| 1. |
- Alves, Dimas irion, et al.
(författare)
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Change Detection Method for Wavelength-Resolution SAR Images Based on Bayes’ Theorem : An Iterative Approach
- 2023
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Ingår i: IEEE Access. - Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 11, s. 84734-84743
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents an iterative change detection (CD) method based on Bayes’ theorem for very high-frequency (VHF) ultra-wideband (UWB) SAR images considering commonly used clutter-plus-noise statistical models. The proposed detection technique uses the information of the detected changes to iteratively update the data and distribution information, obtaining more accurate clutter-plus-noise statistics resulting in false alarm reduction. The Bivariate Rayleigh and Bivariate Gaussian distributions are investigated as candidates to model the clutter-plus-noise, and the Anderson-Darling goodness-of-fit test is used to investigate three scenarios of interest. Different aspects related to the distributions are discussed, the observed mismatches are analyzed, and the impact of the distribution chosen for the proposed iterative change detection method is analyzed. Finally, the proposed iterative method performance is assessed in terms of the probability of detection and false alarm rate and compared with other competitive solutions. The experimental evaluation uses data from real measurements obtained using the CARABAS II SAR system. Results show that the proposed iterative CD algorithm performs better than the other methods. Author
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| 2. |
- Berg, Anders, 1983, et al.
(författare)
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X-Band Interferometric SAR Observations of Baltic Fast Ice
- 2015
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Ingår i: IEEE Transactions on Geoscience and Remote Sensing. - 0196-2892 .- 1558-0644. ; 53:3, s. 1248-1256
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Tidskriftsartikel (refereegranskat)abstract
- Detailed mapping of fast-ice deformation can be used to characterize the rheological behavior of fast ice and subsequently improve sea ice modeling. This study uses interferometric synthetic aperture radar to map fast-ice deformation with unprecedented spatial resolution (meter range) and sensitivity (cm-mm range). Two interferometric acquisitions, each with a temporal baseline of 24 h, were performed by the X-band SAR satellite constellation Cosmo-SkyMed over the northeast Bay of Bothnia in the middle of the 2012 ice season. The first interferogram shows deformation of the fast ice due to force from impinging drift ice, and the normal strain within the fast ice is measured. Complementary intensity correlation measurements reveal a slow movement of the drift ice toward the fast ice. The second interferogram exhibits a low fringe rate over the fast ice with fringes being aligned along the coastline. Deformation appears to be stronger around leads, skerries, and grounded ice ridges. It is also observed that the coherence images provide information that is complementary to the information in the backscatter images.
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| 3. |
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| 4. |
- Dammert, Patrik, 1968
(författare)
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Spaceborne SAR Interferometry: Theory and Applications
- 1999
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis presents the theoretical basis and system model for interferometric synthetic aperture radar, INSAR, from spaceborne platforms. The model describes how the images are formed and what relevant ground parameters affect the signal. There are several useful ground parameters in spaceborne INSAR data. These are scene coherence and backscattering, ground topography, small surface movements and atmospheric artifacts, which may be used for different kinds of applications. This thesis covers four applications, land-cover characterization (using backscattering and coherence), digital height map generation, forest parameter retrieval (using coherence) and measurements of small sea ice movements. For a correct assessment of the accuracy of each application, the parameters have to be estimated correctly from the INSAR data. It is shown that although some parameter estimates are biased and noisy, they are still useful. Land-cover characterization is possible up to 75% accuracy for four land-cover classes for three different test areas. Height maps may have an accuracy down to 3 m for three-day repeat-pass INSAR and slightly higher for longer time intervals over a forested test area. The heights of the trees also add to the height measured over the forest, leading to a "forest height bias" which can vary widely for different INSAR images. Forest parameter retrieval is possible for the case of stem volume measurements with an accuracy down to 30 m3/ha. The small-scale rheology (small movements) in Baltic low-saline sea ice was possible to evaluate and measure with an unprecedented accuracy with INSAR images.
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| 5. |
- Hellsten, Hans, et al.
