A Low-Cost GPS&INS Integrated System Based on a FPGA Platform

Alex Garcia Quinchia1,3

1Dept. de Microelectrònica i Sistemes Electrònics Universitat Autònoma de Barcelona (UAB)

Barcelona, Spain Alex.Garcia.Quinchia@uab.es

Carles Ferrer1,2,3

2Institut de Microelectrònica de Barcelona (CNM, CSIC) 3Institut d'Estudis Espacials de Catalunya (IEEC)

Barcelona, Spain

Abstract—Nowadays is common to find different applications where GPS and INS integration is achieved, such as: identifying track defects, guidance, navigation and control of Unmanned Air Vehicles (UAV), geo-referencing, etc. The combination between these navigation instruments provides higher accuracy in the positioning information than their stand-alone operation, due to the fact that it takes advantages of both instruments, but although there is a wide study of algorithms to evaluate GPS/INS fusion not many studies are undertaken to carry it out using small size, flexible and low cost platforms, which is a challenge in the next generation of navigation systems. Therefore this paper describes the development of a low-cost Loosely Coupled GPS/INS integration with a novel modular platform based on FPGA that presents small size, low power consumption architecture, it is also flexible and has a low risk system design methodology that can be useful in several applications. The platform is assessed by data coming from a flight simulator called FlightGear, this data is perturbed with noise, where has been considered information from real sensors. The platform also has a user friendly PDA-Web server interface to show the navigation solution in Google Maps.

Keywords-inertial navigation system (INS); global positioning system (GPS); field programmable gate array (FPGA); personal digital assistant (PDA); google maps

I. INTRODUCTION Currently many vehicles are equipped with Global

Positioning Systems (GPS) which can give an acceptable positioning. However, in applications where a high accuracy is necessary, the GPS is not enough because it is affected by several errors like multipath, atmospheric errors, clock drifts, jamming, which can compromise the integrity of the navigation. Even with the European Galileo System some errors will remain including signal blockage due to obstructions and low power of the signal [2]. On the other hand, low cost Inertial Navigation Systems (INS) can provide information about position, velocity and attitude using an inertial frame, giving major resolution in the positioning. Although the INS is not so much affected by the environment as the GPS, these systems present an error which increases in a short period of time. Since these instruments have a complementary nature, the GPS/INS integration provides a higher accuracy than their stand-alone operation. Recently, new strapdown systems have been built due to the advances in Fibre-Optic-Gyroscopes (FOG) and Micro Electrical

Mechanical Systems (MEMS). It has increased the demand of the low-cost INS, also the small size of these devices has expanded its use in several applications where GPS/INS are combined. Despite this, these low-cost inertial sensors are characterized by high noise and large uncertainties in the outputs such as bias, scale factor and non-orthogonality [12]. Accordingly, a low-cost INS has errors in position, velocity and attitude which grow rapidly. For this reason, the development of efficient algorithms to minimize this error and in addition, to do the fusion GPS/INS has been the subject of study in several research groups for many years. Among the methods used to accomplish this task it can be mentioned : Linearized Kalman Filter (LKF) [8], Extended Kalman Filter (EKF) [7], Unscented Kalman Filter (UKF) and Particle Filters [4][5], Artificial Neural Network (ANN) [6][15], Least Squares Support Vector Machine (LS-SVM) [3].

Thus, there is a wide study to evaluate algorithms that carry out GPS/INS fusion. In contrast, not many studies are undertaken to achieve the integration using compact and flexible platforms, with the exception of the systems that are offered by navigation companies which generally use high-cost INS. Furthermore, the trend in the navigation systems is to provide small size platforms. This means, systems on a single board and eventually onto a single chip [16][17]. Therefore, this paper aims to develop an embedded system that combines GPS/INS with a novel architecture based on FPGA (Field Programmable Gates Array) technology.

FPGAs are being used in a wide range of applications that require computation-intensive, due to the fact that they have different features such as embedded processors, DSP blocks, high speed serial communication and high density in gates. These can be programmed to achieve several applications with a high performance, taking advantage of their high grade of parallel processing and their capacity of run-time re-configurability. These devices have the benefits of the hardware such as speed and flexibility, apart from their simple design cycle. This means that if there is an error in the design, it just needs to modify the code and reprogram the FPGA, and does not have a high nonrecurring engineering (NRE) as in the case of an Application -Specific Integrated Circuit (ASIC).

