468 research outputs found
High Speed Dim Air Target Detection Using Airborne Radar under Clutter and Jamming Effects
The challenging potential problems associated with using airborne radar in detection of high Speed Maneuvering Dim Target (HSMDT) are the highly noise, jamming and clutter effects. The problem is not only how to remove clutter and jamming as well as the range migration and Doppler ambiguity estimation problems due to high relative speed between the targets and airborne radar. Some of the recently published works ignored the range migration problems, while the others ignored the Doppler ambiguity estimation. In this paper a new hybrid technique using Optimum Space Time Adaptive Processing (OSTAP), Second Order Keystone Transform (SOKT), and the Improved Fractional Radon Transform (IFrRT) was proposed. The OSTAP was applied as anti-jamming and clutter rejection method, the SOKT corrects the range curvature and part of the range walk, then the IFrRT estimates the targetβ radial acceleration and corrects the residual range walk. The simulation demonstrates the validity and effectiveness of the proposed technique, and its advantages over the previous researches by comparing its probability of detection with the traditional methods. The new approach increases the probability of detection, and also overcomes the limitation of Doppler frequency ambiguity
Adaptive Radar Detection of Dim Moving Targets in Presence of Range Migration
This paper addresses adaptive radar detection of dim moving targets. To
circumvent range migration, the detection problem is formulated as a multiple
hypothesis test and solved applying model order selection rules which allow to
estimate the "position" of the target within the CPI and eventually detect it.
The performance analysis proves the effectiveness of the proposed approach also
in comparison to existing alternatives.Comment: 5 pages, 2 figures, submitted to IEEE Signal Processing Letter
SAR Image Formation via Subapertures and 2D Backprojection
Radar imaging requires the use of wide bandwidth and a long coherent processing interval, resulting in range and Doppler migration throughout the observation period. This migration must be compensated in order to properly image a scene of interest at full resolution and there are many available algorithms having various strengths and weaknesses. Here, a subaperture-based imaging algorithm is proposed, which first forms range-Doppler (RD) images from slow-time sub-intervals, and then coherently integrates over the resulting coarse-resolution RD maps to produce a full resolution SAR image. A two-dimensional backprojection-style approach is used to perform distortion-free integration of these RD maps. This technique benefits from many of the same benefits as traditional backprojection; however, the architecture of the algorithm is chosen such that several steps are
shared with typical target detection algorithms. These steps are chosen such that no compromises need to be made to data quality, allowing for high quality imaging while also preserving data for implementation of detection algorithms. Additionally, the algorithm benefits from computational savings that make it an excellent imaging algorithm for implementation in a simultaneous SAR-GMTI architecture
ΠΡΠΎΡΡΠΎΠΉ Π°Π»Π³ΠΎΡΠΈΡΠΌ ΠΊΠΎΠΌΠΏΠ΅Π½ΡΠ°ΡΠΈΠΈ ΠΌΠΈΠ³ΡΠ°ΡΠΈΠΉ ΡΠ²Π΅ΡΡΡΠΈΡ ΡΡ ΡΠΎΡΠ΅ΠΊ ΠΏΠΎ Π΄Π°Π»ΡΠ½ΠΎΡΡΠΈ Π΄Π»Ρ ΡΠ΅ΠΆΠΈΠΌΠ° Π±ΠΎΠΊΠΎΠ²ΠΎΠ³ΠΎ ΠΎΠ±Π·ΠΎΡΠ° Π Π‘Π (Π°Π½Π³Π».)
