Radar data have already proven to be compressible with no significant losses for most of the applications in which it is used. In the framework of information theory, the compressibility of a signal implies that it can be decomposed onto a reduced set of basic elements. Since the same quantity of information is carried by the original signal and its decomposition, it can be deduced that a certain degree of redundancy exists in the explicit representation. According to the theory of compressive sensing (CS), due to this redundancy, it is possible to infer an accurate representation of an unknown compressible signal through a highly incomplete set of measurements. Based on this assumption, this paper proposes a novel method for the focusing of raw data in the framework of radar imaging. The technique presented is introduced as an alternative option to the traditional matched filtering, and it suggests that the new modes of acquisition of data are more efficient in orbital configurations. In this paper, this method is first tested on 1-D simulated signals, and results are discussed. An experiment with synthetic aperture radar (SAR) raw data is also described. Its purpose is to show the potential of CS applied to SAR systems. In particular, we show that an image can be reconstructed, without the loss of resolution, after dropping a large percentage of the received pulses, which would allow the implementation of wide-swath modes without reducing the azimuth resolution.

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A Novel Strategy for Radar Imaging Based on Compressive Sensing

This paper provides principles and applications of the sparse microwave imaging theory and technology. Synthetic aperture radar (SAR) is an important method of modern remote sensing. During decades microwave imaging technology has achieved remarkable progress in the system performance of microwave imaging technology, and at the same time encountered increasing complexity in system implementation. The sparse microwave imaging introduces the sparse signal processing theory to radar imaging to obtain new theory, new system and new methodology of microwave imaging. Based on classical SAR imaging model and fundamental theories of sparse signal processing, we can derive the model of sparse microwave imaging, which is a sparse measurement and recovery problem and can be solved with various algorithms. There exist several fundamental points that must be considered in the efforts of applying sparse signal processing to radar imaging, including sparse representation, measurement matrix construction, unambiguity reconstruction and performance evaluation. Based on these considerations, the sparse signal processing could be successfully applied to radar imaging, and achieve benefits in several aspects, including improvement of image quality, reduction of data amount for sparse scene and enhancement of system performance. The sparse signal processing has also been applied in several specific radar imaging applications.

Sparse microwave imaging: Principles and applications

Synthetic Aperture Radar (SAR) is active and coherent microwave high resolution imaging system, which has the capability to image in all weather and day-night conditions. SAR transmits chirp signals and the received echoes are sampled into In-phase (I) and Quadrature (Q) components, generally referred to as raw SAR data. The various modes of SAR coupled with the high resolution and wide swath requirements result in a huge amount of data, which will easily exceed the on-board storage and downlink bandwidth of a satellite. This paper addresses the compression of the raw SAR data by sampling the signal below Nyquist rate using ideas from Compressed Sensing (CS). Due to the low computational resources available onboard satellite, the idea is to use a simple encoder, with a 2D FFT and a random sampler. Decoding is then based on convex optimization or uses greedy algorithms such as Orthogonal Matching Pursuit (OMP).

Fast Encoding of Synthetic Aperture Radar Raw Data using Compressed Sensing

An efficient method for compressing sampled analog signals in real time, without loss, or at a user-specified rate or distortion level, is described. The present invention is particularly effective for compressing and decompressing high-speed, bandlimited analog signals that are not appropriately or effectively compressed by prior art speech, audio, image, and video compression algorithms due to various limitations of such prior art compression solutions. The present invention's preprocessor apparatus measures one or more signal parameters and, under program control, appropriately modifies the preprocessor input signal to create one or more preprocessor output signals that are more effectively compressed by a follow-on compressor. In many instances, the follow-on compressor operates most effectively when its input signal is at baseband. The compressor creates a stream of compressed data tokens and compression control parameters that represent the original sampled input signal using fewer bits. The decompression subsystem uses a decompressor to decompress the stream of compressed data tokens and compression control parameters. After decompression, the decompressor output signal is processed by a post-processor, which reverses the operations of the preprocessor during compression, generating a postprocessed signal that exactly matches (during lossless compression) or approximates (during lossy compression) the original sampled input signal. Parallel processing implementations of both the compression and decompression subsystems are described that can operate at higher sampling rates when compared to the sampling rates of a single compression or decompression subsystem. In addition to providing the benefits of real-time compression and decompression to a new, general class of sampled data users who previously could not obtain benefits from compression, the present invention also enhances the performance of test and measurement equipment (oscilloscopes, signal generators, spectrum analyzers, logic analyzers, etc.), busses and networks carrying sampled data, and data converters (A/D and D/A converters).