Thereby FPGA is appropriated in this kind of systems not only for the computational cost involved in the strapdown process and the Kalman filter but also for its low-cost, small size, flexibility and low risk system design methodology. In

Support partially founded by Spanish Ministry of Science and Innovationunder DELPHIS project TEC 2009-09712.

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addition, the proposed system interacts with a Personal Digital Assistant (PDA) and a Web server to show the trajectory of the vehicle in Google Maps. This paper is organized as it follows. The first section presents a review of recent similar works. Second section gives a general description of the Loosely Coupled integration which is used in this work. The following sections explain each of the blocks that make up this navigation system. Subsequently, the architecture conducted in the FPGA is presented. Finally, the results show the performance of the GPS/INS integration on Google maps.

II. PREVIOUS WORK Recently studies have been conducted to carry out the GPS/INS integration using platforms such as DSPs (Digital Signal Processors) and FPGAs (Field Programmable Gates Arrays) [7][9][18].

References [7][9] present the integration using DSP and FPGA, the DSP is floating-point and has a good performance. This is suitable for algorithms that require high computational cost and accuracy. In this case the DSP is used to implement the Kalman filter and navigation equations. The FPGA is responsible for the acquisition, communication and control. Although these papers propose novel systems with a high accuracy combining FPGA and DSP, platform size may be reduce using only one device. At the time they were published one of them was under development and the other one had been tested using a spacecraft simulator called Dynamic Flight Controller (FDC).

Reference [18] the GPS/INS integration is done using an FPGA. The first one uses the soft-core processor Nios II in conjunction with eCos (Embedded Configurable Operating System), which is responsible for managing the user interface, the synchronization time, the INS and Kalman filter algorithm. They designed some components using the AHDL (Altera Hardware Description Language). This project shows good results and also proposes a compact platform. However, AHDL language limits the design because this is specifically for use with Altera, consequently it is not an IEEE standard as VHDL (Very High Speed Integrated Circuit Hardware Description Language). In addition, this work does not show many levels of parallel processing using the FPGA.

III. LOOSELY COUPLED GPS/INS INTEGRATION The block diagram of the GPS/INS integration that was

decided to carry out is shown in Fig. 1, it is well known as Loosely Coupled (LC). The inertial solution is combined with the GPS data, having as a result, the residual error of position and velocity ( ),n nR Vδ δ that fed to the Kalman Filter (KF).

There are two ways to implement this strategy, feed-forward and feed-back. The first one is used in systems that have a high-performance Inertial Measurement Unit (IMU), as it merges the GPS/INS information, but it has no control over the error that may occur in the IMU, so it has open-loop architecture. In this work, the feedback has been achieved because it includes a close-loop to correct the INS error (Fig.

1), thus it allows to limit the error of the inertial sensors, which is suitable for our system because it uses a low-cost Inertial Measurement Unit (IMU). Moreover, this strategy has a low computational load and takes advantage of the hardware in comparison with the tightly coupled strategy. Following is described each module of this scheme.

A. IMU and GPS

Regarding the GPS, we used the receiver module LS-40MM with sensitivity of 12 channel, fast acquisition 10< second hot start and 45< second cold start, it has an onboard patch antenna and the rate of update is 1 Hz, the position data is transmitted via UART [13].

The Atomic IMU from Sparkfun was chosen which has 6 degrees of freedom. This is equipped with three gyros LISY300AL single-axe 300 / seg± ° analog output. A single chip MMA7260Q three-axis low g acceleration sensor that allows different sensibility levels ( )1.5g,2g, 4g,6g . It

consumes 81.6 mW of power and can provide inertial data through a serial interface UART[31].

Since the Atomic IMU is a low-cost IMU, it is necessary to model the error of the sensors, which are included in the navigation filter. Thereby, the equations that represent the measurement of the accelerometers and gyros, take into account different sources of error. In this work the first-order Gauss-Markov process was used in INS filter to model sensor errors, due to the fact that it fits a large number of physical processes with reasonable accuracy, and it has a relatively simple mathematical description [4].

