Space-based Automatic Dependent Surveillance-Broadcast Research Articles

Independently convolutional gated recurrent neural unit for space-based ADS-B signal separation with single antenna

Automatic Dependent Surveillance-Broadcast (ADS-B) is a critical technology to transform aircraft navigation by improving safety and overall effectiveness in the aviation industry. However, overlapping of ADS-B signals is a large challenge, especially for space-based ADS-B systems. Existing traditional methods are not effective when dealing with cases that overlapped signals with small difference (such as power difference and carrier frequency difference) require to be separated. In order to generate an effective separation performance of the ADS-B signals by exploring its temporal relationship, Independently Convolutional Gated Recurrent Neural Unit (Ind-CGRU) is presented for encoder–decoder network construction. Experimental results on the dataset SR-ADSB demonstrate that the proposed Ind-CGRU achieves good performance.

Optimization of digital multi-beamforming for space-based ADS-B using distributed cooperative coevolution with an adaptive grouping strategy

Space-based Automatic Dependent Surveillance-Broadcast (ADS-B) technology can eliminate the blind spots of terrestrial ADS-B systems because of its global coverage capability. However, the space-based ADS-B system faces new problems such as extremely low Signal-to-Noise Ratio (SNR) and serious co-channel interference, which result in long update intervals. To minimize the position message update interval at an update probability of 95% with full coverage constraint, this paper presents an optimization model of digital multi-beamforming for space-based ADS-B. Then, a coevolution method DECCG_A&A is proposed to enhance the optimization efficiency by using an improved adaptive grouping strategy. The strategy is based on the locations of uncovered areas and the aircraft density under the coverage of each beam. Simulation results show that the update interval can be effectively controlled to be below 8 seconds compared with other existing methods, and DECCG_A&A is superior in convergence to the Genetic Algorithm (GA) as well as the coevolution algorithms using other grouping strategies. Overall, the proposed optimization model and method can significantly reduce the update interval, thus improving the surveillance performance of space-based ADS-B for air traffic control.

Multi-Scale Convolutional Network for Space-Based ADS-B Signal Separation with Single Antenna

Automatic Dependent Surveillance-Broadcast (ADS-B) signals are very vital in air traffic control. However, the space-based ADS-B signals are easily overlapped and their message cannot be correctly received. It is challenge to separate overlapped signals especially for a single antenna. The existing methods have a low decoding accuracy for small power difference, carrier frequency difference and relative time delay between overlapped signals. In order to solve these problems, we apply the deep learning method to single antenna ADS-B signal separation. A multi-scale Conv-TasNet (MConv-TasNet) is proposed to capture long temporal information of the ADS-B signal. In MConv-TasNet, a multi-scale convolutional separation (MCS) network is proposed to fuse different scale temporal features extracted from overlapping ADS-B signals and generate an effective separation mask to separate signals. Moreover, a large dataset is created by using the real ADS-B data. In addition, the proposed method has been evaluated on the dataset. The average decoding accuracy on the test set is 90.34%. It has achieved the state-of-the-art results.

Satellite coverage traffic volume prediction using a new surrogate model

The space-based Automatic Dependent Surveillance-Broadcast (ADS-B) can significantly improve aircraft traffic efficiency and safety but suffers from the issue of signal collision. A potential solution for this limitation is employing an adaptive ADS-B antenna, e.g., the phased array antenna, which can adjust its coverage area in real-time during the satellite movement according to the satellite coverage traffic volume (SCTV). This paper proposes a new surrogate modeling technique, i.e., subregion radial basis function (RBF), and applies the proposed surrogate to predict the SCTV so that the adaptive strategy for the ADS-B antenna can be designed. Instead of training one model using all training samples as the classical RBF does, the proposed subregion RBF approach first partitions the input space into multiple fuzzy regions, then trains a RBF model in each region, and finally integrates the multiple subregion RBF models in a weighted average manner. By using this partition of unity strategy, the proposed method can greatly reduce the computational complexity of training models meanwhile maintain the interpolation property and high accuracy of RBF models. Further, the proposed subregion RBF is applied to construct a surrogate for the SCTV model to guide the design of the adaptive strategy for the space-based ADS-B. Simulation results show that compared with the classical and state-of-the-art methods, the proposed method has arrived a better balance between predictive accuracy and computational cost. In the simulation scenarios, it is also observed that by using the adaptive strategy, the detection probability of ADS-B receiver can always be greater than 95% and nearly 1/3 of power consumption can be saved. The simulations demonstrate the effectiveness of the proposed method.

