The seven-minute video explains in simple terms how important GPS has become to everyday life — for aircraft and ship navigation, global financial transactions, precision agriculture, weather forecasting, disaster relief, and, of course, smartphones.

The Leica media event at HXGN Live Conference in Las Vegas.

At a media event held during the HXGN Live Conference in Las Vegas today, Leica Geosystems introduced several new products. The highlight of the event was presentation of Leica Nova and the Leica Nova MS50 MultiStation.

The Leica Nova measurement solution is designed to provide users with a complete and seamless workflow, Leica Geosystems said. It integrates advanced measurement technology, modern and intuitive field software, intelligent office software, and Leica’s services to give users the ability to make faster, smarter decisions, across a range of applications. The flagship of the new solution is the Leica Nova MS50 MultiStation, which combines scanning, total station, imaging and GNSS positioning in one instrument to deliver fast and reliable results. 

Leica Nova uses advanced measuring technology that provides accuracy and quality, yet is easy to use for an unprecedented range of applications, Leica said. The Leica Nova solution covers the complete process from capturing and visualizing data, to creating intelligent deliverables that shape the world. Whether users measure objects on a construction site, quickly calculate volumes, monitor a dam or a bridge, capture an accident scene with digital imaging or scan a façade, Leica Nova integrates all these capabilities in one compact, flexible solution.

The Leica Nova MS50 MultiStation.

The Leica Nova MS50 MultiStation combines every significant measuring technology in one device for the first time, Leica said. Precision 3D scanning, extensive and precise total station capabilities, digital imagery and GNSS connectivity are brought together in the multistation. The new mergeTEC technology fuses hardware capabilities with comprehensive data-management solutions. Images are synchronized with scans, and scans are tied into total station measurements, making it easy to manage complex 3D data, Leica said.

The Leica Nova MS50 integrates 3D point cloud measurements into regular survey workflows. Users can collect and visualize their topographic survey data with detailed high-precision scans. They save time by computing results and verifying the integrity and completeness of data immediately in the field, avoiding costly reworking or returns. Ultimately, users benefit from better decisions with richer and more detailed data.

Also part of the innovative new Leica Nova solution are the Leica Nova TS50 high-precision total station and Nova TM50 monitoring station.

The Leica Nova TS50 high-precision total station has 0.5-inch angular accuracy and an optimum combination of angle and distance measuring. Integrated overview and telescope cameras with fatigue-reducing autofocus document the scene with high-quality photogrammetric images using the latest SmartWorx on-board software.

The Leica Nova MS50.

The user sees on the remote control display exactly what the Leica Nova TS50 sees, allowing for easy one-person data capture. The Leica Nova TS50 allows full control through “tap and turn” without the need to go to the instrument and back again. The 20-Hz live video stream can be combined with a graphical overlay for cost-effective recording with immediate clarification of ambiguous situations. Together with the benefits of GNSS connectivity, the Leica Nova TS50 offers complete versatility by delivering reliable results, Leica said.

For critical monitoring tasks, the new Leica Nova TM50 monitoring station provides the highest angle accuracy and offers short measurement cycles through high speed Piezo drives and a fast, highly accurate EDM. The Leica Nova TM50 includes 0.5″ angular accuracy and can measure up to 3’000 meters, which makes it the ideal sensor for monitoring tasks in mines, landslides, etc. For additional documentation, the monitoring sensor includes an overview camera and a telescope camera with 30x magnification and autofocus. The Leica Nova TM50 meets the challenge of 24-hour-a-day, 7-days-a-week monitoring applications. The Leica Nova TS50/TM50 instruments are IP65 rated and designed to withstand the roughest use in the most severe environments.

Leica GeoMoS 6.0 enhances conventional monitoring methods with automatic scanning of surfaces with the Leica Nova MS50 MultiStation.

The Leica software portfolio includes the following:

• With the new Leica Infinity software, users can seamlessly manage, visualize, process and georeference combined total station, imaging and scanning data from the Leica Nova MS50 MultiStation and other Leica Geosystems sensors.
• For AutoCAD users who have never before worked with rich point clouds or 3D datasets, the new Leica MultiWorx for AutoCAD plug-in software offers a simple way to integrate point cloud processing into existing workflows.
• Leica Cyclone and Leica GeoMoS as well as numerous other software packages from partners such as MicroSurvey and Listech can also be integrated into the workflow to meet specific needs.

