The Least Squares (LS) approach has been widely used for solving GPS NAVIGATION problems. Despite its many superior properties, however, the LS estimate can be sensitive to outliers, and its performance, in terms of accuracy and statistical inferences, may be compromised when the errors are large and heterogeneous. The GPS signal is strongly affected by multipath propagation errors. The LS is not able to cope with the above condition to provide a useful and plausible solution. In this paper, an alternative approach, based on the Least Absolute Deviation (LAD) criterion, for estimating NAVIGATION solutions is carried out. As a robust estimator, the LAD estimator is known to approximately produce maximum-likelihood estimation. In this case, the maximum-likelihood estimator is obtained by minimizing the mean absolute deviation, rather than the mean square deviation, and, accordingly, can perform robust and effective estimations. Unlike the LS method, the LAD method is not as sensitive to outliers and, so, may provide more robust estimates. Therefore, the LAD method provides a useful and plausible NAVIGATION solution. Simulation results show that the method can effectively mitigate GPS multipath errors.

In recent years, GPS has attracted the attention of many users in the industrial, military and commercial fields due to its accurate time and position information. Because the received signal power on the earth surface is lower than the thermal noise level, it can seriously subject to intentional or unintentional interferences. Intentional interference is known as ‘jamming’. Although the GPS spread-spectrum signal structure has some inherent jam protection, when a hostile jammer want to disturb a GPS system need only send out a jamming signal with enough power and suitable time/frequency properties to deny the use of GPS. Using Neural Networks (NNs) is a non-linear filtering approach for tracking and canceling interference. In this paper, we investigate one of the NNs structures (multi-layer perceptron) and the possibility of interference elimination using this network. Finally, the proposed method will be compared with one of wavelet structures. It can be seen that the proposed algorithm identifies more than four satellites for solving the NAVIGATION equations. In addition, it is robust against the increment of jammer power ( from 25dB to 50dB) and improves the similarity of predicted signal to the real one about 45% in comparison with the wavelet structure.

As nowadays the GPS NAVIGATION system has more usage in different areas, increasing its efficiency and accuracy has gained more importance. The transmitted signal travels a long distance from the satellites to reach the receivers on the ground, so its power fades. This faded signal can easily be affected by intentional noises, the so-called jamming, or unintentional noises. One of the most destructive kinds of jamming is the continuous wave (CW) jamming. The most favored method for countering this jamming is the notch filter. Therefore, in this paper, an adaptive notch filter (ANF) with a narrow response in proposed to reduce the effects of CW jamming. A kind of PSO evolutionary algorithm called the improved particle swarm optimization algorithm (IPSO) is used to adapt the filter’ s coefficients according to the power and frequency of the jamming signal. Evolutionary algorithms are used in problems without any straight forward answer, and that is why we chose this method for designing the filter. It also reduces the complexity of solving such mathematical problems. Finally, the efficiency of the proposed method is compared to other similar solutions, showing a significant improvement in the similarity of recovered signal to the original signal (up to 99%), as well as an increase in the number of observed satellites up to 6, and error reduction in determining the user coordinates which is the primary goal of the GPS system.

Auto-navigating of unmanned aerial vehicles (UAV) in the outdoor environment is performed by using the Global positioning system (GPS) receiver. The power of the GPS signal on the earth surface is very low. This can affect the performance of GPS receivers in the environments contaminated with the other source of radio frequency interference (RFI). GPS jamming and spoofing are the most serious and intentional RFI attacks. Due to the jamming attacks, the positioning accuracy of UAV will be degraded. This positioning error can be reached to tens of kilometers off the true location as was reported in some research articles. The detection of GPS jamming is the first step to mitigate this attack. Most of jamming detection methods are based on GPS signals processing. If a jamming detection system just relies on GPS signal processing to detect and confront RFI threats, it will be capable of failure in the use of the more advanced hardware and sophisticated methods by invaders intentional. The camera is a common sensor almost in all of UAVs which is a passive sensor and resistant to the jamming signals. Here, the use of image-based NAVIGATION methods for GPS jamming detection will be helpful due to the insensitivity of camera sensors to jamming signals. GPS jamming attacks can be detected in UAV NAVIGATION, independently of the signal processing methods, using a comparison of two flight trajectories assigned for a UAV, determined from visual NAVIGATION and GPS positioning data. For this purpose, the trajectory descriptor of the Normalized Distance of the Consecutive Points (NDCP) and trajectory descriptor of the Consecutive Directions Angles (CDA) is used. These descriptors are independent of the coordinate system of the trajectory but are not independent of the number of trajectory points. As a result of the jamming attacks, the GPS receiver may not be able to receive the signal and the positioning cannot be performed. Therefore, two trajectories of UAV from GPS and visual NAVIGATION may have a different number of points. So, the NDCP and CDA cannot be used for these trajectories in all the time. The trajectory descriptor of Histogram of the Oriented Displacements (HOD) is independent of the number of trajectory points, but it is not independent of the coordinate system of the trajectory. In this paper, a method is developed to allow the use of the HOD trajectory descriptor to detect the occurrence of the GPS jamming attacks. For this purpose, first, the coordinate system of the UAV trajectory from GPS data is transformed to the coordinate system of the UAV trajectory from visual NAVIGATION. Here, a sliding window-based approach is used for determining of the jamming location. The performance of the HOD trajectory descriptor in detecting jamming attacks versus the NDCP and CDA trajectory descriptors were compared. The results show that the HOD trajectory descriptor has a significant advantage in detecting the jamming attacks concerning NDCP and CDA trajectory descriptors, especially in the positioning errors of more than ten meters due to jamming attacks, which can be more reliable. This descriptor can be used to detect the occurrence of the jamming attacks. The ability of the HOD trajectory descriptor is significant in detecting positioning errors greater than five meters, compared to the other two trajectory descriptors.

