Home Articles GPS-Aided-INS for Mobile Mapping in Precision Agriculture

GPS-Aided-INS for Mobile Mapping in Precision Agriculture

Khurram Niaz Shaikh, Abdul Rashid bin Mohammad Shariff, Hishamuddin Jamaluddin, Shattri Mansoor
Dept. of Biological and Agricultural Engineering
Faculty of Engineering, University Putra Malaysia
E-mail: [email protected]

Global Positioning System (GPS) is a network of satellites that continuously transmit coded information, which makes it possible to precisely identify locations on earth by measuring distance from the satellites. GPS offers quick and accurate method of gathering mapping information to meet the geographic data needs but a significant problem can be caused in the data where satellite view is obscured by foliage, buildings or other features, since GPS require line of sight to at least four satellites to achieve full precision. These are costly problems and usually require new surveys to remedy. To address the problem the integrated GPS/INS approach is utilized, which can give accurate result even in the event of temporary GPS signal loss. With GPS and INS hardware becoming ever smaller and less expensive, innovative opportunities for commercial, military, and scientific navigation systems are everywhere-and continue to arise.

The research offers a quick, accurate, reliable, complete and efficient mobile mapping system for site specific farming to help farmers increase the productivity (yield) of the field, through the integration of sophisticated hardware and software known as GAINS (GPS-Aided-INS).

The objective of the research is to build an integrated system using IMU (Inertial Measurement Unit) and GPS Receiver, and to design an algorithm (Kalman Filter) to reduce the error, filtering the output of the integration between GPS and INS.

Inertial Navigation
Navigation is the art of knowing where you are, how fast you are moving and in which direction; and of positioning yourself in relation to your environment in such a way as to maximize your chances for survival. Inertial navigation is accomplished by an Inertial Measurement Unit (IMU) that integrates the output of a set of sensors to compute position, velocity, and attitude. The sensors used are gyros and accelerometers. Gyros measure angular rate with respect to inertial space, and accelerometers measure linear acceleration, again with respect to an inertial frame. Integration is a simple process, complexities arise due to the various coordinate frames encountered, sensor errors, and noise in the system. (Stovall, 1997)

Inertial Navigation is a dead reckoning technique, so it suffers from one serious limitation: drift rate errors constantly accumulate with the passage of time. Because its drift errors relentlessly accumulate, an inertial navigation system that operates for an appreciable length of time must be updated periodically with fresh positioning information. This can be accomplished by using an external navigation reference, such as GPS.

Kalman Filter
Kalman Filter is a recursive algorithm designed to compute corrections to a system based on external measurements. The corrections are weighted according to the filter’s current estimate of the system error statistics. The derivations of the filter equations require some knowledge of linear algebra and stochastic processes. The filter equations can be cumbersome from an algebraic point of view. Fortunately, the operation of the filter can be understood in fairly simple terms. All that is required is an understanding of various common statistical measures.

Kalman filtering is an extremely effective and versatile procedure for combining noisy sensor outputs to estimate the state of a system with uncertain dynamics. Kalman Filter exploits a powerful synergism between the Global Positioning System (GPS) and Inertial Navigation System (INS). This synergism is possible, in part, because the INS and GPS have very complementary error characteristics. Short-term position errors from the INS are relatively small, but they degrade without bound over time. GPS position errors, on the other hand, are not as good over the short term, but they do not degrade with time. The Kalman filter is able to take advantage of these characteristics to provide a common, integrated navigation implementation with performance superior to that of either subsystem (GPS or INS). By using statistical information about the errors in both systems, it is able to combine a system with tens of meters position uncertainty (GPS) with another system whose position uncertainty degrades at kilometers per hour (INS) and achieve bounded position uncertainties in the order of centimeters [with differential GPS (DGPS)] to meters. (Grewal et al, 2001)

A key function performed by the Kalman filter is the statistical combination of GPS and INS information to track drifting parameters of the sensors in the INS. As a result, the INS can provide enhanced inertial navigation accuracy during periods when GPS signals may be lost, and the improved position and velocity estimates from the INS can then be used to make GPS signal reacquisition happen much faster when the GPS signal becomes available again.

Advantages of GPS/INS integration
GPS Aided INS systems have some real advantages in terms of output rate, reliability, and accuracy. (Farell & Barth, 1999) .