Here is a video of the Leica Nova MS50.

The C-Tides suite combines the vertical accuracy of C-Nav’s GNSS Precise Point Positioning service with the latest advanced ocean and coastal tides models, the company said.

C-Tides Online features real-time filters and vessel dynamics, a choice of worldwide Mean Sea Surface or regional reference frame models, and tidal prediction for mission planning.

C-Tides Offline utilities include data smoothing and outlier rejection, harmonic analysis, Doodson X0 filter, and a LAT option.

“It’s been a privilege working with our academic partners to develop what is probably the worlds’ most advanced real-time GNSS tide solution,” said Russell Morton, C-Nav head of development.

C-Tides is a fully supported C-Nav utility. The results are suitable for combining with other suitably calibrated vertical components to achieve IHO SP44 Order 1 or better.

When a team of firefighters, first responders, or soldiers operates inside a building, in urban canyons, underground, in foliage, or under the forest canopy, the GPS-denied environment presents unique navigation challenges. An enhanced personal inertial navigation system (ePINS), based on a strapdown navigation solution using a mid-grade IMU and wavelet-based motion-classification algorithms, can track positions with errors of less than 2 percent of distance traveled in both indoor and outdoor environments.

By Yunqian Ma, Wayne Soehren, Wes Hawkinson, and Justin Syrstad

Numerous pedestrian navigation applications are currently available or proposed for development. Some of them include localization for coordinating firefighters, first responders, or soldiers. In these applications, the safety and efficiency of the entire team relies directly on the location and orientation of each team member. Operations in high signal interference areas such as cities, rugged terrain, forest, or indoor spaces deliver intermittent or no GPS signal. An alternative to GPS-based location is required.

In this article, we introduce an enhanced personal inertial navigation system (ePINS) solution specifically designed for environments where GPS is unavailable. ePINS combines an array of state-of-the-art sensors and fusion algorithms into a personal navigation system that provides accurate location information for pedestrian applications.

The ePINS concept.

The ePINS solution has the following benefits:

• Accurate positioning in GPS-denied environments;
• Small, lightweight unit can be easily carried by first responders, rescue workers, or soldiers;
• Ruggedized packaging to withstand difficult first responder and military environments.

Features of  the ePINS unit include:

• State-of-the-art micro-electromechanical systems (MEMS) gyros and accelerometers, barometric altitude sensor, and advanced navigation software;
• Advanced motion classification algorithms that accurately identify and measure user activity;
• Immunity to magnetic disturbances.

Related Work

In the field of personal navigation, it is common to find systems that rely on sensors that need infrastructure (for example, Wi-Fi positioning) or sensors that actively emit electro-magnetic radiation (such as Doppler radar). These requirements are major drawbacks for communities such as dismounted soldiers in hostile environments.

Other approaches exploit the so-called Zero-velocity update (ZUPT) mechanism, which resets the inertial measurement unit (IMU) velocity errors during the stationary phase of motion. However, implementation of such schemes relies on sensors embedded in footwear, which is not readily accepted in many user communities.

To address these drawbacks, Honeywell has been developing advanced aiding techniques for personal navigation that do not rely on infrastructure and compute a self-contained, relative-navigation solution based only on passive sensors. One technique that Honeywell has developed uses displacement estimation from human-motion models. This technology has been implemented in the ePINS prototype and shows promising performance.

The human-motion model uses IMU measurements as inputs and was developed to infer distance traveled. It generates a displacement estimate that is used as a measurement in the navigation filtering process. The first version of this model was matured under the DARPA individual Precision Inertial Navigation System (iPINS) program. The iPINS system used an IMU, GPS, barometer, and motion classification to estimate a person’s position in both indoor and outdoor environments. In this system, IMU signal characteristics (e.g., peaks and valleys in the accelerations induced by walking) were exploited to differentiate between walking and running. Honeywell recently expanded the human-motion model to identify more specific motion types using a new wavelet motion classification method.

System Description

Figure 1 displays the hardware architecture of the ePINS, a small battery-powered, highly integrated electronic system. The ePINS processing platform is an ARM11-based, i.MX31 system-on-module, paired with support electronics. In addition to the processing platform, the ePINS assembly includes a MEMS IMU, a barometric pressure sensor, a digital magnetometer, and a GPS receiver.

