Airborne radar terrain imaging system

In order to obtain good quality radar terrain images using an aerial-based synthetic aperture radar, a motion compensation procedure must be applied. This procedure can use a precise navigation system in order to determine the aircraft’s position and velocity. A major challenge is to design a motion compensation procedure that can operate in real time, which is crucial to ensure convenient data for a human analyst. The article discusses a possibility of Inertial Measurement System (INS)/Global Positioning System (GPS) navigation system usage in such a radar imaging system. A Kalman filter algorithm designed for this system is described herein, and its modifications introduced by the authors allow the use of navigational data not aligned in time and captured with different frequencies. The presented navigation system was tested using measured data. Radar images obtained with the INS/GPS-based motion compensation system were compared to the INS-only results and images obtained without navigation corrections. The evaluation results presented in the paper show that the INS/GPS system allows for better reduction of geometric distortions in images compared to the INS-based approach, which makes it more suitable for typical applications.

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“…The dynamics model was determined using equations described in [15,17]. Finally, the transition matrix

boldnormal Φ (k, k - 1)

has the following form:

bold-sans-serifΦ = [\begin{matrix} 1 & T_{p} & - \frac{f_{i s 1, D}^{n} T_{p}^{2}}{2} & 0 & 0 & 0 & 0 & 0 & {sans-serifΦ}_{19} \\ 0 & {sans-serifΦ}_{22} & - f_{i s 1, D}^{n} T_{p} & 0 & 0 & 0 & 0 & 0 & {sans-serifΦ}_{29} \\ 0 & - \frac{T_{p}}{R} & {sans-serifΦ}_{33} & 0 & 0 & 0 & 0 & 0 & {sans-serifΦ}_{39} \\ 0 & 0 & 0 & 1 & T_{p} & \frac{f_{i s 1, D}^{n} T_{p}^{2}}{2} & 0 & 0 & {sans-serifΦ}_{49} \\ 0 & 0 & 0 & 0 & {sans-serifΦ}_{55} & f_{i s 1, D}^{n} T_{p} & 0 & 0 & {sans-serifΦ}_{59} \\ 0 & 0 & 0 & 0 & \frac{T_{p}}{R} & {sans-serifΦ}_{66} & 0 & 0 & {sans-serifΦ}_{69} \\ 0 & 0 & 0 & 0 & 0 & 0 & 1 + \frac{g_{b, D}^{n} T_{p}^{2}}{R} & 1 & 0 \\ 0 & 0 & 0 & 0 & 0 & 0 & \frac{2 g_{b, D}^{n} T_{p}}{R} & 1 + \frac{g_{b, D}^{n} T_{p}^{2}}{R} & 0 \\ 0 & 0 & 0 & 0 \end{matrix}

…”

Section: System Modelmentioning

confidence: 99%

Motion Compensation for Radar Terrain Imaging Based on INS/GPS System

Labowski

Kaniewski

Serafin

2019

Sensors

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“…In the case of a loosely integrated system [9], designed in the WATSAR project [17,20], the discrete state-space model is linear and it is given by a pair of equations [11, 13−15]:…”

Section: State-space Model Of Ins/gnss Systemmentioning

confidence: 99%

“…during maneuvers of UAV, and only in high-quality navigation-grade inertial systems. As in the WATSAR project only a medium-quality, tactical-grade INS has been used, a simple 9-state model of INS errors has been applied [17], with 3 states for position errors, 3 states for velocity errors and 3 states for orientation errors with respect to various axes of the local reference horizontal system of coordinates NED (North-East-Down) [9].…”

Section: State-space Model Of Ins/gnss Systemmentioning

confidence: 99%

“…SAR imagery, requirements with respect to the accuracy of positioning are very high [3,4,16,17]. On the other hand, short delays in image availability are often acceptable.…”

Section: Introductionmentioning

confidence: 99%

“…It is assumed that the system is loosely integrated according to the compensation method with feed-forward correction [5,19] and is composed of an INS and a GNSS receiver. Such an INS/GNSS system has been designed and produced within the scope of the WATSAR project, performed by the Military University of Technology, Warsaw, Poland, and a Polish private company WB Electronics S.A. [17,20]. Subsequently, the fixed-interval and the fixed-lag smoothing algorithms are described.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of UAV Position with Use of Smoothing Algorithms

Kaniewski

Gil

Konatowski

2017

Metrology and Measurement Systems

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The paper presents methods of on-line and off-line estimation of UAV position on the basis of measurements from its integrated navigation system. The navigation system installed on board UAV contains an INS and a GNSS receiver. The UAV position, as well as its velocity and orientation are estimated with the use of smoothing algorithms. For off-line estimation, a fixed-interval smoothing algorithm has been applied. On-line estimation has been accomplished with the use of a fixed-lag smoothing algorithm. The paper includes chosen results of simulations demonstrating improvements of accuracy of UAV position estimation with the use of smoothing algorithms in comparison with the use of a Kalman filter.

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