Introduction.Β Range Cell Migration (RCM) is a source of image blurring in synthetic aperture radars (SAR). There are two groups of signal processing algorithms used to compensate for migration effects. The first group includes algorithms that recalculate the SAR signal from the "alongβtrack range β slant range" coordinate system into the "along-track rangeΒ βΒ cross-track range"Β coordinates using the method of interpolation. The disadvantage of these algorithms is their considerable computational cost. Algorithms of the second group do not rely on interpolation thus being more attractive in terms of practical application.Aim. To synthesize a simple algorithm for compensating for RCM without using interpolation.Materials and methods. The synthesis was performed using a simplified version of the Chirp Scaling algorithm.Results.Β A simple algorithm, which presents a modification of the Keystone Transform algorithm, was synthesized. The synthesized algorithm based on Fast Fourier Transforms and the Hadamard matrix products does not require interpolation.Conclusion. A verification of the algorithm quality via mathematical simulation confirmed its high efficiency. Implementation of the algorithm permits the number of computational operations to be reduced. The final radar imageΒ produced using the proposed algorithm is built in the true Cartesian coordinates. The algorithm can be applied for SAR imaging of moving targets. The conducted analysis showed that the algorithm yields Β theΒ image of a moving target provided that the coherent processing interval is sufficiently large. The image lies along a line, which angle of inclination is proportional to the projection of the target relative velocity on the line-of-sight. Estimation of the image parameters permits the target movement parameters to be determined.ΠΠ²Π΅Π΄Π΅Π½ΠΈΠ΅. ΠΠΈΠ³ΡΠ°ΡΠΈΠΈ ΡΠ²Π΅ΡΡΡΠΈΡ
ΡΡ ΡΠΎΡΠ΅ΠΊ ΠΏΠΎ Π΄Π°Π»ΡΠ½ΠΎΡΡΠΈ ΡΠ²Π»ΡΡΡΡΡ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΎΠΌ ΡΠ°ΡΡΠΎΠΊΡΡΠΈΡΠΎΠ²ΠΊΠΈ ΡΠ°Π΄ΠΈΠΎΠ»ΠΎΠΊΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ Π² ΡΠ°Π΄ΠΈΠΎΠ»ΠΎΠΊΠ°ΡΠΎΡΠ°Ρ
Ρ ΡΠΈΠ½ΡΠ΅Π·ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ Π°ΠΏΠ΅ΡΡΡΡΠΎΠΉ (Π Π‘Π). Π‘ΡΡΠ΅ΡΡΠ²ΡΠ΅Ρ Π΄Π²Π΅ Π³ΡΡΠΏΠΏΡ Π°Π»Π³ΠΎΡΠΈΡΠΌΠΎΠ² ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ ΡΠΈΠ³Π½Π°Π»ΠΎΠ² Π΄Π»Ρ ΠΊΠΎΠΌΠΏΠ΅Π½ΡΠ°ΡΠΈΠΈ ΠΌΠΈΠ³ΡΠ°ΡΠΈΠΉ. ΠΠ΅ΡΠ²Π°Ρ Π³ΡΡΠΏΠΏΠ° Π²ΠΊΠ»ΡΡΠ°Π΅Ρ Π°Π»Π³ΠΎΡΠΈΡΠΌΡ, Π² ΠΊΠΎΡΠΎΡΡΡ