Figure 1. ePINS hardware architecture.

The MEMS IMU provides inertial measurements for strapdown navigation. The IMU’s small package size, light weight, low power consumption, and impressive performance make it attractive for use in the ePINS system. The device is less than 5 cubic inches and weighs less than 0.35 pounds. It consumes about 3 watts of power with a typical current draw of 600mA at 5V.

The ePINS software system is shown in Figure 2. The navigation software runs within Honeywell’s Embedded Computing Toolbox and Operating System (ECTOS IIc), which provides a layered, customizable, and reusable software architecture for implementing navigation, guidance, and control software. A Honeywell-developed simulation tool for offline analysis and development of ECTOS-based software was also used in ePINS development and testing.

Figure 2. ECTOS IIc hierarchical software structure.

The ePINS demonstration device can achieve path performance of less 2 percent distance traveled for walking motion after 1 hour of operation, independent of the magnetic environment. Current performance, packaging characteristics, and interfaces are summarized in Table 1.

Table 1. ePINS performance objectives and physical specifications.

Algorithm Description

Figure 3 depicts the overall sensor integration and data processing scheme used in the ePINS device.

Figure 3. Sensor integration using the ECTOS extended Kalman filter.

Extended Kalman Filter (EKF).  The EKF estimates the navigation and sensor errors and computes the resets applied to the strapdown navigation solution to increase its accuracy. Error models for the navigation sensors (IMU, barometric altimeter, magnetometer, GPS, and motion classification) are contained in the EKF. For the ePINS device, the virtual measurements from the step-length model and the strapdown navigation solution are fused by the EKF to assist in bounding the time dependent error growth of the strapdown navigator, which in turn helps maintain calibration of the inertial sensors. A key output of the EKF is the navigation confidence, which is an estimate of the accuracy of the navigation solution.

An important aspect of the EKF and step-length modeling is the residual test that the EKF supports. This test provides a reasonableness comparison between the step-length model estimate and the distance predicted by the strapdown navigation system. This capability significantly increases the robustness of the navigation solution, especially when the user is engaged in motions not recognized during motion classification.

Human-Motion Model. The human-motion model includes two components: wavelet motion classification and step-length model estimation. The wavelet motion classification identifies the type of motion the user is performing, and the step-length model acts as a virtual sensor that quantifies the motion as a distance-traveled estimate.

Wavelet Motion Classification. Human motions are very diverse and highly irregular. Determining what motion is being performed is a challenging problem of classification. Honeywell’s solution is based on wavelet transformation of IMU data. Predefined, or known, characteristics of a variety of motions (such as walking, running, crawling, etc.) are cataloged and stored to a device’s memory. Estimates of those same characteristics for a user are then computed in real time and compared to the catalog of stored information to find the best match.

Generating the catalog of stored information is an offline task that begins by “segmenting” recorded IMU time domain data into individual steps. An example of the output of the segmentation process is shown in Figure 4.

Figure 4. Segmentation of the IMU data using the y-axis accelerometer signal.

Figure 5 displays the segmentation results for two different walking styles (in red and blue) across approximately 15 example steps. As is evident from the graph, walking has characteristics that are common across users, for example, the sharp peaks in the z-axis acceleration caused by foot-ground impacts. Once the data has been segmented, a wavelet transformation on each data channel is performed. Wavelet transformation for many users over many different motion types takes place offline. Subsequently, a wavelet descriptor is built for each motion type based on the transformations into the wavelet domain. With this method, a wide variety of information (that is, descriptors) suitable for input to a classifier is captured about each motion. These descriptors are then cataloged and stored in memory on the ePINS device.

Figure 5. Sample steps for two subjects (red) and (blue).

Finally, for the online phase, the wavelet descriptor of the incoming IMU data is calculated by performing a wavelet transformation on each data channel. This descriptor is then compared to the pre-computed and stored descriptors to classify the motion. FIGURE 7 shows an example of the motion classifier output, where a running motion was used as an input. The classifier successfully determined the motion type (blue field), frequency and phase of the input motion, depicted by the tallest rectangle in the figure.

Figure 7. Classification results from a query of running at a certain frequency and phase (depicted by the dark sphere).

