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SAR-based Augmented Integrity Navigation Architecture SARINA project results presentation

Conference Paper · January 2012

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10 authors, including: Some of the authors of this publication are also working on these related projects: Detection and tracking of ground moving target View project Multitech SeCurity system for intercOnnected space control groUnd staTions-SCOUT project View project Mario Greco Leonardo spa

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SAR-based Augmented Integrity Navigation Architecture

SARINA project results presentation

• M. Greco1, S. Querry2,3, G. Pinelli1, K. Kulpa4, P. Samczynski4,
• D. Gromek4, A. Gromek4, M. Malanowski4, B. Querry2, A. Bonsignore1,

1)IDS – Ingegneria Dei Sistemi – S.p.A.

Pisa, Italy

2)PLV – Polyvionics,

Paris, France

3)LSIIT – University of Strasbourg,

Strasbourg, France

4)Institute of Electronic Systems

Warsaw University of Technology Warszawa, Poland

Abstract—This paper presents selected results obtain under SARINA project. SARINA is a SAR-based Augmented Integrity Navigation Architecture proposed by authors of this paper. The main goal of the SARINA project was designing and assessing a novel aircraft Inertial Navigation System (INS) for missile and UAV that will make use of features extracted from SAR/InSAR imagery and on-board terrain landmark database in order to ensure robustness against uncompensated IMU errors due to possible GPS lack of. Keywords-SAR, Synthetic Aperture Radar, InSAR, Interfereometric SAR, SARINA, INS, Inertial Navigation System, IMU, Inertial Meassurement Unit.

I.

INTRODUCTION

The modern navigation systems comprise of inertial navigation unit and global Positioning System (GPS). Cooperation of both systems provides reliable and accurate navigation data required in many surface and airborne missions [2], [4], [6]. The problem arises when GPS data is not available either because of failure of GPS receiver of the denial action. In that case the navigation can be based in classical approach only on inertial data. The inertial systems error increase with time (usually faster than square time function) and thus can be reliable only for short missions. The chip navigation inertial systems build using integrated accelerometers can provide adequate accuracy only within several minutes, while very expensive optical systems can be used within several hours (depending on required accuracy). The common approach to increase navigation system accuracy is to provide position/velocity corrections based on the other sensors. For long time the optical sensors (e.g. TV cameras) were used for this purpose, but the use of that sensor is limited by light condition, fog and clouds. The day/night 24/7 operation can be assured by using microwave imaging based on SAR (Synthetic Aperture Radar). This work was focused on building the mathematical models and the simulations of navigation system augmented by SAR vision. In the system navigation correction was based on comparing the SAR images with image data base. The SARINA (SAR-based augmented Integrity Navigation Architecture) demonstrator was aimed to provide the feasibility study of novel navigation system for two kinds of unmanned Platforms: Unmanned Aerial Vehicle (UAV) and Missile reaching TRL (Technological Readiness Level) 3 (out of 9) - analytical and experimental critical function and/or characteristic proof of concept. The main goals of SARINA System Simulator was to evaluate the feasibility to retrieve aircraft state variables by exploiting a novel INS based on novel Data Fusion Unit (DFU), able to deal with georeferencing inputs obtained by processing SAR and InSAR (Interferometric SAR) images [1], [2]. II. SARINA SYSTEM DESCRIPTION Two aerials platform were simulated within the project:

• 1. MALE UAV: Predator B (empty: 2200kg; int.+ext.

payloads: 1300+400kg; max. altitude: 7.5km, range≈1000km, presented in Fig. 5)

• 2. Cruise missile: Tomahawk (empty: 477kg; fuel+warhead

payloads: 513+450kg; max. altitude: 6km, range≈1000km, presented in Fig. 4)

The reference navigation sensor for SARINA simulator was a Navigation Grade Inertial Measurement Unit (IMU): LN- 100G from Northrop Grumman (Accuracy 0.05°, 9.8kg, 28x18x18cm3) presented in Fig. 2. The reference SAR Sensor for SARINA Simulator was Pico SAR (stripmap, range>20km, 1m resolution, 10kg, 32x23x16cm3), presented in Fig. 1. The reference Air Data Computer (ADC) Sensor model implemented inside the SARINA Simulator has been: The 2018R from the Goodrich Company (Pressure altitude accuracy around 7 feet), presented in Fig. 3.

