Time synchronization design for integrated positioning and georeferencing systems

W.D. Ding, J.L. Wang, P. Mumford, Y. Li, C. Rizos. Time Synchronization Design for Integrated Positioning and Georeferencing Systems Proceedings of SSC 2005 Spatial Intelligence, Innovation and Praxis: The national biennial Conference of the Spatial Sciences Institute, September, 2005. Melbourne: Spatial Sciences Institute. ISBN 0-9581366-2-9 TIME SYNCHRONIZATION DESIGN FOR INTEGRATED POSITIONING AND GEOREFERENCING SYSTEMS W.D. Ding, J.L. Wang, P. Mumford, Y. Li, and C. Rizos School of Surveying and SIS, the University of New South Wales, Sydney, Austrlia Weidong.ding@student.unsw.edu.au Key words: GPS, INS, integration, time synchronization ABSTRACT More and more surveying companies are embracing digital technologies such as CCD cameras and LiDAR for aerial photogrammetric mapping and imaging applications. In this trend, direct georeferencing plays a crucial role. The obvious benefits of direct georeferencing are reductions in the requirements for aerial triangulation and ground control points, both of which account for a significant cost in survey work. In the case of direct georeferencing, accuracy is of the greatest concern, since it has a decisive influence on the quality of the subsequent digital imaging products. The level of accuracy and reliability of direct georeferencing also influences the degree to which ground control points can be eliminated. This paper deals with the time synchronization issue for a direct georeferencing system based on integrated GPS/INS. Time synchronization is generally considered to be one of the most critical factors in order to achieve high accuracy. Fundamentally the issue is that the GPS and INS technologies were developed independently, hence their data refer to separate internal clocks as time references. The necessity for higher time synchronization accuracy is directly related to the increasing accuracy requirement for GPS/INS direct georeferencing systems addressing modern cutting-edge applications. This paper has analysed the time synchronization issues in GSP/INS integration. The impact of data synchronization error and transmission latency on Kalman filtering results has been investigated using experimental data sets. Optimal synchronization solutions have been proposed according to different integration scenarios. Although this study is based on a GPS/INS integrated platform, the principles are equally applicable to the cases when more imaging and navigation sensors are involved. BIOGRAPHY OF PRESENTER Weidong Ding is currently a Ph.D. student in the School of Surveying and Spatial Information Systems, the University of New South Wales, Australia. He received his B.E. in Electrical Engineering from Beijing Polytechnic University, China, and his M.E in Electrical Engineering from the University of New South Wales, Australia. His research is focussed on developing an integrated positioning and georeferencing platform for kinematic positioning, to address mobile mapping applications. Time Synchronization Design for Integrated Positioning and Georeferencing Systems INTRODUCTION More and more surveying companies are embracing digital technologies such as CCD cameras and LiDAR for mobile aerial or ground based photogrammetric mapping and imaging applications. Advanced digital technologies have provided a solution to the increasing demand for spatial information data in various applications ranging from surveying and mapping to real-time location-based services. It has also reduced the overall cost of collecting, producing and storing such data. Additionally, it brings advantages that conventional methods could not offer, such as 3D information from LiDAR. Direct georeferencing plays a crucial role in these developments. Direct georeferencing refers to the method of providing the solution of the exterior orientation parameters of position, velocity and attitude on-the-fly without the use of block adjustment procedures during post-processing [El-Sheimy, 2005]. The obvious benefit of direct georeferencing is a reduction in the requirements for aerial triangulation and ground control points, both of which account for a significant cost in survey work. Although methods like aerial triangulation have been advanced to automated aerial triangulation during the past decade, the processing still needs a large amount of intensive editing by highly skilled operators [Cramer et al., 2000]. In the case of direct georeferencing, accuracy is of the greatest concern, since it has a decisive influence on the quality of the subsequent digital imaging products. The level of accuracy and