How does navigation system work

Navigation-system Sentence Examples. Related articles. Also Mentioned In. Words near navigation-system in the Dictionary.

Due to the nature of PMMs, the error increases with time. 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. 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. 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. Principle of strapdown inertial navigation systems [ 5 ]. 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. 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. 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 and inaccurate measurement values. Among these estimation techniques, the batch mode and the sequential model, such as the Kalman Filter, are widely used [ 6 ]. This can be expressed as follows:. In the above equation, X represents the optimum prediction result.

Ultimately, Eq. 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.

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:. This forms a state-space equation together with Eq. 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:.

Second, in the measurement and processing step, the actual measurement is performed. The measured value is expressed as z according to Eq. 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:. Accordingly, the weights of the system estimates and measurements are considered. The measurements are updated as follows:.

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. Kalman filter algorithm. 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. 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. The position is determined by a sphere whose diameter is the distance to the satellite if there 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. 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. 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.

Components of GPS systems. 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.

Locking on to more signals yields greater accuracy but a four is the absolute minimum required. Design trade-offs have led to a medium-Earth orbit as the optimal altitude for navigation satellite constellations, commencing with the US GPS and Russian Glonass.

There are solid practical reasons for this: medium-Earth orbits are relatively stable and the satellites move across the sky relatively slowly. Lower orbits would require more satellites to maintain the same coverage while higher orbits would reduce coverage extent.

In addition an extensive ground infrastructure distributed worldwide is required to uplink the navigation signals, keep the different clocks of the constellation synchronised and correct any onboard timing or positioning deviation. The satellite navigation signals are very faint, equivalent to car headlights shone from one end of Europe to another.