The outputs of the sensors error model are part of the Kalman Filter (KF) states, so the error states of the KF are augmented with , ,, , , , ,a g tob a tob g a gb b b b S Sδ δ δ δ , which are associated with the bias drift, the turn-on bias and the scale factor error for each sensor respectively. These values are in the close-loop (Fig. 1) to compensate the acceleration and angular velocity.

Once the inertial sensors error is compensated, the next stage is the Mechanization, which is described in the following section.

Fig.1. Loosely Coupled Integration.

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B. Mechanisation Regarding the Mechanization (Fig. 2), we focus on how the

measurement sensors are combined and processed to obtain navigation data (position, velocity and attitude) of the vehicle. A detailed description of the mechanisation can be found in [1][14].

The movement of the vehicle can be described by the equations that are shown in the Fig. 2; these equations give us position, velocity and attitude of the vehicle with respect to a local geodetic frame, which is North-East-Down (NED) in our case, so this scheme and the equations presented in [1][10] [30] were helpful to implement the Mechanization algorithm. This way we obtain the velocity, position and attitude of the vehicle in the navigation frame. The next step was defined the error state model and computing the Kalman Filter in order to combine the INS data with the GPS data. The error state model is described in [1][10] [29].

C. Kalman filter Kalman Filter is widely used in navigation systems, due to

its ability to integrate various sensors with different bandwidths and its capacity to minimize the navigation error.

In this case the state vector has 27 components that are feed-back, it was already shown in the Fig. 1. The 27 states of the Kalman Filter are:

, ,

Tn n na g tob a tob g a bx r v b b b b S Sδ δ ε δ δ⎡ ⎤= ⎣ ⎦ (1)

Where, nrδ is the position error vector , nvδ is the velocity error vector, nε is the attitude error vector, abδ

and gbδ are the bias drift, ,tob ab and ,tob gb are the turn-on

bias, aS and bS are the scale error vector ( a → accelerometer , g →gyro).

Regarding the measurement model, it can be expressed by the following equation:

1 1 1 1k k k kz H x η+ + + += + (2)

Where, 1kH + is the design matrix of the system at time

1kt + and 1kη + is the measurement noise at time 1kt + with a

covariance matrix 1kR + . Equations to implement the KF are described in [14][20].

IV. PROPOSED SYSTEM The proposed system is showed in Fig. 3. This presents the

Virtex-5 ML509 board, IMU, GPS, PDA and Web server. The first step is read the data coming from the IMU and GPS. There are two UART interfaces which carry out the data acquisition from the navigation instruments. Secondly, the data is processed with the INS and KF equations, which are created as a custom IPs and are used as a peripheral of the soft-core processor inside the FPGA, in this way the

application software is accelerated. Thirdly, after getting the navigation solution on the FPGA, the data is sent using another UART peripheral, where is connected a UART-Bluetooth converter (Bluemore200), this device is suitable for integration in microprocessor systems without operating system since it does not need driver to work [26], this device sends position data to the PDA (Dell Axim x51v). Subsequently, the data is received in a user interface which is developed to run under the operating system Windows Mobile 5.0, which is embedded in the Personal Digital Assistant (PDA). The user interface not only receives information from the FPGA but also is designed to send commands to the FPGA in order to have interaction with the Virtex-5 ML509 platform and at the same time with the vehicle. In addition, to visualize the vehicle trajectory clearly and taking advantage of the PDA which allows a WIFI connection, we decided that the user interface was connected via internet with a Web server to send position data and this way to show the vehicle path in Google Maps.

There are different options in the Web server for analisys and data management , for instance: the navigation solution is interpreted and manage to be plotted in Google Map, shows information position for each geographical point of the vehicle trajectory, velocity at every point of the path, database to search an display trajectories concrete from its name, it compares trajectories from the traditional GPS and the GPS/INS solution based on the FPGA, it can also send commands from the web page to the PDA and turn Virtex-5 ML509 platform, taking interaction with the vehicle.

Fig.3. Proposed System.

Fig.2. Mechanisation.

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Thus, this versatile interface provides a user friendly communication between FPGA, PDA and Web server [27][28].

