ADS-B Signal Verification Using a Coherent Receiver

Automatic Dependent Surveillance-Broadcast (ADS-B) enables aircraft to periodically broadcast their flight states such as position and velocity. Compared to classical radar surveillance, it increases update rate and accuracy. Currently, Mode S Extended Squitter is the most common implementation for ADS-B. Due to the simplicity of Mode S design, ADS-B signals are prone to injections. This study proposes a cost-effective solution that verifies the integrity of ADS-B signals using coherent receivers. We design the verification approach by combining the signal’s direction of arrival, estimated from the multi-channel data, with the target bearing calculated from ADS-B messages. By using another high-performance software-defined radio transceiver, we also conduct real signal injection experiments to validate our approaches.

Standardized Judgment Method of Shooting Training Action Based on Digital Video Technology

Aiming at the difficulty of standardizing the action of basketball shooting training, a new method of standardizing the action of basketball shooting training is proposed based on digital video technology. The digital video signal representation, video sequence coding data structure, and video sequence compression coding method are analyzed, and the pixels of basketball shooting training action position space are sampled to collect basketball shooting training images. The time difference method is used to extract the movement target of basketball shooting training from a digital video sequence. Based on digital video technology, the initial background image is estimated, and the update rate is introduced to update the background estimation image. According to the pixel value sequence of the basketball shooting training image, the pixel model of the basketball shooting training image is defined and modified. By judging whether the defined pixel value matches the background parameter model, the standardization of shooting training can be realized. The experimental results show that the proposed method has good stability, high precision, and short time in determining the standardization of shooting movement, can correct the wrong shooting movement in real time, and can effectively guide basketball shooting training.

Assessment of Galileo E6B Data Demodulation Performance at High Latitudes: A Norwegian Vessel Case Study

The Galileo High Accuracy Service (HAS) is currently in its testing phase, in which actual corrections are transmitted along with standard dummy messages. The dissemination of Precise Point Positioning (PPP) corrections is performed using an innovative scheme based on a Reed–Solomon code, which allows the reconstruction of the original navigation message from a subset of received pages. This approach introduces robustness to the reception process and aims at reducing the Time-To-Retrieve Data (TTRD); that is, the time to retrieve the HAS message. This study investigated the HAS demodulation performance considering Galileo signals collected at high latitudes. In particular, a Galileo E6-capable receiver was mounted on a vessel sailing from Bergen to Kirkenes, Norway, and reaching up to 71 degrees North. The trajectory of the vessel was at the border of the Galileo HAS service area and high-latitudes impact reception conditions, potentially leading to poor satellite geometries. Three months of data from January to March 2021 were analyzed, considering several metrics including Bit Error Rate (BER), Page Error Rate (PER), and TTRD. The analysis shows that the Reed–Solomon scheme adopted for data dissemination is also effective at high-latitudes, with daily PER below one percent and mean TTRD in the order of eight seconds when three satellites are broadcasting valid HAS corrections. Lower values of the TTRD are achieved with an increased number of satellites. These values are significantly lower than the update rate of the corrections broadcast by the Galileo HAS.

Scale up to infinity: the UWB Indoor Global Positioning System

Determining assets position with high accuracy and scalability is one of the most investigated technology on the market. The accuracy provided by satellites-based positioning systems (i.e., GLONASS or Galileo) is not always sufficient when a decimeter-level accuracy is required or when there is the need of localising entities that operate inside indoor environments. Scalability is also a recurrent problem when dealing with indoor positioning systems. This paper presents an innovative UWB Indoor GPS-Like local positioning system able to tracks any number of assets without decreasing measurements update rate. To increase the system’s accuracy the mathematical model and the sources of uncertainties are investigated. Results highlight how the proposed implementation provides positioning information with an absolute maximum error below 20 cm. Scalability is also resolved thanks to DTDoA transmission mechanisms not requiring an active role from the asset to be tracked.

Scale up to infinity: the UWB Indoor Global Positioning System

Determining assets position with high accuracy and scalability is one of the most investigated technology on the market. The accuracy provided by satellites-based positioning systems (i.e., GLONASS or Galileo) is not always sufficient when a decimeter-level accuracy is required or when there is the need of localising entities that operate inside indoor environments. Scalability is also a recurrent problem when dealing with indoor positioning systems. This paper presents an innovative UWB Indoor GPS-Like local positioning system able to tracks any number of assets without decreasing measurements update rate. To increase the system’s accuracy the mathematical model and the sources of uncertainties are investigated. Results highlight how the proposed implementation provides positioning information with an absolute maximum error below 20 cm. Scalability is also resolved thanks to DTDoA transmission mechanisms not requiring an active role from the asset to be tracked.