Figure 1. An example of reference SAR sensor used for SARINA simulator (Pico SAR system produced Selex Galileo [7]) Figure 2. An example of reference IMU platform used for SARINA simulator (LN-100G system produced Northrop Grumman [8]) Figure 3. An example of reference ADC sensor used for SARINA simulator (2018R (produced by Goodrich [9]) Figure 4. An example of reference missile platform used for SARINA simulator (Tomahawk missile produced by Hughes (presently part of Raytheon [9]) Figure 5. An example of reference UAV platform used for SARINA simulator (Predator UAV produced by General Atomics Aeronautical [10])

III. RESULTS EXEMPLIFICATIONS The main aim of the simulation was to show the accuracy improvement during the mission, when GPS data is not

• available. The sample result of exploiting SAR data during

UAV – MISSION is presented in Fig 6, where S is Starting Point, WP – Way Point (every WP corresponds to either SAR

• r InSAR image acquisition), E – End Point. The figure clearly

shows the expected differences among planned trajectory, trajectory based on SARINA system and trajectory based on a traditional INS. Note that both the simulations (SARINA and the traditional INS) assume a jammed GPS. However, only the SARINA system would be robust against any drift caused by uncompensated IMU (due to the jammed GPS) as opposed to the traditional INS, which shows a dramatic drift.

Figure 6. Example of UAV trajectories (S – starting point; WP – waypoint; E – end point): planned trajectory, trajectory based on SARINA system (jammed GPS), trajectory based on a traditional INS (jammed GPS).

The details of SAR mission concept is presented in Fig 7. The mission was fulfilled in the suburban scenario. The Landmarks available in SAR image, was acquired on-board and compared with the data based stored in computer memory. The drift correction was based on the best match between planned (latitude/longitude./altitude) and automatically extracted (azimuth/slant-range) Landmark coordinates as a function of aircraft state variables.

Figure 7. Suburban Scenario: Landmarks available in SAR image, acquired

• n-board (SAR image: courtesi of Agenzia Spaziale Italiana – ASI in the

framework of “Accordo Quadro Cooperazione”)

The automatic extraction step is performed by a novel Automatic Target Recognition chain, which is able to automatically extract the planned mission Landmarks [2], [3]. Fig 8 presents an exemplification of suburban Scenario, where: (a) SAR image (x-axis – slant range; y-axis – cross- range) is simulated according to the available telemetry data and the expected Landmarks [14]; (b) the ATR chain recognizes two planned buildings and four cross-roads (i.e. the six planned Landmarks), by resorting not only to their expected geometrical features (e.g. length, width, height), but also to their expected position in the SAR image. Finally, both extracted radar coordinates (those obtained under the actual SAR viewing geometry) and the world coordinates (those in the mission Data Base - DB) of the same Landmark (e.g. the biggest building) are used to retrieve the aircraft state variables.

8800 9000 9200 9400 9600 9800 10000 10200 10400 10600 500 1000 1500

(a)

8800 9000 9200 9400 9600 9800 10000 10200 10400 10600 500 1000 1500

(b) Figure 8. Suburban Scenario: (a) SAR image (x-axis – slant range; y-axis – cross-range) simulated according to the available telemetry data; (b) result of the ATR chain, which recognizes two planned buildings and four cross-roads.

The SAR concept is designed to be successful in urban and suburban scenario, however in open lands (e.g. in mountains) there are more difficulties to find very characteristic landmarks, so instead of SAR image comparison the hight profiles obtained by InSAR technique was tested. Fig. 9 presents InSAR mission concept using DTM data base. The mission was simulated in Alpine scenario and no landmarks were available in InSAR images, acquired on-board. The correction algorithm was as follows:  For every WayPoint (WP), Phase Field (PF) is overlapped to the local orography.  Drift correction based on the best match between planned (based on the expected Digital Terrain Model - DTM) and automatically extracted PFs as a function of aircraft state variables.

