Evaluation of a Kalman filter for SAR motion compensation

DiFilippo, D.; Haslam, G.; Widnall, William S.

doi:10.1109/plans.1988.195492

Cited by 12 publications

(5 citation statements)

References 3 publications

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“…The process of integration, however, requires bringing INS and GPS measurements to a common moment of time and a point in space, which is neglected in solutions presented in the MOCO literature [1,2,3] but is considered in the method presented in this paper. The data synchronization was achieved by interpolating results obtained at a higher frequency [from the inertial measurement unit (IMU)] to the time of data obtained at a lower frequency (GPS data).…”

Section: System Modelmentioning

confidence: 99%

“…In the MOCO using an integrated INS/GPS system, there is a common assumption that INS and GPS produce data at the same frequency, which simplifies the construction of the measurement vector

boldnormal z (k)

[1,2,3]. In our system, the GPS position was determined at 5 Hz, while GPS velocity was at 20 Hz.…”

Section: System Modelmentioning

confidence: 99%

“…The first group of methods uses additional navigational equipment to determine UAV position and velocity deviations and to compute phase corrections for the received echo signals [1,2,3]. These corrections are then introduced to the echo signals in an azimuth compression procedure [4].…”

Section: Introductionmentioning

confidence: 99%

“…In the proposed system, the INS and the GPS data integration was implemented using a Kalman filter [13,14,15,16]. The algorithm proposed by the authors was modified in comparison to the solutions used thus far in MOCO procedures [1,2,3]. In these systems, it is commonly assumed that INS and GPS receivers provide measurements with the same frequency and their solutions are available at the same moment of time.…”

Section: Introductionmentioning

confidence: 99%

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

Labowski

Kaniewski

Serafin

2019

Sensors

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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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Section: System Modelmentioning

confidence: 99%

“…In the MOCO using an integrated INS/GPS system, there is a common assumption that INS and GPS produce data at the same frequency, which simplifies the construction of the measurement vector

boldnormal z (k)

[1,2,3]. In our system, the GPS position was determined at 5 Hz, while GPS velocity was at 20 Hz.…”

Section: System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Labowski

Kaniewski

Serafin

2019

Sensors

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“…Our limited goals here in using Kalman filter-based technology is merely (1) to correct for residual uncompensated relative motion between target and sensor focal plane [4] 2 and (2) to compensate for any imaging sensor mis-focusing present (that can be modeled) and (3) to give the best results possible in the acknowledged presence of the above enumerated noises. .…”

Section: Introductionmentioning

confidence: 99%

<title>Extending decentralized Kalman filtering results for novel real-time multisensor image fusion and/or restoration</title>

Kerr¹

1996

SPIE Proceedings

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We pursue the idea that recent "decentralized" Kalman filter (KF) technology, by outfitting each participating imaging sensor with its own dedicated 2-D Kalman filter can be used as the basis of a sensor fusion methodology that allows a final collating filter to assemble the data from diverse imaging sensors of various resolutions into a single resulting image that combines all the available information (in analogy to what is already routinely done in multisensor NAVIGATION applications). The novelty is in working out the theoretical details for 2-D filtering situations while assuming that the image registration problem has already been independently handled beforehand. We synchronize frame size and location of pixels of interest to be comparably located with same "raster scan" speed and size used for each to match up for different sensors. Rule for linear Kalman filters with only Gaussian noises is that the combining of underlying measurements or sensor information can only help and never hurt. We interpret this approach as using several common views of the same scene, as instantaneously obtained from different sensors, all being stacked up vertically one on top of the other, each with its own local 2-D Kalman-like image restoration filter proceeding to raster scan (in multi-layer sync). Then apply the multi-filter combining rules from Decentralized filtering to the bunch to obtain a single best estimate image as the resulting output as a convenient methodology to achieve sensor fusion.

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