*2.1.1. Dead-reckoning navigation (DR)*

Dead-reckoning navigation is a method of estimating the current position using the moving direction, velocity, and time. It considers errors according to true north and magnetic north. In the case of ground vehicles, only their own velocity needs to be considered, but aircraft and ships must calculate positions by considering ocean currents, wind, and so on. In fact, all navigation systems currently use this dead-reckoning method. Because the accuracy of this method decreases as time and distance increase, celestial navigation is used to determine the accurate position, and then the dead-reckoning method is used from that point forward. The traditional dead-reckoning method used a plotter (a protractor attached to a straight ruler) or a flight computer to determine position. At present, it is calculated automatically using an electronic flight computer.

#### *2.1.2. Inertial navigation system (INS)*

The Inertial Navigation System is a stand-alone navigation system that continuously calculates the position, direction, and velocity of the main body through its own accelerometer, rotation sensor, and arithmetic unit, without receiving any external information [4]. Although GPS offers a precise navigation system, it has limitations in space, deep seas, tunnels, and similar places because the GPS operates only when it can receive signals from the satellite. Furthermore, INS can avoid GPS jamming issues. Because an INS is a PMM, the error increases with time. Moreover, the price increases exponentially as the precision is enhanced. One critical factor in an INS is the accurate entry of the initial position and velocity. After that, the data measured by the accelerometer and rotation sensor are integrated consecutively. The accelerometer provides position data whereas the rotation sensor (gyroscope) provides attitude data. **Figure 1** shows an example of a strapdown inertial navigation system. Velocity and position data can be obtained by integrating the acceleration twice. It is strapped with an accelerometer that considers the acceleration of the tangent and vector components.

#### **2.2. EDMs**

navigation satellite system (GNSS) is an excellent representative of EDMs. Methods to determine longitude and latitude using polar stars or the sun considered as EDMs, which is utilized in the spacecraft nowadays. PMM and EDM have duality. The accuracy of PMMs is exponentially proportional to the cost, and the error increases over time. However, information can be obtained for three axes (X, Y, and Z); this is a stable configuration as no communication problems are incurred. EDMs are relatively cheap and the accuracy is pre-defined according to sensor. However, there may be some restrictions on communication. The navigation equipment in ground vehicles and the orbit propagator in spacecraft are designed to complement

In the case of spacecraft, auxiliary navigation systems using data compression were proposed due to the unpredictable space environment and limited communication. The orbital equation used in spacecraft is represented by a nonlinear differential equation containing multiple perturbation terms, which makes the determination of orbit numerically complex. Generally, a precise determination of orbit is made on the ground, and the information is then transmitted to the spacecraft periodically. However, Deep space communication is restrictive and expensive. Therefore, the cost of communication can be reduced by compressing orbital data. For Low Earth Orbit satellites, the deviations between nominal and real orbit are compressed in the form of Fourier coefficients by using the periodic characteristics of the trajectory [1, 2]. Deep space explorer orbit compression and transmission were

Representative PMMs include dead-reckoning navigation (DR) and the inertial navigation system (INS). They determine the current position by measuring the vehicle's own velocity and acceleration in addition to initial position data. Due to the nature of PMMs, the error

Dead-reckoning navigation is a method of estimating the current position using the moving direction, velocity, and time. It considers errors according to true north and magnetic north. In the case of ground vehicles, only their own velocity needs to be considered, but aircraft and ships must calculate positions by considering ocean currents, wind, and so on. In fact, all navigation systems currently use this dead-reckoning method. Because the accuracy of this method decreases as time and distance increase, celestial navigation is used to determine the accurate position, and then the dead-reckoning method is used from that point forward. The traditional dead-reckoning method used a plotter (a protractor attached to a straight ruler) or a flight computer to determine position. At present, it is calculated automatically using an

each other, considering the properties of tools such as the INS/GNSS.

proposed using B-spline [3].

4 Multi-purposeful Application of Geospatial Data

**2. Navigation system**

**2.1. PMMs**

increases with time.

electronic flight computer.