reliability of direct georeferencing influences the degree to which ground control points can be eliminated. Much research has been conducted in the surveying and mapping field to investigate the influences of direct georeferencing accuracy on the quality of final imaging products [Tachibana et al., 2004; Skaloud, 2002; Cramer et al., 2000; Kremer, 2002]. Direct georeferencing systems currently in use are mainly comprised of GPS and INS. Qualified delivery of exterior orientation parameters depends on successful integration of GPS and INS subsystems. Time synchronization is generally considered to be one of the critical factors in achieving high integration accuracy. Fundamentally the issue is that the GPS and INS technologies were developed independently, hence their data refer to separate internal clocks as time standards. This non-coordination results in the problem of time tagging bias, asynchronous sampling instants and different sampling rates between GPS and INS. Early studies of time synchronization issues include Bar-Itzhack’s [1984] observation of enigma bias during INS transfer alignment. Knight [1996] described an accurate tagging of INS raw measurements with GPS time as probably the single most critical element of successful GPS/INS tight coupling. Grejner-Brzezinska [2004] shows that time synchronization is a factor that is crucial for achieving high accuracy positioning based on multi-sensor integration. Li [2004] has successfully designed a cost-effective experimental device to mitigate INS data transmission uncertainty. Due to its critical role in GPS/INS integration, a further comprehensive and systematic study of time synchronization is needed to identify working mechanisms and to develop methods to mitigate time synchronization errors. This paper attempts to provide some analysis on time synchronization error propagation rules and to provide useful information for GPS/INS integration platform design. In this paper, the factors influencing time synchronization accuracy are discussed. Then the tight coupling filter model used in data processing is described, since specific discussion on time synchronization accuracy is closely related to the filtering model used. The impact of data synchronization error and transmission latency on Kalman filtering results is analysed using experimental data sets. Finally features and critical defining factors of reaching time synchronization under different scenarios are discussed in detail, and some optimal solutions are proposed. FACTORS INFLUENCING SYNCHRONIZATION To demonstrate the time latencies in the GPS/INS integration context, a typical loosely coupled model is illustrated in Figure 1. Time Synchronization Design for Integrated Positioning and Georeferencing Systems GPS time t Integration Platform GPS receiver t gps ? t ? e gpsbias tins ? t free INS IMU Sensors Hardwiring 1PPS GPS 1PPS capture time RS232 Message GPS Message capture time Hardwiring 1PPS INS 1PPS capture time RS232 Message INS Message capture time t t?? eegpsbias ?? ee3 ?e 7eof5 loosely 1 ?gpsbias Fig. 1 Clock bias and transmissionttins latency coupled mode ins A typical loosely coupled GPS/INS integration model is comprised of three functional subsystems, i.e. GPS receiver, INS, and integration platform. All of the three subsystems need to have internal clocks for proper operations. The GPS receiver, due to the nature of its design, has the possibility to link its internal clock time to GPS Time, which is subsequently linked to UTC Time. Although there is a time difference between UTC Time and GPS Time, it is trivial and not relevant to the issue of time synchronization accuracy in GPS/INS integration. So satellite time is considered as the real time reference. Not every GPS receiver synchronizes its internal clock to satellite time all the time. It depends ??t ?t internal t? ?t?gpsbias e???TeT2gpsmess on individual receiver design. Technically speaking, a GPS receiver can synchronize its to time tttins e 5?7 ?e?1?e?e6eclock T gpassatellite gpsmess gps1 pps 1 pps ? tins 1?pps 1 pps insmess insgpsbias 81 4? Tins insmess (GPS Time) to within 50ns accuracy. In contrast, INS works rather independently, and has no link to real time references but is operating in a free-run mode. This feature fundamentally creates the time synchronization problem for GPS/INS integration, because the data measured by INS cannot match perfectly to GPS data by just using time tags as it would be possible in other instrumentation systems. The only way the integration algorithm can do it probably is to align GPS and INS data according to the instant when they are processed by the integration