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Global positioning system (GPS) technology has enabled accurate positioning and navigation of jamming that deteriorates the positioning accuracy. We focus on accurate detection of GPS jammers in the frequency domain where fast Fourier transform (FFT) is predominantly used. An innovative high-resolution frequency estimation method to accurately detect single and multiple in-band continuous-wave jamming signals transmitted at very close-by frequencies is proposed. The proposed method utilizes orthogonal search that provides robust nonlinear spectral estimation to detect dominant jammer frequencies. The Spirent GSS 6700 GPS simulator was utilized in this study to generate several cases for the GPS L1 signal. The output of the GSS 6700 was acquired using the Novatel FireHose GPS frontend receiver that digitizes and down-converts the signal into in-phase (I) and quadrature (Q) samples. The results demonstrated its capabilities of simultaneously detecting more than one GPS jammer existing at close-by frequencies. It is also shown that jammer frequency estimates obtained for a single jammer are more accurate than those obtained by FFT. Furthermore, FOS yields more accurate results than FFT at considerably smaller window sizes.

Global navigation satellite system (GNSS), such as global positioning system (GPS), has been widely used for vehicular and outdoor navigation. Accuracy is one, among many, of the advantages of using GNSS in the open sky. However, GNSS finds difficulty in achieving similar results in portable navigation, where users spend most of their time indoors or in urban canyons, places where GNSS signals suffer from multipath error or signal blockage. One of the most common solutions for providing location services in such challenging environments is integrating GNSS with inertial sensors, such as accelerometers and gyroscopes. However, the arbitrary orientation of the portable device can present a more difficult challenge when using inertial sensors for portable navigation. In order to obtain a navigation solution using inertial sensors, an accurate heading estimation is required. Resolving the heading misalignment angle between the portable navigation device and the moving platform, such as using the device while walking or in a vehicle while driving, is critical to obtaining an accurate heading estimation. We present a solution for resolving the misalignment between the portable device and the moving platform, which exploits multiple portable devices like smartphones or tablets and/or smart wearable devices such as smart watches, smart glasses, and/or smart fitness and activity trackers/monitors. Several real field test experiments using portable devices were conducted to examine the performance of the proposed method. Results show how a portable navigation solution can be improved by further enhancing misalignment estimation.

The signal acquisition stage of a GPS receiver detects GPS satellites in view and provides coarse estimate of the GPS signal Doppler frequency shift and code delay for use by the tracking loops. The accuracy of the signal acquisition has a direct influence on the tracking performance. The implementation of a GPS signal acquisition algorithm requires compromising between acquisition frequency resolution improvement and reduction in acquisition time. A robust fine acquisition method is proposed to acquire the carrier frequency accurately after the completion of the coarse acquisition of the GPS signals. The proposed method uses Gram-Schmidt orthogonalization to provide robust spectral estimation of satellite Doppler frequency with less computational time. The proposed method starts after the coarse acquisition has been accomplished. The C/A code phase is striped off from the carrier signal. Then, sinusoidal candidate functions are generated at each of the frequencies range of interest, which is typically set around the estimated Doppler shift acquired from the coarse acquisition stage. Finally, an orthogonal search algorithm is utilized to detect the carrier frequency accurately. The performance of the proposed method is evaluated against of the computational load and the effects of the noise. Its performance was also compared to the state-of-the-art FFT and zero-padding FFT-based fine acquisition algorithms. The simulation and experimental results show that the proposed method outperforms existing methods and has sufficient acquisition accuracy for its application in the real world.

Dead reckoning techniques such as inertial navigation and odometry are integrated with GPS to avoid interruption of navigation solutions due to lack of visible satellites. A common method to achieve a low-cost navigation solution for land vehicles is to use a MEMS-based inertial measurement unit (IMU) for integration with GPS. This integration is traditionally accomplished by means of a Kalman filter (KF). Due to the significant inherent errors of MEMS inertial sensors and their time-varying changes, which are difficult to model, severe position error growth happens during GPS outages. The positional accuracy provided by the KF is limited by its linearized models. A Particle filter (PF), being a nonlinear technique, can accommodate for arbitrary inertial sensor characteristics and motion dynamics. An enhanced version of the PF, called Mixture PF, is employed in this paper. It samples from both the prior importance density and the observation likelihood, leading to an improved performance. Furthermore, in order to enhance the performance of MEMS-based IMU/GPS integration during GPS outages, the use of pitch and roll calculated from the longitudinal and transversal accelerometers together with the odometer data as a measurement update is proposed in this paper. These updates aid the IMU and limit the positional error growth caused by two horizontal gyroscopes, which are a major source of error during GPS outages. The performance of the proposed method is examined on road trajectories, and results are compared to the three different KF-based solutions. The proposed Mixture PF with velocity, pitch, and roll updates outperformed all the other solutions and exhibited an average improvement of approximately 64% over KF with the same updates, about 85% over KF with velocity updates only, and around 95% over KF without any updates during GPS outages.

Most of the present navigation sensor integration techniques are based on Kalman-filtering estimation procedures. Although Kalman filtering represents one of the best solutions for multisensor integration, it still has some drawbacks in terms of stability, computation load, immunity to noise effects and observability. Furthermore, Kalman filters perform adequately only under certain predefined dynamic models. Neuron computing, a technology of artificial neural network (ANN), is a powerful tool for solving nonlinear problems that involve mapping input data to output data without having any prior knowledge about the mathematical process involved. This article suggests a multisensor integration approach for fusing data from an inertial navigation system (INS) and differential global positioning system (DGPS) hardware utilizing multilayer feed-forward neural networks with a back propagation learning algorithm. In addition, it addresses the impact of neural network (NN) parameters and random noise on positioning accuracy.