very important for the reliability of the entire system. Since the multipath effect on the water surface is relatively serious, using a measurement antenna with a precise phase center and an Ephemeris coil is an important method to eliminate the errors caused by the water surface environment.

　　2.2 Numerical Processing

　　In response to various errors, various filtering methods have been applied in measurement technology to eliminate or reduce the impact of various errors, such as median filtering, arithmetic mean filtering, advance and retreat recursive filtering, etc. Through a large number of measurement tests and observation analysis, it is found that with the change of time, the change of satellite distribution status and the change of weather, the data read by GPS has drift in different curve directions, but its distribution state is close to normal distribution, so the use of some filtering methods to process the data is crucial to improve the accuracy of the entire measurement system. The following are several filtering methods used in the system.

　　

　　Median filtering method: that is, sort the three measured data, remove the largest and smallest one, and take the middle value as the measured value. Based on this idea, this paper continuously measures n (adjustable) times of longitude and latitude data when the terminal controller is powered on and initialized, and queues them from small to large, removes the largest m times of data and the smallest m times of data, and uses the middle n-2m times of data as a benchmark, and stores them in a storage unit. Since the channel telemetry system does not have high requirements for real-time performance, n is made as large as possible. Suppose the sum of the n-time read data is Xn, the sum of the smallest m-time data after sorting is XmMIN, and the sum of the largest m-time data is XmMAX, then:

　　Xsum=Xn-XmMIN-XmMAX

　　Store Xsum in the storage unit as the reference for subsequent processing methods. Arithmetic mean filtering method: that is, sampling a certain amount of data, and then calculating the average value as the measurement estimate, so that the positive and negative errors that deviate from the true value can be cancelled, so that the measurement value is closer to the true value. This topic calculates the arithmetic average of the n-2m measurement data obtained previously, and stores it in a fixed arithmetic mean storage unit, and makes real-time corrections based on the data read later. In this way, there are:

　　X=(Xswn)/(n-2m); Xi=(Xsumi)/(n-2m).

　　Among them, X is the average value calculated during initialization and is stored in the storage unit as an average reference. Xi is the average value calculated each time the data is read and is used as a position evaluation value in position drift judgment control. [page]

　　

　　Advance and retreat recursive filtering method: The previous two methods read certain data and then perform post-processing, while the measured data must be processed in real time during the measurement process. Therefore, the changing trend of the measured longitude and latitude must be reflected so that when the navigation mark drifts out of the given range due to an accident, an alarm message can be sent to the monitoring center in real time for correction. Based on the results of experiments and observations, this paper adopts an advance and retreat recursive filtering method of advance-new number and retreat-average number, that is:

　　Xswni=Xsum_i-1+Xi-1+xi

　　Limiting filter method: In the measurement process, we often encounter gross errors that deviate far from the median. This will have a great impact on the data benchmark processed by the previous filtering methods. The limiting filter method is aimed at this idea. Set a threshold value. When the measured data is compared with the benchmark data, if the difference exceeds the threshold, it is considered a gross error and discarded. However, in this project, if the beacon light drifts far away due to an accident, it must be identified, and it cannot be discarded as a gross error. Therefore, a counter is specially designed in the control program to count the discard ratio. If the discard ratio is greater than a certain value, it is reinitialized, that is, the sum benchmark and its arithmetic average benchmark are re-read for n-2m times.

　　Figures 2, 3, and 4 are coordinate diagrams of 10 hours of GPS data collected by a data collection and processing program developed using Visual Basic 6.0 after several data processing (where the horizontal and vertical coordinates represent longitude and latitude, respectively). It can be seen from these three figures that from Figure 2 to Figure 4, the data convergence is enhanced successively, which shows that the integration of several filtering methods in data processing will greatly reduce errors and improve system accuracy.