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Spotlight Synthetic Aperture Radar Signal Processing Algorithms

Radar waveform diversity has received considerable attention in recent years due to increasing spectral congestion and the burgeoning capabilities of digital waveform generation. The promise of waveform diversity is far greater utilization of available degrees of freedom to enhance sensing performance and to even facilitate new operating modes. This tutorial provides an overview of this very broad topic, from the basic principles upon which it is founded to the myriad different areas being explored in research for practical sensing applications.

Overview of radar waveform diversity

Digital beamforming (DBF) has long been heralded as the next frontier in phased array technology, and not without reason. The digitization of transmit and receive signals at the element level opens the door to new processing and beamforming schemes and promises to deliver maximum flexibility and unprecedented dynamic range in large systems. However, it is not without inherent technological risks and practical challenges associated with the amount of data to process and the use of less sophisticated transceivers. This paper provides broad overviews of several interrelated aspects of the resulting DBF trade spaces for these systems. In particular, emerging concepts are highlighted for the roles and interconnection of distributed beamforming/processing for fixed and adaptive beamforming. These are then related to digital array calibration mechanisms that can potentially reduce the need for data- and processing-intensive beamforming algorithms.

Digital Phased Arrays: Challenges and Opportunities

Due to constantly increasing demand from commercial communications, defense applications are losing spectrum while still striving to maintain legacy capabilities, not to mention the need for enhanced performance. Consequently, ongoing research is focused on developing multi-function methods to share spectrum between radar and military communication. One approach is to incorporate information-bearing communication symbols into the emitted radar waveforms. However, varying the radar waveform during a coherent processing interval (CPI) causes range sidelobe modulation (RSM) that results in increased residual clutter in the range-Doppler response, thus leading to reduced target visibility. Here a novel approach is proposed to embed information into radar emissions while preserving constant envelope waveforms with good spectral containment. Information sequences are implemented using continuous phase modulation (CPM) and phase-attached to a polyphase-coded frequency-modulated (PCFM) radar waveform, the implementation of which is also derived from CPM. The resulting communication-embedded radar waveforms therefore maintain high power and spectral efficiency. More importantly, the adjustable parameterization of the proposed approach enables direct control of the degree of RSM by trading off bit error rate (BER) and/or data throughput.

A novel approach for embedding communication symbols into physical radar waveforms

Choosing a proper waveform is a critical task for the implementation of multiple-input multiple-output (MIMO) radars. In addition to the general requirements for radar waveforms such as good resolution, low sidelobes, etc, MIMO radar waveforms also should possess good orthogonality. In this paper we give a brief overview of MIMO radar waveforms, which are classified into four categories: (1) time division multiple access (TDMA), (2) frequency division multiple access (FDMA), (3) Doppler division multiple access (DDMA), and (4) code division multiple access (CDMA). A special circulating MIMO waveform is also addressed The properties as well as application limitations of different waveforms are analyzed and compared. Some simulations results are also presented to illustrate the respective performance of different waveforms.

Analysis and comparison of MIMO radar waveforms

The performance of a ground moving target indicator (GMTI) radar is strongly driven by the length of the radar aperture, as longer apertures enable lower minimum detectable velocity (MDV) and better target geolocation. Multiple-input multiple-output (MIMO) techniques can enable the use of long sparse array geometries while avoiding the adverse sidelobe effects typical of such arrays. In 2009 an experiment was conducted at Ft. Devens, MA to collect MIMO GMTI data on instrumented ground targets. A reconfigurable S-band antenna array was programmed to transmit both conventional single-input multiple-output (SIMO) and MIMO waveforms. Analysis of the data indicates improved detection performance with the MIMO techniques compared to the conventional SIMO approach.

Airborne GMTI using MIMO techniques

We examine the clutter covariance matrices for ground moving-target indicator (GMTI) multiple-input, multiple-output (MIMO) radar systems, and, in particular, discuss a potential suboptimal increase in their rank This increase in rank will generically degrade the ability of a MIMO GMTI system to detect slowly moving targets We first give a general theoretical analysis and then focus on simulated and experimental data for MIMO systems employing fast-time random, time-division multiple-access (TDMA), and Doppler-division multiple-access (DDMA) waveforms We will show that for the TDMA/DDMA waveforms the clutter covariance matrix in a given Doppler bin is effectively rank 1 for both the simulated and experimental data

Clutter covariance matrices for GMTI MIMO radar

Moving-target indication radars require advanced signal processing to be able to use pulse-to-pulse waveform diversity. The primary challenge is range-sidelobe modulation (RSM) of clutter. Because range sidelobes differ on each pulse, clutter energy that leaks into range sidelobes cannot be cancelled. We find that higher target SINR can be be obtained by joint design of the pulse-compression filters to obtain impulse responses that match across pulses. The proposed filters can reduce RSM by 5–10 dB compared to low-sidelobe mismatch filters designed separately for each pulse.

http://ieeexplore.ieee.org/iel7/7480715/7485053/07485173.pdf

Joint equalization filters that mitigate waveform-diversity modulation of clutter

In this article, we present a SAR autofocus algorithm that can correct for motion errors exceeding a range-resolution cell. Most traditional autofocus algorithms operate by applying a 1D phase correction in the cross-range dimension of the image spatial frequency domain [1]–[3]. When the motion errors exceed a range-resolution cell, 1D phase-only compensation is not sufficient. We will describe an algorithm that can deal with such errors by using a 2D phase correction. Our algorithm is based on optimizing image focus by minimizing the image entropy.

Minimum entropy autofocus correction of residual range cell migration

In this article, we develop a polar format-based algorithm for recovering synthetic aperture radar (SAR) imagery from sparsely/randomly sampled data in the presence of motion compensation/phase errors. Our method integrates conventional SAR autofocus algorithms into an $\ell _{1}$ norm penalized image recovery problem. We demonstrate the effectiveness of this approach using a real airborne SAR data set covering a large scene.

Joshua M. Kantor

Papers

Airborne GMTI using MIMO techniques

Clutter covariance matrices for GMTI MIMO radar

Joint equalization filters that mitigate waveform-diversity modulation of clutter

Minimum entropy autofocus correction of residual range cell migration

Polar Format-Based Compressive SAR Image Reconstruction With Integrated Autofocus