*2.1.1. Dead-reckoning navigation (DR)*

EDMs include GNSS, which is represented by GPS. The application scope is very broad, and includes ground vehicles, ships, and airplanes. In this chapter, the use of GNSS for satellites and deep space probes is explained. Defining the current position and velocity of a satellite is called "orbit determination." The orbit determination problem can be largely divided into a system model part, a measurement model part, and an estimation technique part. Each can be explained as follows. First, the system model is a mathematical model that represents the orbital motion and various specific variables. It has to be approximated to some degree because many assumptions are included in the process of deriving the equation of motion. Second, for the measurement model, the GPS navigation solution or the tracking data (line of sight, elevation angle, azimuth angle, etc.) of the ground station is used. Here, the measurement values cannot be the true values due to sensor errors and other reasons, and always include some errors. Third, the estimation technique part estimates the optimum prediction values, that is, the position and velocity of the satellite using the approximated system model

**Figure 1.** Principle of strapdown inertial navigation systems [5].

and inaccurate measurement values. Among these estimation techniques, the batch mode and the sequential model, such as the Kalman Filter, are widely used [6].

#### *2.2.1. Data estimation method*

#### *2.2.1.1. Least squares estimation*

Least squares estimation is also known as the "batch filter." The state equation and measurement equation are as follows:

$$\mathbf{z}\_k = h\_k^T \mathbf{x} + \mathbf{v}\_k \tag{1}$$

This forms a state-space equation together with Eq. (1).

*x*̂

*Pk*

*kk* <sup>=</sup> *Pk*

*Pk* = (*I* − *kk hk*) *Pk*

measurements are updated as follows:

*x*̂

with noise. The estimated value is the orbit.

*2.2.2. GNSS*

defined as follows:

The covariances of the measurement error and the system error can be expressed as R and Q, respectively. The error was assumed as zero-mean Gaussian white noise. First, the covari-

*k*−1

<sup>−</sup> (6)

Introduction to Navigation Systems http://dx.doi.org/10.5772/intechopen.71047 7

<sup>−</sup> Φ + *Q* (7)

*<sup>T</sup>* <sup>+</sup> *<sup>R</sup>* (8)

) (9)

<sup>−</sup> (10)

ances of the estimates and deviations in the system can be obtained by the following:

*k* <sup>−</sup> = Φ *x*̂

<sup>−</sup> = Φ *Pk*−<sup>1</sup>

Second, in the measurement and processing step, the actual measurement is performed. The measured value is expressed as z according to Eq. (1). Finally, in the update step, we must determine which value must be given a greater weight depending on the reliability of the mathematical prediction and the actual measurement. For this purpose, the Kalman gain is

> <sup>−</sup> *<sup>h</sup>* \_\_\_\_\_\_\_\_ *<sup>k</sup> hk Pk* − *hk*

<sup>−</sup> + *kk*(*zk* + *hk x*̂

*k* −

Accordingly, the weights of the system estimates and measurements are considered. The

The degree of update of the Kalman filter is automatically adjusted according to the reliability of the measurement. If the reliability of measurement is good and X is very small, the Kalman gain increases and more weight is given to the measurement than the mathematical prediction; otherwise, the mathematical prediction is preferred. In this way, the process of stochastically finding the optimum estimate using the Kalman gain, which is a weighting factor, is

As explained above, the Kalman filter method estimates the value of the state variable in real time by stochastically filtering through a system model after receiving measurements mixed

The global navigation satellite system (GNSS) estimates positions through the GNSS dedicated satellite orbiting the earth. The global positioning system (GPS) is open to the public and is frequently used. The satellite has a precise time and sends its own position and time information every moment. As shown in Eq. (11) the GPS receiver calculates the straight distance between the satellite and the receiver considering the incoming velocity of signals [8]. The position is determined by a sphere whose diameter is the distance to the satellite if there

*<sup>k</sup>* = *x*̂ *k*

repeated in real time. The typical Kalman filter algorithm is shown in **Figure 2**.