(författare)
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Evaluation of a geometrical autofocus algorithm within the framework of Fast Factorized Back-Projection
- 2012
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Ingår i: Proceedings of IET RADAR 2012, 22-25 October, Glasgow, Scotland. - : Institution of Engineering and Technology. - 9781849196765 ; 2012:603 CP
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Konferensbidrag (refereegranskat)abstract
- This paper introduces a new autofocus algorithm, specifically suitable for UWB SAR systems. The strategy is integrated in FFBP and relies on varying track parameters stage by stage to obtain a sharp image. Focus measures are provided by an object function (intensity correlation). The algorithm has been tried out successfully on a synthetic data set.
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| 6. |
- Jonsson, Robert, 1989, et al.
(författare)
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Experimental Analysis of a Clutter Suppression Algorithm for High Time-Bandwidth Noise Radar
- 2023
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Ingår i: Proceedings of the IEEE Radar Conference. - : Institute of Electrical and Electronics Engineers (IEEE). - 2375-5318 .- 1097-5764.
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Konferensbidrag (refereegranskat)abstract
- Noise-like, continuous waveforms have several benefits for radar operation, such as low probability of interception/identification. However, the same types of waveforms come with a significant drawback because strong signals, e.g., ground clutter, produce a correlation noise floor (CNF) that masks all weak signals. In this article, we report on the implementation and performance of an efficient clutter suppression algorithm used to suppress strong clutter echoes and allow for the detection of weaker signals. The algorithm's performance is verified using an experimental system with a time-bandwidth product of 70 dB at a centre frequency of 1.3 GHz. Application of the algorithm suppresses the CNF by over 30 dB, allowing for the detection of an unmanned aerial vehicle (UAV).
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| 7. |
- Klintberg, Jacob, 1994, et al.
(författare)
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A Parametric Approach to Space-Time Adaptive Processing in Bistatic Radar Systems
- 2022
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Ingår i: IEEE Transactions on Aerospace and Electronic Systems. - 1557-9603 .- 0018-9251. ; 58:2, s. 1149-1160
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Tidskriftsartikel (refereegranskat)abstract
- Space-time adaptive processing (STAP) is an important airborne radar technique used to improve target detection in environments of clutter and jammers. Effective STAP implementations are dependent on an accurate estimate of the space-time covariance matrix, which characterizes noise and interference in the radar signal. Inside-looking monostatic radar systems, the estimate based on secondary radar observations is rather straight forward as all the samples in secondary data can be argued to be from a single distribution, and the sample covariance can be used as an estimate of the space-time covariance matrix. However, in many other radar configurations, the vital underlying STAP training assumption that secondary data are identically distributed is violated, which implies that detection performance can be significantly degraded. This article develops a new method that can be used when secondary data do not share a common distribution due to geometry-induced range dependencies. This phenomenon is of particular concern in bistatic radar systems. We propose a model-based approach, where the distribution of noise and clutter for each range bin is parameterized by a set of scenario parameters. Using secondary data, the scenario parameters are estimated by maximizing the likelihood function. Based on the estimated scenario parameters, the STAP covariance estimate is formed for the cell under test. The presented method is compared with other state-of-the-art methods for bistatic radar STAP via numerical simulations. The simulations indicate that the presented method, with a proper initialization, yields an estimate of the STAP covariance matrix that significantly increases the signal-to-interference-plus-noise ratio compared to the other investigated methods.
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| 8. |
- Klintberg, Jacob, 1994, et al.
(författare)
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A Parametric Generalized Likelihood Ratio Test for Airborne Bistatic Radar Systems
- 2022
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Ingår i: Proceedings of the IEEE Radar Conference. - 2375-5318 .- 1097-5764.
