Anti-jamming techniques for multichannel SAR imaging

© The Institution of Engineering and Technology 2006 IEE Proceedings online no. 20045090An airborne broadband jammer present in the mainbeam of a synthetic aperture radar (SAR) can potentially destroy a large region of the SAR image. In addition to this, multipath reflections from the ground, known as hot-clutter or terrain scattered interference will add a non-stationary interference component to the image. The goal of interference suppression for SAR is to successfully suppress these interferences while not significantly effecting the image quality by blurring, reducing the resolution or raising the sidelobe level. The paper provides an analysis of the degradation from hot-clutter, the limited restoration that multichannel imaging and slow-time space time adaptive processing (STAP) can provide and how fast-time STAP can improve the final image quality.L. Rosenberg and D. Gra

Anti-jamming techniques for multichannel SAR imaging

An airborne broadband jammer present in the mainbeam of a Synthetic Aperture Radar (SAR) can potentially destroy a large region of the SAR image. In addition to this, multipath reflections from the ground, known as hotclutter or terrain scattered interference will add a non-stationary interference component to the image. The goal of interference suppression for SAR is to successfully suppress these interferences while not significantly effecting the image quality by blurring, reducing the resolution or raising the sidelobe level. This paper provides an analysis of the degradation from hot-clutter, the limited restoration that slow-time Space Time Adaptive Processing (STAP) can provide and how fast-time STAP can improve the final image.Luke Rosenberg and Doug Gra

Robust interference suppression for multichannel SAR

Forming a Synthetic Aperture Radar (SAR) image while suppressing a broadband jammer can potentially destroy large regions of the image. In addition to this, multipath reflections from the ground, known as hot-clutter or terrain scattered interference will add a non-stationary interference component to the image. The goal of interference suppression for SAR is to successfully suppress these interferences while not significantly effecting the image quality by blurring, reducing the resolution or raising the side-lobe level. Using multiple antennas on a SAR provides spatial degrees of freedom and allows for adaptive beamforming to suppress the jammer signals. This paper presents two constrained spatial techniques which reduce the interference level without significantly effecting the image quality

Fast-time filtering with multichannel SAR

Large regions of a Synthetic Aperture Radar (SAR) image can potentially be destroyed by an airborne broadband jammer. Jammer components include both the direct-path and multipath reflections from the ground, known as hotclutter or terrain scattered interference. Using multiple antennas on a SAR provides spatial degrees of freedomand allows for beamforming to reject the direct-path signal. However, to effectively suppress non-stationary hot-clutter components, fast-time taps fromwithin a pulse have shown to be effective for airborne radar, [1]-[2]. The goal of interference suppression for SAR is to successfully suppress these interferences while not significantly effecting the image quality by blurring, reducing the resolution or raising the side-lobe level. This paper looks at two fast-time STAP algorithms, the Minimum Variance Distortionless Response (MVDR) and the Generalised Sidelobe Canceller (GSC) to study the effect of non-stationary interference suppression for SAR images.Luke Rosenberg and Doug Gra

MIMO radar space–time adaptive processing using prolate spheroidal wave functions

In the traditional transmitting beamforming radar system, the transmitting antennas send coherent waveforms which form a highly focused beam. In the multiple-input multiple-output (MIMO) radar system, the transmitter sends noncoherent (possibly orthogonal) broad (possibly omnidirectional) waveforms. These waveforms can be extracted at the receiver by a matched filterbank. The extracted signals can be used to obtain more diversity or to improve the spatial resolution for clutter. This paper focuses on space–time adaptive processing (STAP) for MIMO radar systems which improves the spatial resolution for clutter. With a slight modification, STAP methods developed originally for the single-input multiple-output (SIMO) radar (conventional radar) can also be used in MIMO radar. However, in the MIMO radar, the rank of the jammer-and-clutter subspace becomes very large, especially the jammer subspace. It affects both the complexity and the convergence of the STAP algorithm. In this paper, the clutter space and its rank in the MIMO radar are explored. By using the geometry of the problem rather than data, the clutter subspace can be represented using prolate spheroidal wave functions (PSWF). A new STAP algorithm is also proposed. It computes the clutter space using the PSWF and utilizes the block-diagonal property of the jammer covariance matrix. Because of fully utilizing the geometry and the structure of the covariance matrix, the method has very good SINR performance and low computational complexity

Autonomous time-frequency cropping and feature-extraction algorithms for classification of LPI radar modulations

