Nitin Sharma

Recent urbanization has posed challenges for the global navigation satellite system (GNSS) to provide accurate navigation solutions. This is especially true in GNSS-denied environments, where the clear line of sight (LOS) path between the satellites and receiver is lacking. For such environments, fusion-based techniques relying on external sensors and/or other signals are widely used. However, such external sensors and signals may not be feasible and/or cost-effective every time. To overcome these limitations, this work proposes a system that makes explicit use of past available measurements, under certain assumptions, to generate new synthetic measurements. For this purpose, two functions are proposed in this work: a geometrically decaying series and a linear combination of past measurements. To enhance the overall performance of the system, an inertial measurement unit (IMU) is used as an additional measurement source in the extended Kalman filter (EKF). In addition, an approach to adapt the noise co-variances that support the generation of synthetic measurements is proposed. Furthermore, we derive the optimal gain under specific assumptions for a concrete theoretical understanding of the proposed algorithm. The proposed algorithm is tested and validated through two real-world datasets collected in Hong Kong, one corresponding to a moving vehicle inside a significantly long sea tunnel and another set in a harsh urban area, involving complex trajectories. A detailed analysis of the results has been performed with respect to all the aforementioned contributions. Additionally, the proposed algorithm has been compared with other existing algorithms. Experimental results show that a mean error of about 4 m is attained inside the tunnel, while it is around 4.6 m for the second scenario set in a harsh urban environment.

This paper presents a modified dual-frequency carrier-aided vectorized tracking (CA-DFVT) algorithm for Navigation with Indian Constellation (NavIC) receiver. In signal blockage and interference scenarios, even the most advanced receiver architecture may fail to provide accurate positioning solutions. Therefore, this paper proposes an improved CA-DFVT architecture for NavIC signals. The architecture is based on the vector delay frequency lock loop (VDFLL), wherein precise carrier phase measurements from NavIC S and unambiguous code phase measurements from the L5 signal are integrated to form new measurements. These measurements are improved by the novel vector tracking (VT) approach introduced in this paper. The approach adopts the Savitzky-Golay filtering technique to improve the integrated measurements without deforming the actual measurements. Furthermore, the improved CA-DFVT receiver architecture includes a bidirectional algorithm called the Rauch-Tung Striebel (RTS). This process involves a forward estimation extended Kalman filter (EKF) and a backward recursion smoother. In addition, this paper also introduces VT for CA-DFVT based on an unscented Kalman filter (UKF). Simulations and hardware implementation are carried out to evaluate the effectiveness of the proposed algorithm under conditions of signal blockage and interference. Experiments demonstrate that the improved CA-DFVT can achieve better position accuracy than the single-frequency L5/S VT, CA-DFVT-EKF, and CA-DFVT-UKF approaches. The mean position accuracy of improved CA-DFVT improves by atleast 2–6 m, and 1–2 m for signal-blockage and interference conditions, respectively.

Modern aircraft are complex systems, equipped with hundreds of embedded sensors that record a wide repertoire of data during flight, such as crucial engine and airframe parameters, status of flight control system, air conditioning system, landing gear, life-saving and emergency systems. The data from the sensors is stored in the Flight Data Recorder. Maintenance personnel routinely transfer this sensor data to a ground terminal device to analyze it for aircraft health and performance monitoring purposes. Manual methods of extracting sensor data can be tedious and error-prone when large fleets of aircraft are involved. This paper presents a novel system to extract sensor data from aircraft to a ground terminal, wirelessly. The wireless system is implemented using unique, configurable wireless transmitter receivers (WTRs) designed for this purpose. The hardware for the wireless transfer of data was designed, interfaced with a modern aircraft’s system, and tested with the aircraft on the ground and another flying object. The data from the aircraft’s Flight Data Recorder was successfully transmitted and received wirelessly by the ground terminal, over a distance of 50 meters (with aircraft on ground) and 10 Kilometers (with a flying object), in a secure mode with zero packet loss. The WTRs have also qualified the requisite tests for airborne certification.

In recent years, there has been a drastic increase in navigation satellites. Due to the computational limitations of a global navigation satellite system (GNSS) receiver, such as limited tracking channels, bandwidth, and high-power consumption, selecting an optimal subset of satellites with a better geometric dilution of precision (GDOP) has become increasingly difficult. Moreover, the computation load on the receiver increases with multi-channel observation. However, a reduced subset of used satellites will significantly increase the receiver computation performance without compromising the positioning accuracy. This paper presents an end-to-end neural network (NN) architecture based on PointNet and VoxelNet to provide an optimal satellite subset from a multi-constellation with a minimum GDOP. The architecture is based on per-satellite parameters obtained from the global positioning system (GPS) and navigation with the Indian Constellation (NavIC) satellites. Experimental results indicate that the input channel with satellite positions, Doppler, acquisition peak metric, elevation, and azimuth angles provides the best outcome in terms of performance. This method can reduce the computation burden and time by 40–50% when used in the receiver.

Time–frequency (TF) analysis can be used as a useful tool to detect the global navigation satellite system (GNSS) code multipath (MP) and its frequency content. An accurate TF representation (TFR) is critical for highlighting the MP’s frequency signature and identifying its possible sources. The popular short-time Fourier transform (STFT) method has limitations in dealing with fast varying instantaneous frequencies, which are frequently observed in global navigation satellite system (GNSS) measurements due to MP. The wavelet-based synchrosqueezing transform (WSST) is a potential technique for providing a detailed high-resolution TFR. In this letter, we use WSST to perform TF analysis on both simulated and real-world data, demonstrating its utility in detecting code MP and its frequency content in urban environments. Based on the results, possible MP sources for the real datasets were identified. Furthermore, the proposed technique’s TF analysis results were validated using the ray-tracing method, demonstrating its effectiveness for MP detection.