Step-Length Modeling. Once the current motion is identified, a step-length model specific to that motion is used to aid the navigation algorithms. The model for each motion type is obtained by first collecting data that measures step length and step frequency. From this data, the step-length models can be computed by performing a regression analysis of the step-length vs. step-frequency data. Since the step-length models act as a virtual sensor, the models must be as accurate as possible to achieve better system performance. To attain model accuracy, an accurate data collection method is needed.

For ePINS development, step-length models for multiple users have been identified from step-length and timing information using a precise GPS truth reference system. Step-length regression calculations then determine the step length as a function of step frequency (that is, inverse of the step time period).  An example of GPS truth data and the corresponding regression model are shown in FIGURE 6 for walking motions.

Figure 6. Step length versus frequency for the walking of subject.

Although basic step-length models are created offline, online calibration of the step-length model can be performed by the EKF if GPS is available during operation. Online calibration tends to increase the overall position accuracy, as variations in the step-length models are likely due to slight variations in biometric differences across humans, terrain features, and even mission plans and duration.

Heading Determination. Heading initialization is one of the key concerns during system start up. In its current operational use, the ePINS device may perform a dynamic or a static initialization of heading. The static method requires the user to survey the system’s initial heading to an accuracy value that is usually specified by mission performance objectives; the absolute position accuracy is dependent upon the accuracy of the initial heading.

The dynamic method is a general method for heading initialization; it is performed without input from the user, but is possible only when GPS is available. This method of heading initialization does not use any a priori information about heading and requires an EKF implementation with a large-azimuth error model. This method requires an additional period of time in which the heading error uncertainty converges.

User Interface. During a mission, the user can interact with the navigation system and monitor its output on a display. The current ePINS prototype offers two-way communication via a serial connection. The serial communication is made wireless by the addition of a Bluetooth interface. Users can use this link to monitor the status of the navigation solution and to send commands to the device.

Honeywell has developed an application for the Android platform for this purpose. One of the key features of the interface design is that the navigation system outputs data in a standard NEMA format. Thus, publically available Android applications, not just proprietary applications, can also receive and display the navigation solution output by the ePINS device.

Honeywell’s personal navigation application displays the user’s traveled trajectory in real-time. The application can be adapted to include building floor plans as well as other navigation information.

Results

The ePINS prototype has been evaluated both in simulations and indoor/outdoor experiments. The navigation results presented here were obtained in February 2012 at a Honeywell facility (FIGURE 8). First, the user completed the heading calibration, and then online step parameter estimation in the presence of GPS was performed. Once calibration and training was completed, the GPS was disabled to simulate a GPS-denied environment outdoors. The user than transitioned to indoors (with GPS still disabled), and walked a course inside that included walking up and down stairs (FIGURE 9) and ended in a conference room (FIGURE 10).

Figure 8. Course for the Honeywell facility demonstration.

Figure 9. The user walking up stairs.

Figure 10. The user at the end of the demo.

Over these conditions, the ePINS system performed robustly and within performance specifications. Live demonstrations and testing showing similar levels of performance were performed at the 2012 Joint Navigation Conference (JNC) and at military test sites in California and Indiana.

Summary

The technical approach of the ePINS solution to the problem of personnel navigation in GPS-denied environments is based on a strapdown navigation solution maintained using a mid-grade IMU and advanced motion-classification algorithms. We integrated an array of sensors and software into a system that provides accurate position information and is suitable for use by first responders, soldiers, and other personnel where GPS is unavailable. ePINS works well for a variety of pedestrian motion types, including walking, running, crawling, walking upstairs, walking downstairs, sidestepping, and walking backwards. The motion classification and modeling method is extensible to other motion types.

We tested the ePINS system in indoor and outdoor environments. FIGURE 11 depicts the future ePINS concept, and TABLE 2 presents its future physical characteristics.

Figure 11. Future ePINS concept and mounting position.

Table 2. Packaging characteristics of the future ePINS.

Acknowledgments

This article is based on a presentation made at ION GNSS 2012.

Manufacturers

The ePINS processing platform uses Honeywell Agile Navigation and Guidance Integrated Electronics support electronics. It includes a Honeywell HG1930 MEMS IMU, a Bosch Sensortec BMP085 barometric pressure sensor, a Honeywell HMC6343 digital magnetometer, and a NovAtel OEMStar GPS receiver.