Three autonomous cropping and feature extraction algorithms are examined that can be used for classification of low probability of intercept radar modulations using time-frequency (T-F) images. The first approach, Erosion Dilation Adaptive Binarization (EDAB), uses erosion and a new adaptive threshold binarization algorithm embedded within a recursive dilation process to determine the modulation energy centroid (radar's carrier frequency) and properly place a fixed-width cropping window. The second approach, Marginal Frequency Adaptive Binarization (MFAB), uses the marginal frequency distribution and the adaptive threshold binarization algorithm to determine the start and stop frequencies of the modulation energy to locate and adapt the size of the cropping window. The third approach, Fast Image Filtering, uses the fast Fourier transform and a Gaussian lowpass filter to isolate the modulation energy. The modulation is then cropped from the original T-F image and the adaptive binarization algorithm is used again to compute a binary feature vector for input into a classification network. The binary feature vector allows the image detail to be preserved without overwhelming the classification network that follows. A multi-layer perceptron and a radial basis function network are used for classification and the results are compared. Classification results for nine simulated radar modulations are shown to demonstrate the three feature-extraction approaches and quantify the performance of the algorithms. It is shown that the best results are obtained using the Choi-Williams distribution followed by the MFAB algorithm and a multi-layer perceptron. This setup produced an overall percent correct classification (Pcc) of 87.2% for testing with noise variation and 77.8% for testing with modulation variation. In an operational context, the ability to process and classify LPI signals autonomously allows the operator in the field to receive real-time results.http://archive.org/details/autonomoustimefr10945270

Reduced rank interference suppression for multichannel SAR

Large regions of a Synthetic Aperture Radar (SAR) image can potentially be destroyed by an airborne broadband jammer. Jammer components include both the direct-path and multipath reflections from the ground, known as hot-clutter (HC) or terrain scattered interference. Using multiple antennas on a SAR provides spatial degrees of freedom and allows for beamforming to reject the direct-path signal. Previous studies have shown that derivative constraints when combined with fast-time taps can suppress HC while maintaining a reasonable SAR image. This approach however requires an expensive matrix inverse and may not be implementable in real time. This paper therefore presents a fast-time Space Time Adaptive Processing (STAP) algorithm with a reduced rank constrained Generalised Sidelobe Canceller (GSC).Luke Rosenberg, Doug Grayhttp://www.dlr.de/hr/Portaldata/32/Resources/dokumente/eusar/EUSAR2006-Final-Conference-Program-2006-May-05.pd

Fast-time STAP performance in pre and post range processing adaption as applied to multichannel SAR

Hot-clutter cancellation using fast-time Space Time Adaptive Processing (STAP) can occur either pre or post range processing (RP) and to date, there has not been a direct comparison on which method offers the best results. This paper provides an analytic comparison which is verified with simulation and aims to provide insight into the location of the adaptive filter which would provide the best hot-clutter suppression. The covariance models are tested with signal models used in a multichannel Synthetic Aperture Radar (SAR)

Constrained fast-time STAP techniques for interference suppression in multichannel SAR

Forming a Synthetic Aperture Radar (SAR) image while suppressing an airborne broadband jammer can potentially destroy large regions of the image. In addition to this, multipath reflections from the ground, known as hotclutter (HC) or terrain scattered interference will add a nonstationary interference component to the image. Using multiple antennas on a SAR provides spatial degrees of freedom and allows for adaptive beamforming to suppress the jammer signals. This paper presents a summary of constrained sub-optimal fasttime Space Time Adaptive Processing (STAP) techniques which reduce the interference level with minimal distortion to the SAR image.Luke Rosenberg and Doug Gra

Radar Signal Processing for Interference Mitigation

It is necessary for radars to suppress interferences to near the noise level to achieve the best performance in target detection and measurements. In this dissertation work, innovative signal processing approaches are proposed to effectively mitigate two of the most common types of interferences: jammers and clutter. Two types of radar systems are considered for developing new signal processing algorithms: phased-array radar and multiple-input multiple-output (MIMO) radar. For phased-array radar, an innovative target-clutter feature-based recognition approach termed as Beam-Doppler Image Feature Recognition (BDIFR) is proposed to detect moving targets in inhomogeneous clutter. Moreover, a new ground moving target detection algorithm is proposed for airborne radar. The essence of this algorithm is to compensate for the ground clutter Doppler shift caused by the moving platform and then to cancel the Doppler-compensated clutter using MTI filters that are commonly used in ground-based radar systems. Without the need of clutter estimation, the new algorithms outperform the conventional Space-Time Adaptive Processing (STAP) algorithm in ground moving target detection in inhomogeneous clutter. For MIMO radar, a time-efficient reduced-dimensional clutter suppression algorithm termed as Reduced-dimension Space-time Adaptive Processing (RSTAP) is proposed to minimize the number of the training samples required for clutter estimation. To deal with highly heterogeneous clutter more effectively, we also proposed a robust deterministic STAP algorithm operating on snapshot-to-snapshot basis. For cancelling jammers in the radar mainlobe direction, an innovative jamming elimination approach is proposed based on coherent MIMO radar adaptive beamforming. When combined with mutual information (MI) based cognitive radar transmit waveform design, this new approach can be used to enable spectrum sharing effectively between radar and wireless communication systems. The proposed interference mitigation approaches are validated by carrying out simulations for typical radar operation scenarios. The advantages of the proposed interference mitigation methods over the existing signal processing techniques are demonstrated both analytically and empirically