The least squares estimation method estimates the state variable *θ*, which minimizes the error, that is, the difference between the mathematically predicted value *hk <sup>T</sup> θ* and the actual measured value (*zk* ). This can be expressed as follows:

$$L(\mathbf{x}) = \frac{1}{n} \mathbb{E} \left[ \frac{1}{2} \sum\_{k=1}^{n} \{ \mathbf{z}\_k - h\_k^T \mathbf{x} \}^2 \right] \\ = \frac{1}{2n} \mathbb{E} \left[ \sum\_{k=1}^{n} \{ \mathbf{z}\_k - h\_k^T \mathbf{x} \}^2 \right] \\ = \frac{1}{2n} \{ \mathbf{Z}\_n - \mathbf{H}\_n \mathbf{X} \}^T \{ \mathbf{Z}\_n - \mathbf{H}\_n \mathbf{X} \} \tag{2}$$

Eq. (2) is a convex shape for the state variable, and the following condition must be met to obtain the minimum value:

$$\frac{\partial \mathbf{J}}{\partial \mathbf{x}} = \mathbf{0} \ = -H\_u \left( \mathbf{Z}\_u - H\_u \mathbf{X} \right) \tag{3}$$

In the above equation, *X* represents the optimum prediction result. Ultimately, Eq. (3) can be rearranged as follows, which is called the Normal Equation:

$$\hat{\mathbf{X}} = \begin{pmatrix} H\_{\boldsymbol{u}}{}^{\mathrm{T}}H\_{\boldsymbol{u}} \end{pmatrix}^{-1} H\_{\boldsymbol{u}}{}^{\mathrm{T}}Z\_{\boldsymbol{u}} \tag{4}$$

The weighted least square estimation can be also used, which uses a weight matrix to prevent the distortion of estimation results by observation values that contain large errors. This batch estimation method is performed after all the data required for estimation is obtained, and multiple reiterative calculations are required to converge to the desired value.

#### *2.2.1.2. Kalman filter algorithm*

Unlike the aforementioned batch filter, the Kalman filter is used often as a sequential method. The Kalman filter algorithm estimates the optimum prediction value in real time by appropriately mixing predictions made by a mathematical model with measured values from sensors [7]. In the mathematical propagation, which is the first step for the Kalman filter, it is possible to propagate to the target point through an analytical method using appropriate numerical integration or a state transition matrix, assuming that the initial conditions of the orbit are given by the mean and covariance matrix at a random point. The mathematical model is represented by the following state equation:

$$\mathbf{x}\_{k+1} = \mathbf{Q}\_k \mathbf{x}\_k + \mathbf{u}\_k \tag{5}$$

This forms a state-space equation together with Eq. (1).

and inaccurate measurement values. Among these estimation techniques, the batch mode and

Least squares estimation is also known as the "batch filter." The state equation and measure-

The least squares estimation method estimates the state variable *θ*, which minimizes the error,

(*zk* − *hk <sup>T</sup> x*)<sup>2</sup>

*T*

*<sup>T</sup> Hn*)<sup>−</sup><sup>1</sup> *Hn*

Eq. (2) is a convex shape for the state variable, and the following condition must be met to

In the above equation, *X* represents the optimum prediction result. Ultimately, Eq. (3) can be

The weighted least square estimation can be also used, which uses a weight matrix to prevent the distortion of estimation results by observation values that contain large errors. This batch estimation method is performed after all the data required for estimation is obtained, and

Unlike the aforementioned batch filter, the Kalman filter is used often as a sequential method. The Kalman filter algorithm estimates the optimum prediction value in real time by appropriately mixing predictions made by a mathematical model with measured values from sensors [7]. In the mathematical propagation, which is the first step for the Kalman filter, it is possible to propagate to the target point through an analytical method using appropriate numerical integration or a state transition matrix, assuming that the initial conditions of the orbit are given by the mean and covariance matrix at a random point. The mathematical model is rep-

*xk*+1 = Φ*<sup>k</sup> xk* + *wk* (5)

<sup>∂</sup>*<sup>x</sup>* <sup>=</sup> <sup>0</sup> <sup>=</sup> <sup>−</sup>*Hn*

multiple reiterative calculations are required to converge to the desired value.