Integrated antenna and receiver system with self-calibrating digital beamforming for space-based ADS-B

This paper describes a digital beamforming (DBF) system with a self-calibrating function to receive Automatic Dependent Surveillance-Broadcast (ADS-B) signals on a satellite, which can expand the coverage range and improve the detection probability that is mainly reduced by multi-signal collision. This system integrates a 4 × 4 microstrip array antenna and a 16-channel receiver into a cuboid box that can simultaneously generate more than 19 independent beams whose size and direction can be flexibly controlled. Specifically, the on-orbit self-calibration method, signal processing algorithm, antenna and receiver design, and system test method are presented in this paper. Test results in a microwave anechoic chamber verified that the design can significantly improve the sensitivity of the receiver, reduce signal collision, and enhance the reliability of the space-based ADS-B system.

The CanX-7 ADS-B Mission: Signal Propagation Assessment

The CanX-7 Automatic Dependent Surveillance-Broadcast (ADS-B) nanosatellite mission collected more than four million ADS-B messages between October 2016 and April 2017. An analysis of data collected over the north Atlantic Ocean from 05 to 28 Oct included 20,707 position messages in which the angle from satellite nadir to aircraft was determined. The proximity of the received signal strength to the noise floor of the sensor allowed for an analysis of optimal aircraft-satellite orientation for ADS-B transmission detection. The results showed a significant disparity between descending and ascending passes of the satellite. For descending passes, the average nadir angle was 50.1° with 90% of the contacts greater than 40°. The ascending passes had an average nadir angle of 31.6° with only 24.8% of the contacts exceeding 40°. The evidence suggests that the satellite magnetic torquer may not have been fully aligned with the north magnetic pole as the satellite moved northward, resulting in ascending pass nadir angles that were not reflective of the full range of values. Further analysis of the descending passes showed agreement with an ADS-B signal propagation model with peak reception at nadir angles of 51° ± 8°. For space-based ADS-B operations, the results support the replacement of the current aircraft upper quarter-wave monopole to an antenna that will transmit more energy directly above the airframe.

Adaptive Multi-beamforming for Space-based ADS-B

We investigate a strategy to address the problem of low aircraft detection probability of space-based Automatic Dependent Surveillance-Broadcast (ADS-B). A nineteen-element hexagonal array and adaptive multi-beamforming method is proposed. This method aims to adjust the beam pattern dynamically to reduce signal collision and enlarge coverage. With the focus on the mission requirement of global aircraft detection by 2020, the appropriate gain and direction of beams are studied and designed in detail. Theoretical analysis and simulations show that this adaptive multi-beam antenna can greatly improve flight detection probability at an average reporting interval from 10 seconds to 5 seconds when compared with the traditional fixed multi-beam antenna. The results of this work show that the design of a 19-element antenna along with the adaptive multi-beamforming method can be considered as a building block of future space-based ADS-B.

The CanX-7 Nanosatellite ADS-B Mission: A Preliminary Assessment

The development of space-based Automatic Dependent Surveillance-Broadcast (ADS-B) will allow surveillance of aircraft in areas not covered by radar or ground-based ADS-B systems. In September 2016, the Canadian Advanced Nanospace eXperiment-7 (CanX-7) satellite was launched into a 690 km sun synchronous orbit with an ADS-B receiver payload. The first phase of ADS-B data collection took place over the North Atlantic between 4 and 31 October. A preliminary assessment of the data indicates that the average ADS-B signal strength is close to the calculated receiver detection threshold of D94.5 ± 0.5 dBm. The pattern of received ADS-B reception appears to be consistent with a signal propagation model developed for the CanX-7 mission. Future work includes the comparison of coincidental flight plan data for the operations area and an analysis of the payload antenna pattern.

