Película pictórica

As it was described in section 3, the EIRE global navigator developed in this thesis is used during the take-off and global waypoint navigation phases of the RUAV. The purpose of this system is to combine the data of the GNSS, INS and additional sensors in an optimal way to obtain a navigation solution with both higher update rate and smaller position error than the standalone sensors. Figure 4-1 presents the advantages of using a combined strategy for obtaining the vehicle’s position.

Figure 4-1: Position errors of GPS, INS and combined INS/GPS (Bijker's, 2006).

In most cases, the fusion of GNSS/INS uses to be performed by an Extended Kalman filter. This algorithm is often implemented in a complementary form where the estimates states usually correct the position, velocity, attitude and the IMU sensors errors(Ding, 2008). The filtering outputs use to be fed forward to the INS navigation outputs to generate the navigation solution and they are also fed back to the INS sensors in order to calibrate their errors. The complementary integration structure is commonly used because it provides a convenient reference trajectory that makes easier the linearization of non-linear dynamics. Moreover, it estimates only the error components of the integrated system and in that way, reduces the

tracking dynamics. This is possible because the error components use to change much slower than the direct positioning dynamics(Brown and Hwang, 1997). In literature, it is possible to find multiple integration architectures that provide combined navigation solutions (Falco, Pini and Marucco, 2017). The differences between the different implementations use to be based on the type of information that is shared between the individual sensors and how this information is managed. GNSS/INS integrated system may have a variety of structures. However, in practical implementations, two main approaches predominate over the rest: the loosely coupled (LC)(Solimeno, 2007) and the tightly coupled (TC)(Petovello, 2003) schemes. Both strategies can be open-loop, where the estimation of the INS errors does not interfere with the operation of the INS, or closed loop, where the sensor errors are compensated within the calculation procedure of the INS mechanization algorithm.

In a loosely coupled integrated system, the GNSS receiver runs internally its own Kalman filter (GNSS filter). This filter calculates the user position and velocity by processing the pseudorange and Doppler measurements measured by the sensor chip. Under this approach, the differences between the INS and GNSS calculated positions and velocities, are used as measurements for a second Kalman filter (INS filter). This second filter is in charge of providing estimations of all the observable INS errors, which are consequently used to correct the IMU raw measurements and to compensate the INS outputs. Figure 4-2shows a typical loosely coupled scheme (Solimeno, 2007)

In Figure 4-2, 𝝎𝑖𝑏𝑏 is the angular rate in body axes measured by the gyroscopes, 𝒂𝑏

is the specific force in body axes measured by the accelerometers, 𝒓𝐼𝑁𝑆𝑛 and 𝒗𝐼𝑁𝑆𝑛 are

the position and velocity in the navigational frame respectively, 𝛿𝒓𝑛 and 𝛿𝒗𝑛 are the difference between the calculated position and velocity of the GPS and the INS, 𝝆 and 𝝆̇ are the raw pseudoranges and carried phase measured by the GPS receiver and Δ𝒓𝑛, Δ𝒗𝑛 and 𝜺𝑛 are the position, velocity and attitude errors estimated by the INS Kalman filter.

On the other hand, the tightly coupled integration uses a single Kalman filter to integrate both GNSS and INS measurements. This is referred to as tight integration because the INS measurements are also used to aid the GNSS processing. In this case, the position is used to form predicted range measurements to each of the satellites. These ranges are then differenced with the raw GNSS measurements to estimate the INS errors using a single integration filter. An example of this kind of strategy is shown in Figure 4-3.

Figure 4-3: INS/GPS Tightly Coupled integration scheme

measurements of the GPS and the predicted ones by using the Ephemeries information and the INS calculations.

The loosely coupled scheme has as advantages that the processing time is faster than the tightly coupled one. This is because the dimension of its state vector is smaller than the used in the tightly scheme. Also, the implementation of this architecture is simple and it is possible to build it with the most commons GNSS receivers interfaces (no need to access to information that it is usually handled internally by the GNSS receiver). However, the disadvantages of this scheme are that the GPS receiver needs at least 4 satellites for computing the solution and that the capabilities of filtering are reduced because the process noise is added to two different fusion filters. On the other hand, the tightly coupled integration can provide a solution of navigation even if there are less than 4 satellites visible. The existence of a single filter provides a statistically rigorous information framework between filter states and improves the filtering capabilities because the process noise is only added to this unique filter. Another advantage of the tightly coupled algorithms is the use of the individual data provided by the satellites in the measurement update since satellites with poor measurements can be rejected. However, the TC implementation has some problems because it uses a larger size of the state vector so the computational cost of the fusion algorithm is increased with respect to the LC one. Furthermore, another disadvantage is that requires complete access to the GNSS raw data information, and this is not usually available by regular receivers, so the list of devices that can be used for this approach is drastically reduced.

In this thesis, the global navigator algorithm is implemented as a loosely-coupled scheme. The filter developed manages the following information: accelerations, angular rates, magnetic field measurements and the position, velocity and integrity solution of the EIRE receiver. In Figure 4-4, it is shown the scheme of the implemented filter architecture. The sensor fusion algorithm is based on an Extended Kalman Filter whose system model has been implemented by using the INS error dynamic equations (see section 4.4).

Figure 4-4: Loosely coupled architecture of the EIW global navigator.

In Figure 4-4, 𝜓𝑀𝐴𝐺 is the heading calculated by the magnetometer, 𝛿𝜓 is the

difference between the headings calculated with the magnetometer and the INS, 𝒃𝑎

and 𝒃𝑔 are the bias correction terms for the accelerometers and the gyroscopes

respectively, and PL are the protection levels calculated by using the integrity information provided by EGNOS.

In the developed algorithm, the measurement model of the fusion filter uses as inputs the differences between the positions and velocities calculated through the INS and the GNSS receiver respectively. It also uses the heading difference calculated comparing the magnetometer calculations and the INS solution. Here, one of the novelties is the use of the integrity information provided by the EGNOS satellites for weighting the filter parameters, allowing the adaptation of the algorithm to the current state of the satellite constellations (this is described in section 4.5.3). This algorithm provides estimations of all observable INS errors, which are used to correct the IMU raw measurements and also to compensate for the navigation solution calculated previously through the mechanization equations. Through this approach, it is possible to obtain a navigational solution with both higher update rate and smaller positioning error than the standalone GPS-receiver and with better accuracy than those navigation algorithms that do not make use of the EGNOS properties.