. Figure 9. Alpine Scenario: no Landmarks available in InSAR couple, acquired on-board

The InSAR Processing step is performed by a processing chain, which is able to automatically extract the orography of the imaged area. Fig 10 presents an exemplification of Mountains Scenario, where: (a) InSAR image couple (x-axis – slant range; y-axis – cross-range) is simulated according to the available telemetry data and the actual local orography; (b) the InSAR Processing chain retrieve both the actual wrapped and (then) unwrapped PF of the local orography, by exploiting the acquired image couple. The extracted local orography (i.e. the actual unwrapped PF) is compared to the planned mission

• rography (i.e. the expected unwrapped PF) to retrieve the

aircraft state variables.

(a) (b) Figure 10. Mountains Scenario: (a) InSAR couple (left- master; right - slave) simulated according to the available telemetry data; (b) wrapped (on the left) and unwrapped (on the right) PFs from the “acquired” InSAR couple.

An early navigation result and the reached errors are plotted in

• Fig. 11. The results of SARINA software simulation are very
• promising. The position error (real vs. planned trajectories)

changes for ca. 30 m to ca. 6 m after SARINA correction and such positioning errors are expected during whole mission.

Figure 11. Early result from UAV Mission: (left) horizontal trajectory; (right) across track distance

IV. CONCLUSIONS In the paper selected results obtain under SARINA project has been presented. The results show high capability of the proposed SARINA system. Preliminary results showed that the SARINA system can outperform the current navigation systems. Compared to traditional INS/GPS systems: SARINA is insensitive to electronic war. A standalone sensor is used as external position correction; contrarily to the GPS, no link is needed with the external world, this explains why this system is jamming resistant. Compared to INS-only navigation system, which works without any GPS: SARINA can fly over plate zones, on the contrary to the Terrain Contour Matching (TERCOM) radar navigation system [15]; SARINA can fly during the night and in any weather conditions, on the contrary to the Digital Scene- Mapping Area Correlator (DSMAC) electro-optics navigation system. However, such results were reached by developing new modules integrated in the SARINA system. It is worth noting that such modules are represents absolutely novel approaches tailored for SARINA concept:  EKF-DFU able to manage SAR/InSAR georeferencing estimates and their intrinsic latencies [1], [2];  Autofocusing technique able to work in the absence of accurate navigation data and prominent scatterers [11], [12];  novel ATR processing chain able to robustly recognize small (e.g. buildings, warehouses) and linear (e.g. roads, railways) Landmarks [2], [3];  novel InSAR processing chain able to accurately retrieve a/c state variables on the basis of the difference between

planned and actually extracted PF of the monitored area [1], [2]. ACKNOWLEDGMENT The authors acknowledge the support of the SARINA project A-0932-RT-GC, which is coordinated by the European Defense Agency (EDA) and partially funded by 10 contributing Members (Cyprus, France, Germany, Greece, Hungary, Italy, Norway, Poland, Slovakia, Slovenia and Spain) in the framework of the Joint Investment Programme on Innovative Concepts and Emerging Technologies (JIP-ICET). REFERENCES

[1] M. Greco, K. Kulpa, G. Pinelli, P. Samczynski, ”SAR and InSAR georeferencing algorithms for Inertial Navigations Systems”, in Proc. of SPIE, Vol. 8008 (SPIE, Bellingham, WA, 2011) [2] M. Greco, G. Pinelli, K. Kulpa, P. Samczynski, B. Querry, S. Querry, “The study on SAR images exploitation for air platform navigation purposes”, in Proceedings of the International Radar Symposium – IRS 2011, September 7-9, 2011, Leipzig, Germany, pp. 347-352. [3] L. Fabbrini, M. Messina, M. Greco, and G. Pinelli, “Linear landmark extraction in SAR images with application to augmented integrity aero- navigation: an overview to a novel processing chain”, in Proc. of SPIE,

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[14] K. Kulpa, P. Samczynski, M. Malanowski, W. Gwarek, B. Salski, G. Tanski, „SAR Raw Radar Simulator combining optical geometry and full-wave electromagnetic approaches” – paper has been accepted to be published in Proceedings on EuSAR 2012 – 9th European Conference

• n Synthetic Aperture Radar, April 23-26, 2012, Nurnberg, Germany, in

press [15] Golden, J.P. “Terrain Contour Matching (TERCOM): A Cruise Missile Guidance Aid". Proceedings of the International Society for Optical Engineering (SPIE) Image Processing for Missile Guidance, volume 238, 10{18. 1980.

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