Aeronautical satellite data link concept, design, and flight test results

Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) are very practical method to get high precision navigation. GPS/INS data Fusion in navigation has been a research topic for decades. Usually the update time of GPS an INS is different. The update time of GPS is 20HZ, and the time of INS is 100HZ or higher. Since GPS and INS are mainly methods in navigation system. Techniques of fusing GPS and INS data are in high demand in navigation industry. Usually the Karman filter is used to fuse the data from GPS and INS. But the Kalman filter is model-dependent and a priori the model parameters need to be known. In this work, a generic and intelligent approach of GPS and INS data fuse using fuzzy interpolation before Kalman algorithm is proposed. First a simplified interpolation model is introduced in a ordinary GPS and INS data fusion. Second a fuzzy rule-based system is established to fuzzy interpolation with objective fuzzy method. Then the parameters of objective fuzzy system are identified, the best n-rule is determinate and the optimal rule base system is found. Finally this approach is applied on an example for a car GPS and INS data fusion, and the results are calculated with computer system to demonstrate the advantages of this approach.

A relative navigation system based on both the Inertial Navigation System (INS) and the Global Positioning System (GPS) is developed to support situational awareness during formation flight. The architecture of the system requires an INS/GPS integration across two aircraft via a data link. A fault-tolerant federated filter is used to estimate the relative INS errors based on relative GPS measurements and a range measurement obtained from the data link. The filter is constructed based on a reduced-order model of the relative INS error process. A method for analyzing the filter performance is presented. A case involving two helicopters in formation flight is studied under three different night trajectories to account for the effect of vehicle motion on the INS state transition matrix. The results of the covariance analysis are compared with actual night results over an instrumented test range.

How to achieve continuous, reliable and accurate positioning performance using low-cost sensors is one of the main challenges for aviation navigation system. Global Positioning System (GPS) can provide the primary means of navigation in a number of aviation navigation applications (e.g., manned and unmanned aircraft vehicle, airport ground vehicle). However, GPS signal deteriorations typically occur due to aircraft itself during maneuvering, ionospheric scintillation, Doppler shift, multipath and so on. Thus, there is a need to research GPS augmentation strategies which can be used in the Communication, Navigation, Surveillance/Air Traffic Management. GPS integration with Inertial Navigation system (INS) is one of the key strategies. But once GPS signal outages, the integrated navigation system works in pure INS, and positioning accuracy deteriorates with time. When using low cost GPS/INS integration, a primary problem is the rapid performance deteriorate during GPS outages. To provide continuous, accurate and reliable positioning information in aviation, discrete grey prediction model (DGPM) aided fusion methodology is proposed. The DGPM provides pseudo-GPS position information for INS during GPS outages. The mathematical model of integrated navigation system is established, including INS error equations, Kalman filter and DGPM. The model works in the update mode when there is no GPS failure, whereas it switches to the prediction mode in case of GPS outages. To verify the feasibility and effectiveness of the proposed methodology, real road test is performed. The comparison results show that accuracy of longitude and latitude are improved by more than 80% and 70%, respectively. The DGPM can effectively provide position corrections for standalone INS during GPS outages.

국제해사기구(International Maritime Organization, IMO)에서는 선박의 해상 충돌을 방지하기 위하여 SOLAS의 요구조건에 따라 모든 선박에 대하여 선박자동식별장치(Automatic Identification System, AIS)를 의무적으로 장착하도록 권고하고 있다. AIS를 사용하면 타선에 대한 제원 및 항행정보의 획득이 가능하여 충돌방지뿐만 아니라 광역관제, 조난 선박의 탐색구조 등 안전관리에도 활용이 가능하다. 본 논문에서는 AIS 장비를 사용하여 ADS-B (Automatic Dependent Surveillance - Broadcast) 개념 기반의 항공감시용 송수신기를 구현한 결과에 대하여 살펴보도록 한다. 기본적으로 AIS는 해상에서 사용되기 때문에 고도 정보를 사용하지 않지만, AIS 장비 내부에 사용하는 GPS (Global Positioning System) 칩셋에서 제공하는 고도 정보의 추출이 가능하다. 고도 정보를 포함한 감시 메시지 형식을 정의하고, SOTDMA (Self-Organizing Time Division Multiple Access) 방식을 개선하여 ADS-B 송수신기를 구현하였다. 적용 결과를 확인하기 위하여 지상시험 및 비행시험을 수행하였다. International Maritime Organization (IMO) recommends the installation of an Automatic Identification System (AIS) according to requirements by SOLAS to avoid maritime collision. AIS provides traffic information of other ships that may be used for maritime traffic control, SAR (Search and Rescue) and collision avoidance to apply safety management. In this paper, preliminary results to implement an aeronautical surveillance transceiver using AIS transceiver based on ADS-B concepts are described. Although altitude information is not required for AIS since the AIS is operated at MSL (Mean Sea Level), altitude information can be extracted by a GPS (Global Positioning System) chip-set in the AIS transceiver. ADS-B transceiver is implemented by defining a surveillance message format including the altitude information and modifying SOTDMA (Self-Organizing Time Division Multiple Access) protocol. Ground tests and flight tests are performed to validate the implementation results.