  • It is autonomous and does not rely on any other external aids or on visibility conditions and maintains the availability of navigation solution during GPS outages due to interference, jamming, etc.
  • Optimal mixing of the INS and the GPS information reduces the effect of GPS errors. Therefore GPS-only accuracy is improved on by the integrated solution.
  • The INS provides the full navigation (6 degrees of freedom) state without differentiation. The 6 degrees of freedom refer to 3 translational and 3 rotational degrees of freedom. GPS signals could be used to determine accelerations by differentiation or attitude by techniques.
  • The INS provides the navigation solution in real time (i.e. without latency) at rates higher than may be achievable from a GPS receiver.

Figure 1 GPS may be blocked by Trees, buildings, and mountains but INS keeps on working.

Precision Agriculture
Precision Agriculture refers to the use of an information and technology-based system for within-field management of crops. “It basically means adding the right amount of treatment at the right time and the right location within a field—that’s the precision part,” Farmers want to know the right amounts of water, chemicals, pesticides, and herbicides they should use as well as precisely where and when to apply them. (Herring, 2001)

By using the tools of precision Agriculture, growers can specifically target areas of need within their fields and apply just the right amounts of chemicals where and when they are needed, saving both time and money and minimizing their impact on the environment. Irrigation is both difficult and expensive and gets even more difficult when the topography of the terrain is graded. Farmers have a tendency to over irrigate, spending both more time and money than is necessary. Often times farmers look at weather variables and then schedule irrigation based on that information. But if they had better information, they could use scientific models and equations to compute more precisely, how much water their crop is using or how much more is needed. And all this require to have an accurate 3D map of the field. Much of the ability to implement precision agriculture is based on information technologies; in particular, global positioning and navigation and geospatial mapping and analysis. From beginning to end, the cornerstone for precision agriculture is based on precise locations and time. Fortunately the Global Positioning System (GPS) provides positioning, velocity, and timing capability, and Geographic Information Systems (GIS) provide mapping and analysis capability.

Mobile Mapping is essentially useless without the GPS component. The GPS component not only provides the location for all data collected but also provides the time in which it was collected. GPS also enables the user to navigate back to any particular location anytime thereafter. Once the field data has been collected using mobile mapping, the data can be downloaded into a desktop GIS. The GIS then provides the producer the ability to consider all the options for production. The producer can then use the positional data and the decisions that were made with the GIS to carry out the mechanized part of precision agriculture. (Rasher) GAINS provides a mapping solution that is especially useful for operation in plantations, orchards and forests, where GPS signal is degraded by the canopy. Farmers can use GAINS to determine their fertilizer and pesticide needs, improving crop yield, and preventing hazardous ground-water runoff.

The PA includes the following generic applications: (Rasher, )
Soil sampling: – the ability to determine the physical characteristics and the variability of the soil in the field.

Variable Rate Application: – the ability to precisely apply the required type and quantity of nutrient of chemical needed to specify areas of the field.

Yield Monitoring: – the ability to accurately measure the yield and simultaneously record the location in the field.

Following are additional specific applications practiced in PA.

  • Soil Mapping
  • Weed & Pest Mapping
  • Disease Mapping
  • Crop Growth
  • Rainfall & other conditions
  • Plant Nutrients
  • Harvest Yield Monitoring, etc

Research Methodology
The study area of the research is Durian orchard in the Agriculture Park of University Putra Malaysia. The research is in its initial phase, where an accurate 3D field map has to be created to compare it later with GAINS map, to justify the accurate integrated solution. The creation of uneven terrain of durian orchard is itself a challenge. At first GPS survey is conducted for the based line of the traverse. Two Leica GPS system 500 receivers were used with differential post processed solution. With these two control points, total station traverse is carried out to determine the planimetric coordinate and height is determined using Leveling. After collecting sufficient number of points for interpolation, a 3D surface is generated using a software Civil Survey Design (CSD). The next phase is to check the errors accumulated by the IMU only over the surface. Since the IMU errors accumulate with time, an analysis can be made for the relationship between distance and error with time. The most important and critical phase is to integrate both hardwares, GPS receiver and IMU (Fig 3 is Block Diagram of GPS/INS Integration). The Kalman Filter would be utilized for determining better accuracy.

Research Methodology Flowchart

Figure 2

Block Diagram of GPS/INS Integration

Figure 3

Expected Results
The integrated system (GAINS) would thereafter be driven through the same field, which was mapped earlier through GPS and Total Station survey, to compare and justify the GAINS. (Fig 2 is the methodology Flowchart of the research). We expect there to be an accuracy improvement with the utilization of GAINS compared to traditional methods.


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