] <sup>=</sup> \_\_\_1

*<sup>T</sup> x* + *ν<sup>k</sup>* (1)

<sup>2</sup>*<sup>n</sup>* (*Zn* − *Hn X*)*<sup>T</sup>*

(*Zn* − *Hn X*) (3)

*<sup>T</sup> Zn* (4)

*<sup>T</sup> θ* and the actual mea-

(*Zn* − *Hn X*) (2)

the sequential model, such as the Kalman Filter, are widely used [6].

that is, the difference between the mathematically predicted value *hk*

] <sup>=</sup> \_\_\_1 <sup>2</sup>*<sup>n</sup> <sup>E</sup>*[ ∑ *k*=1 *n*

). This can be expressed as follows:

(*zk* − *hk <sup>T</sup> x*)<sup>2</sup>

rearranged as follows, which is called the Normal Equation:

*2.2.1. Data estimation method*

6 Multi-purposeful Application of Geospatial Data

*2.2.1.1. Least squares estimation*

ment equation are as follows:

*<sup>n</sup> <sup>E</sup>*[ \_\_1 <sup>2</sup> ∑ *k*=1 *n*

<sup>∂</sup>*L*(*x*) \_\_\_\_\_

*X*̂ = (*Hn*

obtain the minimum value:

*2.2.1.2. Kalman filter algorithm*

resented by the following state equation:

sured value (*zk*

*L*(*x*) = \_\_1

*zk* = *hk*

The covariances of the measurement error and the system error can be expressed as R and Q, respectively. The error was assumed as zero-mean Gaussian white noise. First, the covariances of the estimates and deviations in the system can be obtained by the following:

$$
\mathfrak{X}\_k^- = \mathfrak{O} \,\mathfrak{X}\_{k-1}^- \tag{6}
$$

$$P\_k^- = \blacktriangleright P \\ P\_{k-1}^- \oplus \blacktriangleright Q \tag{7}$$

Second, in the measurement and processing step, the actual measurement is performed. The measured value is expressed as z according to Eq. (1). Finally, in the update step, we must determine which value must be given a greater weight depending on the reliability of the mathematical prediction and the actual measurement. For this purpose, the Kalman gain is defined as follows:

$$k\_k = \frac{P\_k^{\cdot \cdot} h\_k^{\cdot}}{h\_k P\_k^{\cdot \cdot} h\_k^{\cdot \cdot} + R} \tag{8}$$

Accordingly, the weights of the system estimates and measurements are considered. The measurements are updated as follows:

$$\pounds\_{k} = \pounds\_{k}^{-} + k\_{k}(z\_{i} + h\_{k}\pounds\_{k}^{-}) \tag{9}$$

$$P\_k = \left(I - k\_k h\_k\right) P\_k^- \tag{10}$$

The degree of update of the Kalman filter is automatically adjusted according to the reliability of the measurement. If the reliability of measurement is good and X is very small, the Kalman gain increases and more weight is given to the measurement than the mathematical prediction; otherwise, the mathematical prediction is preferred. In this way, the process of stochastically finding the optimum estimate using the Kalman gain, which is a weighting factor, is repeated in real time. The typical Kalman filter algorithm is shown in **Figure 2**.

As explained above, the Kalman filter method estimates the value of the state variable in real time by stochastically filtering through a system model after receiving measurements mixed with noise. The estimated value is the orbit.