platform. Transmission and processing latency add to time synchronization ambiguity. Since in most cases GPS and INS data are transmitted using a serial communication link like RS-232(EIA-232), RS-422(EIA-422), time delay is unavoidable especially when a buffering technique is used and/or the communication load is heavy. Data losses may happen due to overflow when transmission load is heavy, data distortion caused by ambient interferences, and so on. Then the whole correspondence relationship of the data is disrupted. Furthermore, running an integration algorithm like a Kalman filter is computationally intensive. Data arriving at the processing platform is usually put into a queue waiting for unpacking and error checking, before they are properly time tagged by the processing platform using internal clock time. The impact of the above time delay and uncertainty can be approximately illustrated using the loosely coupled model. In a general sense, the common method of constructing integration algorithms is based on differencing GPS and INS measurements to form error propagation rules from one epoch to the next. The navigation parameters and INS modelling parameters are estimated using data fusion techniques like the Kalman filtering. In such a case, the dynamic equation has the general form as follows: eAewzHev=+=+& (1) Time Synchronization Design for Integrated Positioning and Georeferencing Systems Where e is the state vector of errors A is the dynamic matrix H is the observation matrix ()()()GPSINSztXtXt=− z is the observation vector which can be calculated by ,wv are the noises that are assumed to be white and Gaussian. Their covariance matrices are Q and R respectively, see Grewal et al. (1993) for more details about Q and R. Time synchronization biases between GPS and INS data would introduce additional measuring errors: ()()()GPSINSztXtXtt=−+Δ After making a Tailor expansion of ()INSXtt+Δ , the observation equation can be expressed as zHevζ=++ Where ζ (2) (3) is the additional residual caused by different data sampling time between the GPS and INS measurements. ζ When it is not explicitly addressed, is often implicitly treated as a part of measuring noise in the modelling process. ζ However, is not guaranteed to be white and Gaussian as required by the Kalman filter. In this case, additional estimation error is introduced into the filtering results. INTEGRATION FILTER When Kalman filter is used for GPS/INS integration, it is often classified as either direct or indirect filtering [Hwang, 2004]. In direct filtering, total states such as position and velocity are chosen as state variables of the dynamic equations, and direct measurements from GPS and the INS outputs are used as observables. Such integration filtering architecture is mainly used in submarine applications and GPS stand-alone situations. The widely accepted integration model in surveying for georeferencing is indirect filtering. Errors of position, velocity, acceleration and parameter error of INS modelling are treated as state variables. The error states can be fed forward and back to compensate sensor errors and to correct the navigation outputs. In such a context, INS psi-angle error model or its equivalence is often used in the GPS/INS tight coupling model [BarItzhack, 1977; Bar-Itzhack et al.; 1988, Grejner-Brzezinska, 2004; Lee et al., 2001]: ()ieineninrä δωωδδψδδωδδψωδψε=−+×−×++∇=−×+=−×+vvfgrv&&& (4) δv, ∇ δr and δψ are the velocity, position, and attitude error vectors respectively is the accelerometer error vector δg is the error in the computed gravity vector ε is the gyro drift vector A twenty four state Kalman filter is used for data fusion and error estimation, which includes nine navigation solution errors of position, velocity, and attitude in three dimensions, six accelerometer error modelling parameters (bias and scale factors of each axes), three gyro drifts, three gravity uncertainty errors, and three lever arm values. Refer to equation (5) for details. [,,,,,,,,][,,,,,][,,,,,][,,][,,]TNavNEDNEDNEDTAccbxbybzfxfyfzTGyrobxbybzfxfyfzTGravNEDTAntbxbybzrrrvvvgggLLLδδδδδδδψδψδψεεεεεεδδδδδδ==∇∇∇∇∇∇ (5) Time Synchronization Design for Integrated Positioning and Georeferencing Systems GPS measurements in the Kalman filter consist of double-differenced carrier phase observations: CPH GPS Base CPH GPS Rover DD computation Fixing ambiguity DD error KF Predicted DD ?V ? ? IMU Corrections INS navigation solution Fig. 2 Tight coupling Kalman filter DATA ANALYSIS The AIMSTM software package was used for GPS/INS integration processing. It was developed by the Center for Mapping at the Ohio State University (OSU) for large scale