V. IMPLEMENTATION To implement the system on the Virtex-5 ML509

development system, we used the ISE Design suite 12.1 and also the EDK (Embedded Development Kit) from Xilinx. The Microblaze processor was customized to run at 100 MHz , this soft-core processor is implemented using the logic primitives of the FPGA and this is part of the Xilinx embedded solutions. This Intellectual property (IP) core has a rich instruction set optimized for embedded applications, which gives complete flexibility to select the combination of peripherals, memories and interfaces, so this provides the

exact system that is necessary at the lowest cost possible [21][22][23]. Thereby, the architecture was customized with three UART peripherals, two of them to obtain data from the IMU and GPS (Fig. 4), the other one was used to communicate with the PDA though a UART-Bluetooth converter. The main components of the system (Mechanization and Kalman filter) were added as custom peripherals to the DOPB (Data On-chip Peripheral bus), this provides flexibility because once the module is created, it may be changed, modified or adapted to achieve different applications where GPS/INS integration is done. It means that we could add different estimators as a custom IPs to do the GPS/INS integration, as for instance: UKF, EKF, etc. This architecture (Fig. 4) also allows us to attach different sensors if it is necessary; we just need to create a new core IP depending on the sensor connection.

EDK was used to add custom IPs that were created in VHDL for the Mechanization and the Kalman filter, this approach offers efficiency for the matrix operations and the math calculations involved in this algorithms because its hardware description can be customized. In addition, it provides parallel processing which is one of the advantages when the FPGA is used. In this case, we used the quaternions to update the attitude, due to the lack of trigonometric functions, thus the singularities are avoided and the computational cost is reduced.

One the most important calculations in these modules were the matrix multiplication. So we took advantage of the DSP48E slices, which are Digital Signal Processing element that are present in the FPGA Virtex-5 XC5VLX110T. These slices provide flexibility, efficiency, reduce overall power consumption [24]. These DSP48E slices combine a signed multiplier 18-bit by 25-bit with a 48-bit adder and supports various functions such as wide-bus multiplexing, three input add, barrel shift, magnitude comparator, but in this case the most remarkable is the multiply accumulate (MACC), since matrix multiplication is to be accumulative multiplication of terms.

Having customized the architecture with the IPs (UARTs, Mechanisation, Kalman Filter), each of them was attached to the bus DOPB. Subsequently, the FPGA was configured with the bitstream containing the Microblaze hardware platform.

The on-chip dual-port block (BRAM) was initialized with a bootloader application, this was necessary to run the GPS/INS application from a external memory, which had enough space to store data, instruction, stack and heap. In this way, the bootloader accesses to a determined location in the serial flash and copies the GPS/INS application from flash to an external memory, this process is well described in [25]. When the FPGA is reset the bootloader jumps to the extern memory a run the navigation application. Finally, due to the fact that the Virtex-5 ML509 does not have a display to show the results of the system, within the research group there has been developed an interface FPGA, PDA and Web server. The data is sent from a UART-Bluetooth converter to a PDA and from this to a web server which plots the trajectory of the vehicle in Google maps.

VI. RESULTS The board used in this work is the Xilinx University

Program XUPV5, which has a FPGA Virtex-5 XC5VLX110T, the system is built around the Microblaze soft-core processor, the INS and KF equations are developed in VHDL language, so the Mechanization and the Kalman filter are created as custom IPs of the Microblaze, this feature give us flexibility because we can modify these custom IP’s or create a new one whether to improve the performance of the integration or to change the integration strategy.

In order to evaluate the system implemented in this development system, several flights were performed using data from a flight simulator framework called Flightgear (FG) and JSBsim, which is a Flight Simulator Model (FDM), that is the default FDM for FG, it can be used to model and simulate a small autonomous Unmanned Aerial vehicle (UAV). This software is mainly developed in C++, both of them (FG, FDM) are open source for use in research or academic environments which can model the flight dynamic of several aircrafts [10].

The data coming from the aircraft, such as acceleration ( , , )x y za a a , angular velocity ( , , )x y zω ω ω , position and

velocity ( , , , , , )n e dLat Lon Alt V V V were obtained from a the simulation via the generic protocol of FlightGear (FG),

Fig.4. GPS/INS integration with Microblaze processor.