In this paper, we present the results of a hierarchical SLAM/GPS/INS/WLFP sensor fusion to be used in NAVIGATION system devices. Due to low quality of the inertial sensors, even a short-term GPS failure can lower the integrated NAVIGATION performance significantly. In addition, in GPS denied environments, most NAVIGATION systems need a separate assisting resource, in order to increase the availability and reliability of the device. When the GPS service/information is available, the integrated SLAM system arranges for a landmark-based map using a GPS/INS feature. But in case of inaccessibility of GPS information, the latest formerly produced map plays an important role in decreasing the INS errors. In addition, a Wireless Fingerprinting (WLFP) mechanism helps us limit the errors in the system. The results of the proposed method decreases the average estimation precision on the order of 2.6m, without any performance degradation and in different experiments, which is the maximum sustainable error (below 2.66m) for flyer landing on the base. The mentioned method could be used in computer networks to schedule the services too.

The estimation of situation in a combinational NAVIGATION GPS/INS with least number of satellites is the main purpose of this paper. As inertial measurement unit uses altimeter for height measurement, we can assume which height poses certain amounts, whereas geographical length and width are unknown to us in this paper. The single difference GPS is employed for updating the inertial NAVIGATION system and also there is a proper use of particle filter for filtering and error estimation in this system, although regarding to high dimension of state equations in combinational NAVIGATION GPS/INS the use of particle filter needs a lot of calculations and consequently more time. As the kalman filter could be the optimum filter for the linear systems which bear gussian noise, the use of kalman filter for estimation is recommended for linear equation section and use of particle filter in nonlinear section. The comparison of mentioned algorithm's results with extended kalman filter, indicated when there is two GPS satellites the error estimation of situation is bounded by using this method, whereas the application of extended kalman filter increases the error.

Due to the importance of NAVIGATION accuracy, the inertial NAVIGATION system is often combined with one of the other NAVIGATION systems. In one of these methods, the Inertial NAVIGATION System is combined with Global Positioning System. An integrated NAVIGATION system consisting of both systems provides reliable, accurate, and consistent NAVIGATION capabilities. The design of the estimator filter is one of the basic steps in the implementation of integrated NAVIGATION systems. Due to the difficulty in obtaining the accurate model of nonlinear systems, also the complexity of noise in practical environments and existence of noise with uncertain statistical characteristics, the accuracy of the KF estimation is greatly reduced. Therefore, in this paper, to improve the integrated NAVIGATION performance, a concept of self-adaptation to the KF is introduced, and the modified Sage-Husa adaptive Kalman filter algorithm based on the recursive noise estimator basis of the maximum posterior likelihood estimation is formed to overcome the shortcomings of the KF methods and solve the problem of state estimation in practical environments with complex noise and uncertain statistical characteristics and uncertainty in the model. A vehicle test was used to evaluate the proposed algorithm, and the results showed that the proposed algorithm has very acceptable accuracy and performance. so it was able to improve the RMSE evaluation criteria of position in the direction of height and speed in the vertical direction by about 38% and 25%, respectively and The RMSE evaluation criteria and Std evaluation criteria of the heading angle are 18% and 17%, respectively.

Inertial NAVIGATION is a method to determine the position, velocity and attitude of a vehicle. In these systems, three perpendicular accelerometers and three perpendicular gyroscopes are used to measure linear accelerations and angular velocities of vehicles, respectively. Using some special equations and the measured accelerations and angular velocities, the position, velocity and attitude of a vehicle can be determined. Although these systems have great advantages, some disadvantages such as the increase of error position, velocity and attitude of vehicle by the passage of time exist. These disadvantages are due to errors of inertial sensors. In this paper, firstly, a suitable error model of inertial sensors is proposed. In the following, to decrease these errors and to correct the operation of inertial NAVIGATION system; the INS is integrated with GPS. Integration of INS and GPS is done using extended Kalman filter. To this purpose, required equations and relations for integration of INS and GPS are extracted and proposed completely and illustratively. Finally, the simulation of integration of INS and GPS is presented for two different motion scenarios. The results of simulation show great improvements in characteristic of INS+GPS system in comparison to INS individually.

Due to widespread use of Global Positioning System (GPS) in different applications, the issue of GPS signal interference cancelation is becoming an increasing concern. One of the most important intentional interferences is spoofing signals. An effective interference (delay spoof) reduction method based on adaptive filtering is developed in this paper. The principle of method is using adaptive filters to eliminate interference, obtain an estimate of interfering signal and subtract that from the corrupted signal. So, what remains in the output is the desired signal. Here, for updating the filter coefficients adaptive algorithms in both time (statistical and deterministic) and transform domain will be studied. The proposed adaptive filter is applied to a batch of spoofing GPS data in pseudo-range level. The results indicate that all investigated algorithms are able to reduce positioning steady-state miss-adjustment up to 70 percent. In this context, the variable step-size least mean square algorithm performs better than others do.

Inertial NAVIGATION System (INS) and Global Positioning System (GPS), are used in various NAVIGATION and positioning applications. Because each of the INS and GPS technologies has some limitations and advantages, during last two decades, the systems integration has been widely used for accurate and reliable NAVIGATION and positioning. In an integrated system, accurate GPS observations are used to estimate the high rate INS errors and state vector (including INS error vector, position, velocity and other optional parameters). A field test results are presented in this paper. The goal of this test is to compare the coordinates of a relatively low cost INS, GPS RTK coordinates, and the integrated GPS/INS results. The decentralized approach has been used for this integration.