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Konferensbidrag (refereegranskat)abstract
- One of the main objectives of a radar system is to provide target detections. That is, from observations contaminated by receiver noise and interference determine the presence or absence of targets in the current measurements. To enable target detections, the test statistics formed by the processor is dependent on an accurate estimate of the spacetime covariance matrix to characterize the influence of thermal noise and interference on the radar signal. In a side-looking monostatic configuration, the estimate is rather straight forward as the secondary data used in the estimate can be argued to be statistically identical and independently distributed as the observation in the cell under test. However, for many other radar configurations, secondary data may suffer from angleDoppler variations over the range dimension, which introduces a non-stationary behavior in the observations. If used in a detector, such secondary data may cause significantly degraded detection performance. In this work, we propose an approach which incorporates the non-stationarities of the secondary data into the generalized likelihood ratio test. Thus, we propose a scenario and range dependent parametric model of the observed data and formulate an adaptive detector based on the generalized likelihood ratio test. The presented approach is evaluated against other state-of-the-art methods for managing target detections in the presence of non-stationary secondary data in bistatic systems. The simulations indicates that the proposed approach of imposing scenario based structure on the generalized likelihood ratio test significantly contributes to an increased performance of the target detection scheme compared to the other investigated methods.
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| 9. |
- Klintberg, Jacob, 1994, et al.
(författare)
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Mitigation of Ground Clutter in Airborne Bistatic Radar Systems
- 2020
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Ingår i: Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop. - 2151-870X. ; 2020-June
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Konferensbidrag (refereegranskat)abstract
- Space-Time Adaptive Processing is a commonly used technique to mitigate ground clutter reflections from an airborne radar system. It estimates a covariance matrix based on spatial and temporal information, and the estimate is thereafter used to suppress the ground clutter. In a side-looking monostatic radar system, the estimate is rather straight forward based on radar observations. However, in this paper, we consider bistatic systems where the power of adaptivity is limited due to nonstationarity of the ground clutter reflections over the range dimension. To overcome this, scenario dependent transformations are commonly used when forming the sample covariance matrix. In this contribution we instead investigate a detector where the clutter covariance matrix is determined from the geometry of the bistatic scenario. Using a Monte-Carlo simulation, we investigate how sensitive the detector is to errors in the assumed geometry, and compare this with state-of-the-art adaptive methods. The results indicates that a good clutter rejection is obtained for errors of order 103 m for assumed transmitter position and 100km/h for assumed transmitter velocity.
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| 10. |
- Klintberg, Jacob, 1994, et al.
(författare)
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Scenario Based Transformations for Compensation of Non-Stationary Radar Clutter
- 2022
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Space-Time Adaptive Processing is an important technique for enhancing detection performance in airborne radar systems. The enhanced performance is obtained by mitigating the influence of interference and noise in the radar observations. To perform the mitigation, an accurate estimate of the corresponding space-time covariance matrix of the interference and noise distribution is required. Usually, such an estimate is obtained from secondary data collected from neighboring range bins around the currently investigated cell-under-test. However, in a bistatic radar configuration, the secondary data suffers from geometry-induced angle-Doppler variations along the range dimension. In such configurations, additional processing to handle the angle-Doppler variations is required to obtain a covariance matrix estimate of high accuracy. In this paper, we derive a transformation matrix framework to compensate for the variations over range in the secondary data. The framework is a combination of an incomplete scenario model and secondary data which are used together to obtain a space-time covariance matrix estimate. Thus, the incomplete scenario model is used to find the unitary transformation matrix which, in a Frobenius norm sense, minimizes the expected clutter response from the incomplete scenario model in each range bin towards the corresponding clutter response in a reference range bin. The unitary property of the transformation preserve the stationary behavior of the thermal noise under the transformation. Using such transformation, a set of non-stationary secondary data can be transformed to become more stationary distributed after the transformation. A sample covariance matrix estimator is applied on the transformed set of secondary data to obtain a space-time covariance matrix estimate. The outlined procedure is denoted as a Scenario Based Transformation (SBT) STAP. In numerical simulations, the SBT algorithm is compared with other state-of-the-art methods for the considered problem. The numerical simulations include evaluations on scenarios with a various degree of mismatch between the model generating observations and the model assumed by the investigated algorithms. The included model misspecifications are intrinsic clutter motion, antenna array calibration residuals and incorrect antenna gain patterns. In case of a model match, the simulations indicated that the SBT method yields an improved performance compared to the other investigated methods. For the simulations including model misspecifications, the results indicates that the level of misspecification influence the performance of the considered methods. For a low level of misspecifications, the SBT approach yields an accurate covariance estimate. However, for large misspecifications, the simulations indicates that a non-parametric approach leads to better results.
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