mapping and precise positioning applications (GrejnerBrzezinska, 2004; Da, 1997). To investigate the impact of time difference on the positioning errors, two sets of ground based experimental data were processed using AIMS. The first set of data was obtained from the Center for Mapping,OSU, while the second set was collected at the University of New South Wales (UNSW). The hardware configuration for the system used by OSU for collecting the data consisted of two GPS receivers, and one Litton LN-100 strapdown INS. The configuration used by UNSW consisted of two Leica receivers and one DQI-NP INS. (DQI-NP is used purely as an INS, even though it has a GPS receiver integrated with it.) Processing results of the original OSU data are illustrated by Figure 3, which shows the 3D trajectory of the system. e e r g e d n i 225 220 t h g 215 i e 210 H -83.052 -83.047 -83.042 Longitude in degree -83.038 39.997 39.998 39.999 40 40.001 40.002 40.003 40.004 Latitude in degree Fig. 3 Output trajectory when OSU original data is processed Since an accurate reference trajectory can not be directly measured, first the ambiguity-resolved segments are used for comparison in order to assure the accuracy of reference at the centimetre level. Figure 4 shows the positioning error in the local North-East-Down frame (NED) when compared with the GPS-only solution. The standard deviation of the positioning errors is within 2cm. These results conform with the reported test accuracy from OSU [Grejner-Brzezinska, 2004; Da, 1997]. Time Synchronization Design for Integrated Positioning and Georeferencing Systems ) m ( h t r o N ) m ( t s a E ) m ( t h g i e H 0.2 ) m ( mean 0.017 std 0.015 h t r o N 0 -0.2 4.165 4.17 4.175 4.18 4.185 4.19 4.195 x 10 0.2 ) m ( t s a E 0 4.17 4.175 4.18 4.185 4.19 4.195 x 10 0.2 5 mean 0.005 std 0.017 4.17 4.175 4.18 4.185 4.19 ) m ( t h g i e H 0 -0.2 4.165 4.195 x 10 GPS second mean 0.026 std 0.019 0 -0.2 4.165 4.17 4.175 4.18 4.185 4.19 4.195 5 mean -0.016 std 0.017 -0.2 4.165 0.2 x 10 0.2 5 mean -0.026 std 0.021 0 -0.2 4.165 4.17 4.175 4.18 4.185 4.19 4.195 x 10 0.2 5 mean -0.005 std 0.026 0 -0.2 4.165 4.17 4.175 4.18 4.185 GPS second 5 4.19 4.195 x 10 5 Fig. 4 Positioning error compared with GPS-only results (only the part Fig. 5 Positioning error compared with GPS-only results when time when ambiguities are resolved) delay is 10ms Then time latency was added to the time tags of the INS data in order to simulate transmission delay. The resulting INS data were re-processed following the same integration procedure as before. As expected, an increase in positioning errors has been observed in all the test results, with different time delays of INS data. To demonstrate, Figure 5 shows the situation when a 10ms delay was simulated. It can be seen that positioning error increased, but the change did not reach a significant level. 10ms delay only caused the standard deviation of positioning errors to increase to over 2cm. The means of the positioning errors were also increased to more than 2cm. As different increments of time delay were added to the time tags of the INS data, the positioning errors steadily increased as the time delay became larger. The same trend was obvious even when the magnitude of the time delay became negative, as for the case when INS data are transferred faster than GPS data (Figure 6). It can be concluded that time tag differences of the order of 10ms between GPS and INS data seem tolerable when only positioning error is considered, and when GPS phase ambiguities can be resolved. s a i b 0.35 r . o ct cc A a 0.3 r e 0.25 t e m n i f e l a c s 0.2 . c c A D T 0.15 S s a i b 0.1 0.05 0 -40 -20 0 20 40 60 80 Delay time in ms Fig. 6 STD of positioning errors versus time delay 100 3 x 10 -3 2 1 0 -20 -10 0 10 20 30 40 50 -10 0 10 20 30 40 50 -10 0 10 20 30 40 50 0.2 0.1 0 -20 0.01 o 0.005 r y G 0 -20 delay time in ms Fig. 7 Estimated INS sensor errors versus time delay In order to investigate the impact of time delay on the trajectory segments when GPS phase ambiguities cannot be resolved; INS errors estimated by the Kalman filter were further studied. In contrast to the segments when GPS phase ambiguities were resolved and where positioning error can be somehow bounded by the high precision GPS measurements, positioning accuracy for segments when the GPS phase ambiguities are unresolved is significantly more degraded. The estimated accelerometer biases, scale factors and gyro biases when time delay was added to INS data were compared with those when there was no time delay. Figure 7 illustrates the differences in the estimation of INS Time Synchronization Design for Integrated