CW Direct Detection Lidar with a Large Dynamic Range of Wind Speed Sensing in a Remote and Spatially Confined Volume

A novel continuous-wave (CW) direct detection lidar (DDL) is demonstrated to be capable of wind speed measurement 40 m away with an update rate of 4 Hz using a fiber-based scanning Fabry–Perot interferometer as an optical frequency discriminator. The proposed CW DDL has a large dynamic wind speed range with no sign ambiguity and its sensitivity is assessed by comparing its performance with that of a CW coherent detection lidar (CDL) in a side-by-side wind measurement. A theoretical model of the spatial weighting function of the fiber-based CW DDL is also presented and validated experimentally. This work shows that the CW DDL has a spatially confined measurement volume with a Lorentzian axial profile similar to that of a CW CDL. The proposed DDL has potential use in various applications in which requirements such as high-speed wind sensing and directional discrimination are not met by state-of-the-art Doppler wind lidar systems.

A Single-Copter UWB-Ranging-Based Localization System Extendable to a Swarm of Drones

This paper presents a single-copter localization system as a first step towards a scalable multihop drone swarm localization system. The drone was equipped with ultrawideband (UWB) transceiver modules, which can be used for communication, as well as distance measurement. The location of the drone was detected based on fixed anchor points using a single type of UWB transceiver. Our aim is to create a swarm localization system that enables drones to switch their role between an active swarm member and an anchor node to enhance the localization of the whole swarm. To this end, this paper presents our current baseline localization system and its performance regarding single-drone localization with fixed anchors and its integration into our current modular quadcopters, which was designed to be easily extendable to a swarm localization system. The distance between each drone and the anchors was measured periodically, and a specially tailored gradient descent algorithm was used to solve the resulting nonlinear optimization problem. Additional copter and wireless-specific adaptations were performed to enhance the robustness. The system was tested with a Vicon system as a position reference and showed a high precision of 0.2 m with an update rate of <10 Hz. Additionally, the system was integrated into the FINken copters of the SwarmLab and evaluated in multiple outdoor scenarios. These scenarios showed the generic usability of the approach, even though no accurate precision measurement was possible.

Ultra-Wideband Indoor Positioning and IMU-Based Activity Recognition for Ice Hockey Analytics

Currently, gathering statistics and information for ice hockey training purposes mostly happens by hand, whereas the automated systems that do exist are expensive and difficult to set up. To remedy this, in this paper, we propose and analyse a wearable system that combines player localisation and activity classification to automatically gather information. A stick-worn inertial measurement unit was used to capture acceleration and rotation data from six ice hockey activities. A convolutional neural network was able to distinguish the six activities from an unseen player with a 76% accuracy at a sample frequency of 100 Hz. Using unseen data from players used to train the model, a 99% accuracy was reached. With a peak detection algorithm, activities could be automatically detected and extracted from a complete measurement for classification. Additionally, the feasibility of a time difference of arrival based ultra-wideband system operating at a 25 Hz update rate was determined. We concluded that the system, when the data were filtered and smoothed, provided acceptable accuracy for use in ice hockey. Combining both, it was possible to gather useful information about a wide range of interesting performance measures. This shows that our proposed system is a suitable solution for the analysis of ice hockey.

Using KNN Algorithm Predictor for Data Synchronization of Ultra-Tight GNSS/INS Integration

The INS system’s update rate is faster than that of the GNSS receiver. Additionally, GNSS receiver data may suffer from blocking for a few seconds for different reasons, affecting architecture integrations between GNSS and INS. This paper proposes a novel GNSS data prediction method using the k nearest neighbor (KNN) predictor algorithm to treat data synchronization between the INS sensors and GNSS receiver and overcome those GNSS receiver’s blocking, which may occur for a few seconds. The experimental work was conducted on a flying drone over a minor Hungarian (Mátyásföld, 47.4992 N, 19.1977 E) model airfield. The GNSS data are predicted by four different scenarios: the first is no blocking of data, and the other three have blocking periods of 1, 4, and 8 s, respectively. Ultra-tight architecture integration is used to perform the GNSS/INS integration to deal with the INS sensors’ inaccuracy and their divergence throughout the operation. The results show that using the GNSS/INS integration system yields better positioning data (in three axes (X, Y, and Z)) than using a stand-alone INS system or GNSS without a predictor.