#### *2.2.2. GNSS*

The global navigation satellite system (GNSS) estimates positions through the GNSS dedicated satellite orbiting the earth. The global positioning system (GPS) is open to the public and is frequently used. The satellite has a precise time and sends its own position and time information every moment. As shown in Eq. (11) the GPS receiver calculates the straight distance between the satellite and the receiver considering the incoming velocity of signals [8]. The position is determined by a sphere whose diameter is the distance to the satellite if there

**Figure 2.** Kalman filter algorithm.

is one satellite, by a circle in space if there are two satellites, and by one point if there are three satellites (**Figure 3**). **Figure 3** illustrates the typical satellite orbit determination method. This orbit determination algorithm can be implemented by a computer installed in the ground station or in the satellite, according to the satellite operation scenario. In other words, an appropriate model and algorithm should be chosen depending on the performing entity. This concept can be expanded to ships, ground vehicles, airplanes, etc. (*<sup>X</sup>* <sup>−</sup> *Xs*)<sup>2</sup> <sup>+</sup> (*<sup>X</sup>* <sup>−</sup> *Xs*)<sup>2</sup> <sup>+</sup> (*<sup>X</sup>* <sup>−</sup> *Xs*)2 + *<sup>s</sup>* (11)

$$r = \sqrt{(X - X\_s)^2 + (X - X\_s)^2 + (X - X\_s)^2} + s \tag{11}$$

The GNSS is comprised of a space part, a ground control part, and a user part, which have an organic relationship with one another (**Figure 4**). The space part consists of approximately 30 clustered satellites in a space orbit. When satellites reach the end of their life, new satellites are launched to maintain a constant number. The measurement error decreases as the number of satellites increases, and more satellites are also added due to communication interruptions by the Earth. Techniques such as SBAS and DGPS are used to enhance accuracy. The ground control part constitutes control facilities on the ground that monitor and adjust the correct rotation of the satellites around the orbit. The GPS has one main control station and four unmanned monitor stations. They monitor if satellites are moving in a given orbit, and perform orbital maneuvering if any satellite moves out of the orbit. The user part consists of

Introduction to Navigation Systems http://dx.doi.org/10.5772/intechopen.71047 9

The INS and GPS are complementary. The INS detects the navigation information (position, velocity, and attitude) of moving bodies using an accelerometer and gyroscope. The precision and cost of an INS are exponentially proportional and its error increases with time. However, it is more stable than other systems because it can obtain information about three axes, X, Y, and Z, and has no communication problems. It has the advantage of not being affected by the external environment, and providing highly accurate and continuous navigation data for short voyage durations. The GPS is a system that determines the position and velocity of moving

general users and GPS receivers that can be purchased.

**2.3. GPS/INS coupling system**

**Figure 4.** Components of GPS systems.

**Figure 3.** Orbit termination scheme via GPS.

The GNSS is comprised of a space part, a ground control part, and a user part, which have an organic relationship with one another (**Figure 4**). The space part consists of approximately 30 clustered satellites in a space orbit. When satellites reach the end of their life, new satellites are launched to maintain a constant number. The measurement error decreases as the number of satellites increases, and more satellites are also added due to communication interruptions by the Earth. Techniques such as SBAS and DGPS are used to enhance accuracy. The ground control part constitutes control facilities on the ground that monitor and adjust the correct rotation of the satellites around the orbit. The GPS has one main control station and four unmanned monitor stations. They monitor if satellites are moving in a given orbit, and perform orbital maneuvering if any satellite moves out of the orbit. The user part consists of general users and GPS receivers that can be purchased.

### **2.3. GPS/INS coupling system**

is one satellite, by a circle in space if there are two satellites, and by one point if there are three satellites (**Figure 3**). **Figure 3** illustrates the typical satellite orbit determination method. This orbit determination algorithm can be implemented by a computer installed in the ground station or in the satellite, according to the satellite operation scenario. In other words, an appropriate model and algorithm should be chosen depending on the performing entity. This

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

(*<sup>X</sup>* <sup>−</sup> *Xs*)<sup>2</sup> <sup>+</sup> (*<sup>X</sup>* <sup>−</sup> *Xs*)<sup>2</sup> <sup>+</sup> (*<sup>X</sup>* <sup>−</sup> *Xs*)2 + *<sup>s</sup>* (11)

concept can be expanded to ships, ground vehicles, airplanes, etc.