Positioning and Georeferencing Systems sensor measuring errors from the no-delay estimation versus having delays. The estimation when there was no delay was treated as reference, so difference is zero at delay time zero. It can be seen from Figure 7 that increasing time delay has caused the estimated INS errors to deviate quickly from the original estimation (when there is no time delay added). For gµexample, the initial estimation of accelerometer biasgin µ the Kalman filter was 3.0e-4 m/s/s which equals to about 30 . The technical specification of the INS states 25 for accelerometer bias. The 10ms delay has caused additional estimation error more than that amount. This degradation of estimation accuracy may explain the large error when the complete trajectory with 10ms time delay as shown in Figure 8 is compared with the original trajectory in Figure 3. r o r r e Integrated resolution e d u t ri t o ra rL e e e r g e d 225 n i 220 t h g 215 i e 210 H -83.052 -83.05 -83.048 40.004 -83.046 40.002 -83.044 40 -83.042 39.998 -83.04 Longitude in degree 39.996 e d u t i g n r o oL r r e t h g i e H 4 x 10 -6 2 0 -2 4.165 4.17 4.175 4.18 4.19 x 10 4 x 10 5 2 0 -2 4.165 4.17 4.175 4.18 4.185 4.19 x 10 5 0.2 0 -0.2 4.165 4.17 4.175 4.18 Latitude in degree Fig. 8 Output trajectory when 10ms delay is added to INS data 4.185 -6 4.185 4.19 x 10 5 Fig. 9 Error of trajectory with 10ms time delay when compared to original trajectory 10ms delay in the INS data has caused the integration accuracy to degraded from the centimetre level to the decimetre level when GPS phase ambiguities are not resolved in a short period of time, i.e. max. 111 seconds (Figure 9). When GPS phase ambiguity resolution is lost for a longer time, for example the last part of the trajectory has lost ambiguity resolution for about 20 minutes, the resulting trajectory diverged from the referenced one very quickly. (The divergent part is not drawn out in Figure 9 in order to show more details of the smaller errors.) It should be noted that the magnitude of the errors demonstrated here is only in a relative sense since the comparison is not based on the true trajectory. Correlation coefficients between velocity trajectories and positioning error trajectories were calculated at different delay times in order to evaluate the influence of vehicle speed on GPS/INS positioning accuracy. No obvious correlation was detected at any of the tested delay steps (Figure 10). SYNCHRONIZATION METHODS To solve the time synchronization problem systematically, three scenarios have been identified [Li, 2004] according to the nature of the GPS/INS configurations. Then solutions are proposed accordingly. In the following discussion, the GPS receiver is always considered as the time master which provides a 1PPS timing signal and serial timing messages. It is also assumed that the GPS-derived 1PPS indicates the physical time of the validation of the GPS timing messages. Time Synchronization Design for Integrated Positioning and Georeferencing Systems s t n e i c i f f e o c 0.1 Temprature Sensor 0.09 0.08 Gyros Multiplexer 0.07 n o i t a l e r r o C A/D Converter Data output Sample/ Hold 0.06 Accelerotmeters 0.05 0.04 0.03 Smapling control circuit GPS 1PPS signal 0.02 0.01 0 -40 -20 0 20 40 Delay time in ms 60 80 100 Fig. 11 Block diagram of IMU data sampling circuits [Ma et al., 2004] Fig. 10 Correlation coefficients between velocity and positioning error versus time delay Scenario one - synchronization at INS’s data sampling circuit In terms of accuracy, the best solution is to implement time synchronization in the data sampling circuits. It is also the common method in the instrumentation industry when high precision of synchronization of data sampling is required. A common reference sampling clock (or counter) is used to synchronize the sampling action, normally implemented in the A/D circuits. The sampling control circuit creates triggers according to sampling frequency required. Each trigger is used to start sample and hold circuits to take a snapshot of all the sensor measuring values at exactly the same time. Meantime an internal counter value is frozen as an indication of the sampling time. In the A/D output, the digital value is combined with the counter value to form a complete measurement with precise corresponding time. GPS 1PPS signal is introduced to sampling control circuits to control the generation of sampling pulse. Although high accuracy can be achieved using this method it is not an easy task to modify INS hardware circuits or to develop an INS from individual IMU sensors. Scenario two - synchronization using GPS and INS’s timing signals The characteristic of this scenario is that the INS works like a “black box”, but with pulses output indicating the physical time of validation of timing messages. 