Π½Π° ΠΎΡΠ½ΠΎΠ²Π°Π½ΠΈΠΈ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΡΡΠ½ΠΊΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠΉ ΠΈΠ½ΡΠ΅ΡΠΏΠΎΠ»ΡΡΠΈΠΈ ΠΎΡΡΡΠ΅ΡΡΠ²Π»ΡΠ΅ΡΡΡ ΠΏΠ΅ΡΠ΅ΡΡΠ΅Ρ ΠΏΡΠΈΠ½ΡΡΡΡ
ΡΠΈΠ³Π½Π°Π»ΠΎΠ² ΠΈΠ· ΡΠΈΡΡΠ΅ΠΌΡ ΠΊΠΎΠΎΡΠ΄ΠΈΠ½Π°Ρ "ΠΏΡΠΎΠ΄ΠΎΠ»ΡΠ½Π°Ρ Π΄Π°Π»ΡΠ½ΠΎΡΡΡ β Π½Π°ΠΊΠ»ΠΎΠ½Π½Π°Ρ Π΄Π°Π»ΡΠ½ΠΎΡΡΡ"Β Π² ΡΠΈΡΡΠ΅ΠΌΡ "ΠΏΡΠΎΠ΄ΠΎΠ»ΡΠ½Π°Ρ Π΄Π°Π»ΡΠ½ΠΎΡΡΡ β ΠΏΠΎΠΏΠ΅ΡΠ΅ΡΠ½Π°Ρ Π΄Π°Π»ΡΠ½ΠΎΡΡΡ". ΠΠ΅Π΄ΠΎΡΡΠ°ΡΠΊΠΎΠΌ Π°Π»Π³ΠΎΡΠΈΡΠΌΠΎΠ² Π΄Π°Π½Π½ΠΎΠΉ Π³ΡΡΠΏΠΏΡ ΡΠ²Π»ΡΠ΅ΡΡΡ ΠΈΡ
Π²ΡΡΠΎΠΊΠ°Ρ Π²ΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½Π°Ρ ΡΠ»ΠΎΠΆΠ½ΠΎΡΡΡ. ΠΠ»Π³ΠΎΡΠΈΡΠΌΡ Π²ΡΠΎΡΠΎΠΉ Π³ΡΡΠΏΠΏΡ Π½Π΅ ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΡΡ ΠΈΠ½ΡΠ΅ΡΠΏΠΎΠ»ΡΡΠΈΠΎΠ½Π½ΡΠ΅ ΠΌΠ΅ΡΠΎΠ΄Ρ ΠΈ ΡΠ²Π»ΡΡΡΡΡ ΠΏΠΎΡΡΠΎΠΌΡ Π±ΠΎΠ»Π΅Π΅ ΠΏΡΠΈΠ²Π»Π΅ΠΊΠ°ΡΠ΅Π»ΡΠ½ΡΠΌΠΈ Π΄Π»Ρ ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ.Π¦Π΅Π»Ρ.Β Π‘ΠΈΠ½ΡΠ΅Π·ΠΈΡΠΎΠ²Π°ΡΡ ΠΏΡΠΎΡΡΠΎΠΉ Π°Π»Π³ΠΎΡΠΈΡΠΌ ΠΊΠΎΠΌΠΏΠ΅Π½ΡΠ°ΡΠΈΠΈ ΠΌΠΈΠ³ΡΠ°ΡΠΈΠΉ Π±Π΅Π· ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ ΡΡΠ½ΠΊΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠΉ ΠΈΠ½ΡΠ΅ΡΠΏΠΎΠ»ΡΡΠΈΠΈ.ΠΠ°ΡΠ΅ΡΠΈΠ°Π»Ρ ΠΈ ΠΌΠ΅ΡΠΎΠ΄Ρ. Π‘ΠΈΠ½ΡΠ΅Π· Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΠΎΡΡΡΠ΅ΡΡΠ²Π»Π΅Π½ Π½Π° ΠΎΡΠ½ΠΎΠ²Π°Π½ΠΈΠΈ ΡΠΏΡΠΎΡΠ΅Π½Π½ΠΎΠΉ Π²Π΅ΡΡΠΈΠΈ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΠΠ§Π-ΡΠΈΠ»ΡΡΡΠ°ΡΠΈΠΈ (Chirp Scaling Algorithm).Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ. Π‘ΠΈΠ½ΡΠ΅Π·ΠΈΡΠΎΠ²Π°Π½ ΠΏΡΠΎΡΡΠΎΠΉ Π°Π»Π³ΠΎΡΠΈΡΠΌ, ΡΠ²Π»ΡΡΡΠΈΠΉΡΡ ΠΌΠΎΠ΄ΠΈΡΠΈΠΊΠ°ΡΠΈΠ΅ΠΉ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° "Π·Π°ΠΌΠΊΠΎΠ²ΠΎΠ³ΠΎ ΠΊΠ°ΠΌΠ½Ρ".ΠΠ»Π³ΠΎΡΠΈΡΠΌ ΠΎΡΠ½ΠΎΠ²Π°Π½ Π½Π° ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠΈ Π±ΡΡΡΡΡΡ
ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠΉ Π€ΡΡΡΠ΅ ΠΈ ΠΏΠΎΡΠ»Π΅ΠΌΠ΅Π½ΡΠ½ΡΡ
ΠΌΠ°ΡΡΠΈΡΠ½ΡΡ
ΡΠΌΠ½ΠΎΠΆΠ΅Π½ΠΈΠΉ. Π Π°Π»Π³ΠΎΡΠΈΡΠΌΠ΅ Π½Π΅ ΠΏΡΠΈΠΌΠ΅Π½ΡΡΡΡΡ ΠΌΠ΅ΡΠΎΠ΄Ρ ΠΈΠ½ΡΠ΅ΡΠΏΠΎΠ»ΡΡΠΈΠΈ.ΠΠ°ΠΊΠ»ΡΡΠ΅Π½ΠΈΠ΅. ΠΡΠΎΠ²Π΅ΡΠΊΠ° ΠΊΠ°ΡΠ΅ΡΡΠ²Π° Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΏΠΎΠ΄ΡΠ²Π΅ΡΠ΄ΠΈΠ»Π° Π΅Π³ΠΎ Π²ΡΡΠΎΠΊΡΡ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ. ΠΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ ΡΠΌΠ΅Π½ΡΡΠΈΡΡ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²ΠΎ Π²ΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΡ
ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΉ.Π€ΠΈΠ½Π°Π»ΡΠ½ΠΎΠ΅ ΡΠ°Π΄ΠΈΠΎΠ»ΠΎΠΊΠ°ΡΠΈΠΎΠ½Π½ΠΎΠ΅ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠ΅, ΠΏΠΎΠ»ΡΡΠ°Π΅ΠΌΠΎΠ΅ Ρ ΠΏΠΎΠΌΠΎΡΡΡ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ°, ΡΡΡΠΎΠΈΡΡΡ Π²Β ΠΈΡΡΠΈΠ½Π½ΠΎΠΉ Π΄Π΅ΠΊΠ°ΡΡΠΎΠ²ΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΠ΅ ΠΊΠΎΠΎΡΠ΄ΠΈΠ½Π°Ρ. ΠΠ»Π³ΠΎΡΠΈΡΠΌ ΠΌΠΎΠΆΠ΅Ρ Π±ΡΡΡ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ Π΄Π»Ρ ΠΏΠΎΡΡΡΠΎΠ΅Π½ΠΈΡ Π Π‘Π ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ Π΄Π²ΠΈΠΆΡΡΠΈΡ
ΡΡ ΡΠ΅Π»Π΅ΠΉ. ΠΠ°Π½Π½ΡΠΉ Π² ΡΡΠ°ΡΡΠ΅ Π°Π½Π°Π»ΠΈΠ· ΠΏΠΎΠΊΠ°Π·Π°Π», ΡΡΠΎ Π°Π»Π³ΠΎΡΠΈΡΠΌ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ ΠΏΠΎΡΡΡΠΎΠΈΡΡ Ρ
ΠΎΡΠΎΡΠΎ ΡΡΠΎΠΊΡΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ΅ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠ΅ Π΄Π²ΠΈΠΆΡΡΠ΅ΠΉΡΡ ΡΠ΅Π»ΠΈ, ΠΊΠΎΠ³Π΄Π° ΠΈΠ½ΡΠ΅ΡΠ²Π°Π» ΡΠΈΠ½ΡΠ΅Π·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π΄ΠΎΡΡΠ°ΡΠΎΡΠ½ΠΎ Π²Π΅Π»ΠΈΠΊ. ΠΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠ΅ Π΄Π²ΠΈΠΆΡΡΠ΅ΠΉΡΡ ΡΠ΅Π»ΠΈ Π²ΡΡΡΡΠ°ΠΈΠ²Π°Π΅ΡΡΡ Π²Π΄ΠΎΠ»Ρ ΠΎΡΡΠ΅Π·ΠΊΠ° ΠΏΡΡΠΌΠΎΠΉ, ΡΠ³ΠΎΠ» Π½Π°ΠΊΠ»ΠΎΠ½Π° ΠΊΠΎΡΠΎΡΠΎΠΉ ΠΏΡΠΎΠΏΠΎΡΡΠΈΠΎΠ½Π°Π»Π΅Π½ ΠΏΡΠΎΠ΅ΠΊΡΠΈΠΈ ΠΎΡΠ½ΠΎΡΠΈΡΠ΅Π»ΡΠ½ΠΎΠΉ ΡΠΊΠΎΡΠΎΡΡΠΈ ΡΠ΅Π»ΠΈ Π½Π° Π»ΠΈΠ½ΠΈΡ Π²ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ. ΠΡΠ΅Π½ΠΊΠ° ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ² ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ ΠΎΠΏΡΠ΅Π΄Π΅Π»ΠΈΡΡ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΡ Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΡ ΡΠ΅Π»ΠΈ
A Signal Processing Algorithm Based on 2D Matched Filtering for SSAR
This study discusses a smart radar antenna scanning mode that combines features of both the sector-scan mode used for conventional radar and the line-scan mode used for synthetic aperture radar (SAR) and achieves an application of the synthetic aperture technique in the conventional sector-scan (mechanically scanned) radar, and we refer to this mode as sector-scan synthetic aperture radar (SSAR). The mathematical model is presented based on the principle of SSAR, and a signal processing algorithm is proposed based on the idea of two-dimensional (2D) matched filtering. The influences of the line-scan range and speed on the SSAR system are analyzed, and the solution to the problem that the target velocity is very high is given. The performance of the proposed algorithm is evaluated through computer simulations. The simulation results indicate that the proposed signal processing algorithm of SSAR can gather the signal energy of targets, thereby improving the ability to detect dim targets
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