In modern navigation applications, Inertial Navigation System (INS) is attractive for integrating with Global Positioning System (GPS). Although traditional GPS/INS integrated systems can bridge the GPS gaps, the navigation capability is strongly dependent on the performances of standalone INS. As an important complementary, a new terrestrial, Radio-Frequency (RF) based, distance measurement technology “Locata” can provide continuously time-synchronised ranging signals even in many GPS challenged environments e.g. poor satellite geometry, signal blockage in suburban, tunnels, high rise buildings canyon. This paper investigates the integration of Locata, GPS, and INS with a focus on the loosely-coupled triple integration algorithm. Firstly, the Conventional Kalman Filtering (CKF) based triple integration of Locata/GPS/INS architecture is described and briefly discussed. Secondly, to overcome the pitfalls of conventional Locata/GPS/INS integration algorithm, an Adaptive Dual Kalman Filtering (ADKF) algorithm is proposed and developed in three stages: (1) To enhance the reliability of position and velocity (PV) quantities generated from Locata/GPS integrated sensors, the 1st KF is additionally constructed to reliably estimate the PV solution before fusing INS sensor. (2) Combining the 15-state INS error model and the measurements which are the differences between PV solution from the 1st KF and INS sensor, the 2nd KF is subsequently employed to correct the INS navigation errors. (3) The final integration solution is reversely used as the feedback for precisely estimating the stochastic model (i.e. variance of dynamic and observation model noise) of the 1st KF. Finally, the real flight experiment is carried out to demonstrate the efficiency and validity of Locata/GPS/INS integration algorithms. The results show that: (1) Conventional GPS/INS integration performs well but its accuracy dramatically decreases when GPS signals are unavailable for a short period. (2) Augmented by Locata, GPS/INS produces tolerable results in whole experiment even without aiding of GPS. (3) By additionally operation of the 1st KF and adaptively estimating its stochastic model with feedback of integration solution, ADKF achieves more accurate and reliable position, velocity and attitude (PVA) solution than CKF.KeywordsINS errorLocata/GPS/INS integrationAdaptive Kalman filteringStochastic model

In present days, real time navigation depends on Kalman filter to fuse data from Global Positioning System (GPS) and Inertial Navigation System (INS). But there exist drawbacks like long design time, requirement of prior knowledge in fusing data from GPS and INS using Kalman Filter. In order to overcome the drawbacks of Kalman filter, GPS and INS integration are done using Artificial Neural Networks (ANN) like FFNNs. But still there exists certain drawbacks like less accuracy in FFNNs for GPS and INS data integration. Therefore, this paper introduces Higher Order Neural Networks (HONNs) approach for INS and GPS data integration to overcome the drawback. A higher order feed forward neural network architecture with optimum number of nodes is used. In the given architectures, the replacement of summation at each node by multiplication results in more powerful mapping, because of its capability of processing higher-order information from training data. Performance comparison of HONNs with FFNNs shows that the proposed architecture provides satisfactory results in terms of error rate and number of epochs. The HONNs like Multiplicative Neural Network (MNN) module and Sigma-Pi-ANN module and Traditional Feed Forward Neural Networks(FFNNs) like Radial Basis Function Neural Network (RBF) module and Back propagation Neural Network (BPN) module are trained to predict the INS position error and provide accurate position of the moving aircrafts. Sigma-Pi ANN is found to be the best in terms of accuracy, number of epochs and execution time among the two HONNs.