Yunqian Ma is a principal scientist at Honeywell Aerospace. He received his Ph.D. degree in electrical engineering from the University of Minnesota, Twin Cities. He is currently the program manager of the GPS-denied navigation program and the next-generation personal navigation program.

Wayne Soehren is a senior technical manager at Honeywell Aerospace. He was the program manager for the development of Honeywell’s first MEMS-based GPS/INS, which developed the core capability now used in Honeywell’s IGS-2XX family of MEMS-based GPS/INS products. He holds an MSEE from the University of Minnesota.

Wes Hawkinson is an engineering fellow at Honeywell Aerospace. He holds a BSEE/CE from the University of Wisconsin–Madison.
Justin Syrstad is a guidance and navigation scientist. He received a master’s degree in aerospace engineering from the University of Minnesota.

Initially, wireless carriers deployed 4G/LTE solely for data use. Without VoLTE9-1-1 capabilities, carriers must process emergency calls over 3G networks (circuit-switched fallback), even in areas where LTE is deployed. However, with TCS’ VoLTE9-1-1 service, they can now process 911 calls in an all-LTE environment, enabling them to reclaim or reuse 3G spectrum.

“As carriers increasingly move toward LTE networks, the ability to handle 911 emergency communications is critical,” said Thomas Ginter of TCS. “By leveraging VoLTE9-1-1, network operators are helping to ensure subscribers receive the responsiveness they need in an emergency situation, while expanding coverage to areas where 3G coverage is lacking.”

TCS VoLTE9-1-1 features:

• Call routing to the PSAP: The TCS VoLTE9-1-1 service routes a 4G/LTE-originated 911 call using coarse location via the route determination function component.
• PSAP telecommunicators can call back if disconnected: The TCS VoLTE9-1-1 service remains fully backwards compatible, supporting necessary functions such as providing PSAPs with full 10-digit subscriber callback numbers.
• Re-bid by a PSAP for precise location after call routing: The location retrieval function allows a wireless carrier complete flexibility in choosing its underlying high-accuracy location technology and supports updated/precise position requests.
• Emergency voice call continuity for location service: Location continuity and location delivery to the PSAPs are supported in usage scenarios where the 911 call switches from 4G/LTE to 3G/2G networks.
• Expansion beyond voice: As wireless networks advance, multimedia objects such as text, audio and video can be transferred to a compatible termination point with LTE IP networks, for example, an NG ESINet and i3 PSAP. Leveraging an all-IP network makes it easier and more cost effective to interconnect services.
• Small cell support: The TCS VoLTE9-1-1 solution supports small cells, including femtocells, microcells, and picocells, which are now commonly used in dense urban, indoor areas and enterprise networks.

TCS supports half of all U.S. wireless E911 calls, serving more than 140 million wireless and IP-enabled devices.  The company holds more than 280 patents, 43 of which relate to public safety, and more than 360 pending worldwide.

MAX-M5Q: ideal positioning solution for industrial M2M and Russia’s ERA-GLONASS emergency call system.

Swiss u-blox introduces the surface-mount MAX-M5Q, a compact satellite positioning module that supports GPS and GLONASS, as well as Japanese QZSS satellite GNSS systems. High-performance GPS/GLONASS parallel operation is also supported to enhance positioning speed and accuracy.

Designed for use in rugged environments and wide temperature range, MAX-M5Q is intended for industrial machine-to-machine (M2M) applications as well as Russia’s ERA-GLONASS emergency call system. MAX-M5Q enhances positioning in poor GNSS satellite visibility conditions as well as in high latitude and polar regions, u-blox said.

“With parallel GPS/GLONASS operation, MAX-M5Q is able to track all 50 and more U.S. and Russian satellites to deliver incomparable speed, accuracy, and positional availability,” said Thomas Nigg, vice president of Product Marketing at u-blox, “Its compact size and high-reliability makes it an ideal positioning solution for mobile resource management and ERA-GLONASS emergency call applications.”

With dimensions of 9.7 x 10.1 x 2.5 mm, MAX-M5Q is the newest member of u-blox’ MAX GNSS LCC module series. Additional features include autonomous A-GPS that reduces warm start TTFF by as much as 90%, and an embedded data logger which can store location information to internal Flash memory for up to 16 hours at 15 second intervals.