1e 5eThe relationship between GPS 3and e 7eINS clocks can then be traced. This is the case demonstrated in Figure 1, where , are 1PPS alignment errors, . , are timing message output delays, 2e 4e 6e 8e , , , are processing delays within the integration platform. Since the magnitudes of e1 and e5 are available from product technical specifications, and e2 and e6 should have the same magnitude depending on integration platform performance, we have 11()insinsppsgpsppsttTT≈+− (6) According to the correspondence between the INS timing pulse and time message, the relation between INS clock time and GPS Time can be found. Time tagging of INS data can then be translated into GPS Time. The above equation shows that the clock error and drift of the integration platform does not influence tins much, since its error is not cummulative. When the short term stability and accuracy of the clock oscillator is satisfactory, the GPS message and INS data can be well synchronized by checking the time difference between the GPS 1PPS time and the INS pulse time. A sufficiently accurate and stable digital counter can be used to calculate the time difference between the GPS 1PPS and INS pulses. Time Synchronization Design for Integrated Positioning and Georeferencing Systems Since sampling instants are not coordinated between GPS and INS, guaranteed accuracy depends on the INS sampling rate. When INS has a sampling rate of 100Hz, guaranteed synchronization accuracy is 0.005 seconds. This is one drawback when compared with method introduced in scenario one. Sometimes an interpolation method can be used to improve the “virtual” sampling rate of INS. Some GPS receivers provide a function called event time tagging. It is not suitable for synchronizing INS data because it is hard to match the INS data and the recorded pulses’ time tags during processing when the sampling rate is high. Scenario three - synchronization without INS’s timing signals Many INS or IMU products do not provide the timing pulses described in scenario two. In this case time synchronization has to be performed by analysing the digital signal features. Often IMU/INS units are using serial communication to output measuring data, most likely RS232 (EIA232), RS422 (EIA422), 1553B, etc. In these cases, factors influencing synchronization accuracy include sampling and processing delay in the IMU/INS, serial communication delay, buffering uncertainty, and time differences of the sampling instant. Measures can be taken to reduce transmission delay and uncertainty. The best is to find a time mark which can definitely indicate the beginning of the transmission, so the transmission time can be estimated and compensated for. For example, hardware handshaking signals for serial communication can be used for this purpose. Measures to prevent buffer overflow at the serial port include having a link speed high enough, and giving the service program higher priority. INS communication load is easy to estimate. Manufacturers may provide parameters concerning the internal sampling and processing delays of their INS. Otherwise the value has to be determined in a laboratory. The correlation method can be used to check the correspondence of trajectory features of the INS navigation output, and the GPS positioning measurements. The techniques are similar to those used in airborne vector gravimetry [Kennedy, 2002; Kwon, 2000]. Nevertheless, higher time synchronization accuracy cannot be expected in this case when compared with scenarios one and two. CONCLUSION This paper has reviewed the time synchronization issue in GPS and INS integration in the context of direct georeferencing applications. Factors influencing the accuracy have been identified and analysed. The impact of time synchronization error on GSP/INS positioning accuracy has been studied with the aid of experimental data sets. The test results have demonstrated that within the integration Kalman filter, the measuring errors caused by the time synchronization biases are not directly transferred into positioning errors. However, millimetre level synchronization accuracy is still necessary in order to calibrate the INS parameters properly. Three time synchronisation scenarios have been described. The feasibility of each of these scenarios has been discussed . It has been noted that high dynamics of the platform does not necessarily increase the impact of the time synchronization biases on positioning errors. 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