Adaptive compression and decompression of bandlimited signals

An enhancement that reduces the digital interface rate of analog-to-digital (A/D) and digital-to-analog (D/A) converters through the use of compression and decompression is described. Improved A/D converters compressing a sampled version of an A/D converter's analog input signal in real time, thereby significantly decreasing the required bit rate of the A/D converter's digital interface. Similarly, improved D/A converters decrease the required bit rate of the D/A converter's digital interface. D/A converters include a decompressor that decompresses the D/A converter's compressed digital input in real time, prior to conversion to an analog output signal.

Enhanced data converters using compression and decompression

Proposes new algorithms for synthetic aperture radar (SAR) raw data compression and compares the resulting image quality with the quality achieved by commonly used methods. The compression is carried out in time and frequency domain, with statistic, crisp, and fuzzy methods. The algorithms in the time domain lead to high resolution and a good signal-to-noise ratio, but they do not optimize the performance of the compression according to the frequency envelope of the signal power in both range and azimuth directions. The hardware requirements for the compression methods in the frequency domain are significant, but a higher performance is obtained. Even with a data rate of 3 bits/sample, a satisfactory phase accuracy is achieved which is an essential parameter for polarimetric and interferometric applications. Preliminary analysis concerning the suitability of the proposed algorithms for different SAR applications shows that the compression ratio should be adaptively selected according to the specific application. >

A comparison of several algorithms for SAR raw data compression

New wavelet based approaches for efficient data compression of complex SAR images with high reconstruction quality are presented. These approaches utilize either a polar format representation to compress magnitude and phase information of the complex SAR images, separately by different compression schemes, or use a Fourier transform scheme to convert the complex image data format to a real data format before applying the wavelet decomposition technique. In both concepts the phase information of the compressed images is preserved in great extent. Additionally, a concept based on vector quantization is used, taking advantage of the correlation between polarization channels for data compression in multi-polarization channel modes. Therefore, these approaches are suitable for data compression in both interferometric and polarimetric applications. Finally, the quality of the reconstructed images are compared in terms of compression ratio and proved by image quality parameters.

Wavelet based approaches for efficient compression of complex SAR image data

This paper shows a comparison of different SAR (synthetic aperture radar) raw data reduction algorithms as applied to E-SAR data (experimental airborne SAR) and spaceborne ERS1 data. The Block Adaptive Quantizer (BAQ) and a Fuzzy Block Adaptive Quantizer (FBAQ) were selected and analysed. In addition, different algorithms based on a BAQ, the Discrete Cosine Block Adaptive Quantizer (DCT-BAQ), the Walsh-Hadamard Block Adaptive Quantizer (WHT-BAQ) and the Block Adaptive Vector Quantizer (BAVQ) mere examined. Signal-to-distortion noise ratios (SDNR) of 11.69 dB (BAQ), 8.00 dB (FBAQ), 2.09 dB (DCT-BAQ and) and 11.94 dB (BAVQ) for E-SAR data and 8.77 dB (BAQ), 5.17 dB (DCT-BAQ) and 9.56 dB (BAVQ) for ERS1 data for a data rate of 2 bits/sample were achieved with a reduction factor of about 3 for the E-SAR data and 2.5 for ERS1 data. >

U. Benz

Papers

A comparison of several algorithms for SAR raw data compression

Wavelet based approaches for efficient compression of complex SAR image data

A comparison of several algorithms for on-board SAR raw data reduction