*r* = √

**Figure 3.** Orbit termination scheme via GPS.

**Figure 2.** Kalman filter algorithm.

8 Multi-purposeful Application of Geospatial Data

The INS and GPS are complementary. The INS detects the navigation information (position, velocity, and attitude) of moving bodies using an accelerometer and gyroscope. The precision and cost of an INS are exponentially proportional and its error increases with time. However, it is more stable than other systems because it can obtain information about three axes, X, Y, and Z, and has no communication problems. It has the advantage of not being affected by the external environment, and providing highly accurate and continuous navigation data for short voyage durations. The GPS is a system that determines the position and velocity of moving

**Figure 4.** Components of GPS systems.

bodies by measuring the pseudo range to moving bodies from at least 4 of the 24 satellites orbiting the Earth. The GPS is relatively low-priced, does not accumulate errors with time, and the error range is fixed. However, its disadvantage is that the performance drops if there is severe jamming, or the number of visible satellites is less than four. Furthermore, visibility and communication may become restricted [9].

If you design a navigation system using only one method, high costs are required to obtain the desired performance. However, high-performing sensors can be obtained at low cost if you consider complementary performance. Thus, the navigation system of ground equipment and the orbiter of a space probe are designed to complement each other. We can design navigation systems more effectively by using the INS and performing periodic calibrations with GPS. Furthermore, this can make the overall navigation system more robust than if using only one method [10]. Current research is attempting to integrate these two systems, using integration methods that can be largely divided into tight and loose INS/GPS integration systems [11]. One characteristic of these integration systems is that the performance can be enhanced by improving the tracking performance of the code and carrier tracking loops of the GPS receiver.

**3. Auxiliary navigation system using data compression technique**

received from the ground station.

**Figure 6.** Loosely coupled INS/GPS integration systems.

The analysis of space orbital motion generally means the integration of the nonlinear orbital motion equation. For this analysis, special perturbation [12], which is a numerical method, or general perturbation, which is an analytical method, is used. Special perturbation has a small error due to numerical integration during orbit propagation, but requires a high-performance onboard computer. General perturbation has a small computational load during orbit propagation, but the numerical integration error increases substantially with time. To improve this shortcoming, low-orbit satellites generate a residual, which is the difference between the reference orbit and the true orbit. The residuals, which exhibit periodic characteristics, are approximated using the coefficients of trigonometric and Fourier functions, before being transmitted to the satellites along with the reference orbital elements. The satellite computer can improve the precision of orbit propagation and greatly reduce the computational load by generating a reconstruction orbit [13, 14] using coefficients and reference orbital elements

Introduction to Navigation Systems http://dx.doi.org/10.5772/intechopen.71047 11

However, the approximation method using residuals cannot be applied to deep space probes for the Moon and Mars because they do not rotate around the Earth repeatedly, like satellites. As a solution, an auxiliary navigation system using B-spline data compression has been proposed [3]. The data compression rate must be increased because deep space communication is expensive and limited in communication time. For Earth and lunar orbits where takeoff and landing occur, intensive control and communication are essential due to the danger and unpredictability. However, in the transition segment, which is stable and accounts for the majority of navigation, communication time can be saved, and stable navigation data can be

Satellite orbit compression using the Fourier technique, and development of an auxiliary navigation system using this technique, have mainly been studied using a low-orbit satellite model [1, 2, 12, 15]. The overall operation concept of an onboard orbit propagator is

received by sending compressed orbit data calculated on the ground to the probes.

**3.1. Satellite orbit compression using the Fourier technique**

The structure of the tightly coupled INS/GPS integration system is shown in **Figure 5**. Data measured from the GPS receiver (pseudo range, pseudo range rate) and the INS data (position, velocity, attitude) include many errors. The INS and GPS do not perform separate filtering, but these errors are estimated by one Kalman filter. The loosely coupled INS/GPS integration system is shown in **Figure 6**. In this system, INS and GPS perform measuring and filtering separately.