Global Positioning System and Inertial Navigation System can be used to determine position and velocity. A Global Positioning System module is able to accurately determine position without sensor drift, but its usage is limited in heavily urbanized environments and heavy vegetation. While high-cost tactical-grade Inertial Navigation System can determine position accurately, low-cost micro-electro-mechanical system Inertial Navigation System sensors are plagued by significant errors. Global Positioning System is coupled with Inertial Navigation System to correct the errors, while Inertial Navigation System itself can be used to provide navigation solution during a Global Positioning System outage. Data from Global Positioning System and Inertial Navigation System can be integrated by extensive Kalman filtering, using loosely coupled integration architecture to provide navigation solutions. In this study, real-time low-cost loosely coupled micro-electro-mechanical system Inertial Navigation System/Global Positioning System sensors have been used for pedestrian navigation. Trial runs of Global Positioning System outages have been conducted to determine the accuracy of the system described. The micro-electro-mechanical system Inertial Navigation System/Global Positioning System can successfully project a trajectory during a Global Positioning System outage and produces a root mean square error of 9.35 m in latitude direction and 10.8 m in longitude direction. This technology is very suitable for visually impaired pedestrians.

With the development of the intelligent vehicle navigation systems and vehicle networking systems, vehicle positioning and state estimation become very important. This paper investigates the integration of Global Positioning System (GPS), Inertial Navigation System (INS) and Dead Reckoning based on Anti-lock Braking System (ABSDR) positioning based on Kalman filter, and aims at achieving full-time vehicle positioning. In this paper, we use Kalman Filter to estimate INS positioning error based on GPS or ABSDR solution, take the INS solution compensated with estimated error as system output. This paper introduces INS positioning algorithm, dead reckoning method based on the ABS wheel speed and Kalman filtering method. After that, simulation analysis and experimental verification of the positioning algorithm are carried out. The results show that the system output high-precision high-frequency positioning information when GPS is available, and when GPS is unavailable, INS positioning errors can be compensated well with ABSDR introduced, achieving the full-time positioning of the vehicle.

An ATC (Air Traffic Control) experiment using ETS-V (Engineering Test Satellite V) was conducted. In the experiment, the position of aircraft was measured by CIS (Cooperative Independent Surveillance) and ADS (Automatic Dependent Surveillance). Data communication and voice communication using NBFM and LPC between ground and air was conducted. The result shows that the positioning accuracy of CIS and ADS is enough to minimize present oceanic separation of aircraft. More than 95% of the controllers participated in the experiment evaluated the quality of voice communication using NBFM as bettr than that of present HF communication. Also 81% of them prefer LPC to HF. An error rate of received characters on aircraft through data communication is less than 2×10^<-4>, which shows that the satellite data link has the possibility of improving oceanic ATC capability.

When the vehicle passes through the tunnel or bridge, Global Positioning System (GPS) will fail due to signal occlusion or interference. In order to improve the positioning accuracy of vehicle-carried inertial navigation system (INS) in GPS rejection environment, the lateral constraint method based on vehicle-carried INS is studied in this article. According to the characteristics of lateral constraint, in the area with good GPS signal, installation errors between the INS body coordinate frame and the vehicle coordinate frame are estimated at the same time of integrated navigation. In the GPS-denied area, the estimated installation errors are compensated to calculate the reference velocity at first. After compensation, the difference between INS velocity and reference velocity is taken as the measurement. Finally, the Kalman filter model is established to correct the inertial navigation position. In addition, the simulation and experimental verification of the proposed lateral constraint method are carried out. The results show that the INS positioning accuracy can be improved above 50% by using the lateral constraint method in GPS-denied environment, which verifies the effectiveness of the proposed lateral constraint method.

Although a USV navigation system is an important application of unmanned systems, combining Inertial Navigation System (INS) with Global Positioning System (GPS) can provide reliable and continuous solutions of positioning and navigation based on its several advantages; the random error characteristics of INS and the instability derived from the GPS signal blockage represent a potential threat to the INS/GPS integration of USV. Under this background, a composition framework based on nonlinear generalization capability of support vector machines (SVM) and multi-resolution ability of wavelet transform is used to solve the difficulty that the INS suffers from the interference of stochastic errors, and the dynamic information of the USV is not influenced. An innovative fuzzy broad learning structure based on the broad learning (BL) method is utilized in the INS/GPS integration of USV, in which the navigation information of INS and GPS are deemed as the input of the Fuzzy Broad Learning System (FBLS) to train the network, and then the trained network of FBLS and navigation information of INS are applied for estimating the optimal navigation solution during the GPS signal blockage. Based on the USV platform, a sea trial was carried out to confirm the validity and feasibility of the proposed method by comparing with existing algorithms for INS/GPS integration. The experimental results show that the proposed approach could achieve the better denoising effect from random errors of INS and provide high-accuracy navigation solutions during GPS signal blockage.