Smith’s photograph was taken at 21:58:38 UTC (start) with a Canon EOS 450D Digital Rebel camera with an 18-55mm zoom lens. The camera settings were: focal length 55mm, aperture f/5.6, and an exposure of 8 seconds at an ISO value of 1600. Two images are shown below: the original, as obtained from the camera, and a greyscale image with edge enhancement.

The Centaur can be seen traveling left to right and starts its track as it crosses the constellation of Cygnus. There’s a slight wobble at the beginning as the shutter release was pressed. The glow at the bottom of the frame is from a streetlight. The elevation angle of the Centaur was approximately 12 degrees.

SVN66 will operate as PRN27 and it will eventually occupy the C-2 orbital slot, replacing SVN33/PRN03, a Block IIA satellite launched in 1996. SVN66 is currently in a drift orbit about 400 kilometers above the operational constellation. It should reach the C-2 slot within a few days from now. The satellite has already been added to the broadcast almanac although it has not yet started to transmit standard signals. It is currently marked as unhealthy in the almanac and will remain so, even after standard signals are switched on, until testing is completed sometime this summer.

Centaur upper stage with the payload still attached, original photo. Photo credit: Andy Smith

The same photo digitally enhanced:

Digitally enhanced photo. Photo credit: Andy Smith

Photo credit: Pat Corkery, United Launch Alliance.

A U.S. Air Force Global Positioning System satellite built by Boeing was successfully launched May 15. The fourth GPS IIF satellite, Space Vehicle Number (SVN) 66, was carried aboard a United Launch Alliance Atlas V Launch Vehicle at 5:38 p.m. EDT (21:38 UTC) May 15 from Cape Canaveral Air Force Station, Florida.

The new capabilities of the IIF satellites will provide greater navigational accuracy through improvements in atomic clock technology; a more robust signal for commercial aviation and safety-of-life applications, known as the new third civil signal (L5); and a 12-year design life providing long-term service. These upgrades improved anti-jam capabilities for the warfighter and improved security for military and civil users around the world, the Air Force said in a statement.

The Atlas rocket took off on schedule. The satellite was released from the Centaur upper stage at T+ 3 hours, 23 minutes and 52.8 seconds or about 01:02 UTC on May 16. Details on the Block IIF satellites and the Atlas rocket can be found here.

“I’m extremely pleased with today’s launch and delighted to be part of this mission that enhances our nation’s critical GPS capability. Thanks to the superb efforts of the of the 45th and 50th Space Wings, United Launch Alliance, our industry partners, the Atlas V and GPS IIF launch teams, the GPS IIF-4 mission was successfully carried out,” said Col. Bernie Gruber, director of the Space and Missile Systems Center’s Global Positioning Systems Directorate.

“The GPS constellation remains healthy and continues to meet and exceed the performance standards to which the satellites were built. Our goal is to deliver sustained, reliable GPS capabilities to America’s warfighters, our allies and civil users around the world, and this is done by maintaining GPS performance, fielding new capabilities and developing more robust modernized capabilities for the future,” said Colonel Gruber.

Here are videos of the launch:


Opening photo by Pat Corkery, United Launch Alliance.

Photos show the launch of the U.S. Air Force’s GPS IIF-4 satellite from the Kennedy Space Center and Cape Canaveral Air Force Station.

Sun Jiadong, chief designer of BDS and an academician of the Chinese Academy of Sciences, made his comments in an interview with The Beijing News, as reported by the Chinese government’s website.

He added that this compatibility is the only way to ensure the protection of national information. “Safety issues abound in economic areas,” said Sun. “Ordinary people may have few concerns about the security of information but it is of vital significance.”

The development of Beidou also largely depends on the government’s involvement. “Even though the enterprises spare no effort in developing the system, the products they make would not be available for mass production, which will in turn be reflected by the prices. The government has to promote the research and development of the system,” Sun said. Sun cited the governments of Beijing, Shanghai, and Guangzhou as examples of local governments that were effectively helping to develop the BDS.

The use of Beidou could go beyond basic navigation functions and extend to the civilian market. It would take longer for the BDS to be available for civilians, said Sun. The use of Beidou on mobile phones relies on the development of a small and power-efficient chip. Otherwise the phone cannot be used.

When asked about when and how the cost of developing the BDS will be recovered, Sun reiterated that Beidou was developed to ensure the security of national information, and not to make profits.

The Beidou global navigation system will be available by 2020 with the launching of more than 30 satellites.