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this protocol allowed us to keep the data from the flight simulator in a plain text file. In this case, the Cessna C172P airplane was used to test the system. The sampling frequency for the inertial sensors was 100Hz and the total time flight was 777 seconds. After getting different trajectories, the plain text file is read and then random noise and error to data sensors are introduced, taking into account the measurements that were made on the real devices. This perturbed data is downloaded into the FPGA where is processed with the algorithm developed.

The Euler Angles ( ), ,ψ θ φ are shown in the Fig. 5, this describes the dynamic of the aircraft in the body frame, the green line is the attitude of the INS-only, for this reason the angle pitch grows without control. The red line represents the reference attitude and the blue line is estimated with the INS/GPS solution. As we can see there are some peaks in the roll angle that appear when the aircraft turns rapidly, despite this kalman filter compensated the navigation system, trying to

follow the reference attitude, although in this cases the Unscented Kalman Filter (UKF) can handle large and small attitude errors seamlessly, however, cannot deal with complete uncertainties in the attitude because it works based upon averaging sigma points [4].

The yaw angle estimated is quite different from the reference yaw angle, so the attitude can be improved using a different filter, which can deal with this attitude error, as for instance: Extended Kalman Filter (EKF), Kalman Filter Smoother (KFS) or Unscented Kalman Filter (UKF), the last one its most remarkable advantage is that it can handle large and small attitude errors seamlessly, however, cannot deal with complete uncertainties in the attitude because it works

based upon averaging sigma points [4]. When the attitude error is small the EKF provides similar results to the UKF.

Position of the aircraft in the local navigation frame (NED) is shown in the Fig. 6, the blue line represents the estimated position with the Loosely Coupled strategy, the red line is the reference trajectory done by the Cessna C172P airplane, both of them are overlap and the green one is the INS-only.

A. GPS Outages In this part was performed INS prediction during GPS

signal outage, for this experiment a total of two periods of GPS blockage were simulated on the FPGA, each one of these periods last 20 seconds, it is showed in the Fig. 7 . In this figure we can see that when the aircraft presents a high

dynamic as in the stage 2, the integrity of the navigation system is compromised but when there is a low dynamic as in the stage 1, the estimated trajectory try to follows the reference trajectory with a better accuracy than in the stage 2. However, this is well known that the GPS/INS integration using a simple Loosely Coupled presents significant errors when there is GPS signal blockage. Therefore, now we are working in order to improve the performance of the system in this kind of environment.

Finally, the estimated trajectory of the aircraft is depicted in the web site (Fig.8) using Google maps.

VII. CONCLUSIONS In this work Loosely Coupled GPS/INS integration solution

based on FPGA is developed, using a Kalman Filter with 27 states that provides position, velocity, attitude, bias and scale

Fig.7. GPS Outages.

Fig.6. Position NED.

Fig.8 . Estimated trajectory of the aircraft in Google Maps.

Fig.5. Euler angles.

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factor correction. The performance of this approach is verified with data coming from a flight simulator framework.

A novel modular implementation based on FPGA has been proposed, with the Mechanisation and the Kalman filter blocks as custom IP’s that provide flexibility and compact size, it can be used in several applications where the GPS/INS integration is carried out. If it is necessary the platform allows to add more sensor or modify modules to the soft-core processor Microblaze in order to improve the performance of the system.

With the interaction between FPGA, PDA and Web-server more information could be introduced in the kalman filter, which would help to improve the performance, this can be achieved obtaining access to map information from Google Maps, and furthermore this information can be used in the alignment stage decreasing latency of this process.

Its re-configurability can be useful in cases where the algorithm changes, as for instance: in the alignment stage or when the vehicle dynamic is diverse.

The future work is to test the platform in a real environment. Time synchronization between GPS and INS, reduction to nine of the number of the states in the KF and evaluation of the performance either nonlinear or adaptive navigation filter on the FPGA will be considered.

ACKNOWLEDGMENT Thanks to Jesús Iglesias, Jose Verdugo and Carles Gili who

carried out work in the research group, mainly in the interface FPGA, PDA and Web server.

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