The Global Navigation Satellite Systems (GNSS) such as Global Positioning System (GPS) are the prime land and autonomous vehicles navigation information source. However, urban canyons high rise buildings block the GPS signal. Therefore, the Inertial Navigation System (INS) or the Reduced Inertial Sensor System (RISS) are utilized as an alternative source of the navigation information. The RISS is able to produce a full navigation solution using less number of sensors and calculations. Dramatically, the RISS solution drifts over time as the INS system. The integration between the GPS and the RISS is mitigating each system drawbacks. However, during the GPS outage periods, the navigation system is only depending on the inertial measurements. The RISS is utilizing the odometer/speedometer to measure the vehicle's forward speed. Many types of error affect the odometer measurements. These errors are either due to the vehicle's specifications as the differences in wheel diameters, and/or inefficient wheelbase or the road nature as wheel slips, uneven road surfaces, and/or skidding. In this paper, a Radar-based RISS system is introduced to take place the traditional RISS or INS systems. The radar unit is an essential part of the adaptive cruise control (ACC) system. Furthermore, it is capable of measuring the relative velocity and distance between the carrying and the in front vehicle to reduce collisions and increase the safety of driving. In addition, the radar measurements are not affected by the error sources that affect the odometer. The idea is to invest the ground reflection and derive the forward speed of the onboard vehicle as the ACC keep a safe distance between the cars. A novel method based on extracting the ground reflection features is introduced to obtain the onboard vehicle's speed. The obtained speed is utilized with two accelerometers and one vertical gyroscope to produce the Radar-based RISS system. The proposed system has been integrated with the GPS producing a Radar-based RISS/GPS integrated navigation system. The system has been tested on a real road trajectory in a downtown area and involved several GPS signal outages. The results show the significant capabilities of the proposed system in keeping the navigation solution drift to a minimum especially when the GPS signal is in outage compared with the traditional RISS/GPS system.

: The HAAFT program was conducted to obtain high altitude radar altimeter data that could be used in a feasibility assessment of High Altitude TERCOM (HATCOM). The HAAFT program involved collecting altimeter data over seven scenes with terrain altitude standard deviations ranging from 25 to 320 feet located in the vicinity of Edward AFB and Point Mugu, California. Two pulse and one FM/CW altimeters were operated simultaneously at nominal altitudes of 5, 20, 30, 45, and 65 kft, AGL. The aircraft three-dimensional position was measured and provided by the AN/FPS-16 radar sets at Edwards AFB and Pt. Mugu. The tracking data were later merged with the data recorded in the aircraft and stored on computer tapes. An inertial navigator was included in the flight test equipment to provide aircraft attitude, velocity, and position data. In addition to the normal altitude measurement data provided by the altimeters, the pulse return waveform for the pulse altimeters and the prediscriminator waveform for the FM/CW altimeter were processed to be recorded on television video cassette recorders.

This chapter provides an overview of missile flight performance; the missile guidance, navigation, and flight control subsystems; as well as the state‐of‐the‐art of missile technology in these areas. Most of the material was derived from the textbook Tactical Missile Design, 2nd Edition and the technical course Tactical Missile Design – Integration ( http://genefleeman.home.mindspring.com/ ). Topics addressed in the missile flight performance section include drivers that limit the flight performance envelope; optimizing the parameters in the equations of motion for missile flight control effectiveness, maneuverability, speed, and range; predicting flight performance in cruise, descent, and boost; flight range comparisons of rocket, ramjet, and turbojet propulsion; and the required velocity for a homing intercept. Guidance topics include a comparison of active, semi‐active, and command guidance; line‐of‐sight rate requirement for proportional guidance; and the reduction in seeker range from atmospheric attenuation. Navigation topics include satellite Global Positioning System (GPS), inertial navigation system (INS), and data links. Flight control topics include types of flight control (tail, canard, wing, thrust vector, reaction jet) and flight control maneuver laws (skid‐to‐turn, bank‐to‐turn, rolling airframe). Finally, a discussion is provided of the role of missile system simulation in the technology development and design validation process.

Automatic Dependent Surveillance (ADS) is a system whereby aircraft automatically transmit, via data link, data derived from onboard navigation system such as INS. It is expected to improve the ATC surveillance capability in oceanic aerospace. The ADS Pacific Engineering Trials are being conducted to collect basic data for ADS. The trials were conducted using INMARSAT satellites and Japan Airlines' B747-400 aircraft with a low rate satellite data communication system on overseas flights in the NOPAC route. The results of the trials are shown as follows ; 1) The aircraft movement in the oceanic space can be monitored accurately. 2) The ratio between ADS request transmissions and ADS data receipts was 88%. 3) The transmission delay of most ADS data is less than 79 seconds. 4) The distance between ADS-and SSR-derived positions is less than 2.4 nautical miles after a long transpacific flight using INS.