CubeSat constellation design for air traffic monitoring

Suitably equipped global and local air traffic can be tracked. The tracking information may then be used for control from ground-based stations by receiving the Automatic Dependent Surveillance-Broadcast (ADS-B) signal. In this paper, we describe a tool for designing a constellation of small satellites which demonstrates, through high-fidelity modeling based on simulated air traffic data, the value of space-based ADS-B monitoring. It thereby provides recommendations for cost-efficient deployment of a constellation of small satellites to increase safety and situational awareness in the currently poorly-served surveillance area of Alaska. Air traffic data were obtained from NASA's Future ATM Concepts Evaluation Tool, for the Alaskan airspace over one day. The results presented were driven by MATLAB and the satellites propagated and coverage calculated using AGI's Satellite Tool. While Ad-hoc and precession spread constellations have been quantitatively evaluated, Walker constellations show the best performance in simulation. Sixteen satellites in two perpendicular orbital planes are shown to provide more than 99% coverage over representative Alaskan airspace and the maximum time gap where any airplane in Alaska is not covered is six minutes, therefore meeting the standard set by the International Civil Aviation Organization to monitor every airplane at least once every fifteen minutes. In spite of the risk of signal collision when multiple packets arrive at the satellite receiver, the proposed constellation shows 99% cumulative probability of reception within four minutes when the airplanes are transmitting every minute, and at ~100% reception probability if transmitting every second. Data downlink can be performed using any of the three ground stations of NASA Earth Network in Alaska.

The Flying Laboratory for the Observation of ADS-B Signals

Automatic dependent surveillance-broadcast (ADS-B) is a system in which aircraft continually transmit their identity and GPS-derived navigational information. ADS-B networks for air traffic monitoring have already been implemented in areas around the world, but ground stations cannot be installed in midocean and are difficult to maintain in the Arctic, leaving a coverage gap for oceanic and high latitude airspace. A potential solution for worldwide tracking of aircraft is through the monitoring of aircraft-transmitted ADS-B signals using satellite-borne receivers. To investigate this possibility, a high altitude balloon experiment was carried out in June 2009 to determine if ADS-B signals can be detected from near space. The Flying Laboratory for the Observation of ADS-B Transmissions (FLOAT) was the first stratospheric platform to collect ADS-B data. The FLOAT mission successfully demonstrated the reception of ADS-B signals from near space, paving the way to the development of a space-based ADS-B system.

Balloon-borne air traffic management (ATM) as a precursor to space-based ATM

The International Space University—Balloon Air traffic control Technology Experiment (I-BATE 1 1 I-BATE: International Space University—Balloon Air traffic control Technology Experiment. ) has flown on board two stratospheric balloons and has tracked nearby aircraft by receiving their Automatic Dependent Surveillance-Broadcast (ADS-B) transmissions. Air traffic worldwide is facing increasing congestion. It is predicted that daily European flight volumes will more than double by 2030 compared to 2009 volumes. ADS-B is an air traffic management system being used to mitigate air traffic congestion. Each aircraft is equipped with both a GPS receiver and an ADS-B transponder. The transponder transmits an equipped aircraft's unique identifier, position, heading, and velocity once per second. The ADS-B transmissions can then be received by ground stations for use in traditional air traffic management. Airspace not monitored by these ground stations or other traditional means remains uncontrolled and poorly monitored. A constellation of space-based ADS-B receivers could close these gaps and provide global air traffic monitoring. By flying an ADS-B receiver on a stratospheric balloon, I-BATE has served as a precursor to a constellation of ADS-B-equipped Earth-orbiting satellites. From the ∼30 km balloon altitude, I-BATE tracked aircraft ranging up to 850 km. The experiment has served as a proof of concept for space-based air traffic management and supports a technology readiness level 6 of space-based ADS-B reception.