Joshua M. Kantor

Airborne GMTI using MIMO techniques

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.

Clutter covariance matrices for GMTI MIMO radar

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.

Joint equalization filters that mitigate waveform-diversity modulation of clutter

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.

Space-time adaptive mismatch processing

Conventional signal processing for GMTI radar (pulse-compression and STAP clutter-cancellation applied sequentially) results in significant losses when pulse-to-pulse waveform diversity is used, because of range-sidelobe modulation. This report describes STAMMP, a method for joint pulse-compression, beamforming, and Doppler processing that mitigates range-sidelobe modulation. The method is partially-adaptive and uses a small number of adaptive degrees of freedom to ensure the filters are robust to radar pointing and calibration errors. SINR is evaluated in a GMTI radar simulation and STAMMP is shown to outperform conventional factored processing approaches.

Mean-Squared-Error Prediction for Bayesian Direction-of-Arrival Estimation

In this article, we study the mean-squared-error performance of Bayesian direction-of-arrival (DOA) estimation in which prior belief about the target location is incorporated into the estimation process. Our primary result is an extension of the method of interval errors (MIE) to the case of maximum a posteriori (MAP) direction-of-arrival estimation. We work in a general framework in which the prior information used in the MAP estimation may not match the actual target distribution. In particular, when the prior is incorrect, the MAP estimator degrades relative to the performance of a MAP estimator with the correct prior. Our methods are able to accurately predict the performance of a MAP estimator in this more general situation. We apply our methods to investigate the sensitivity of MAP direction-of-arrival estimation to mismatches between the chosen prior and the actual angular distribution of the target.

Prior mismatch in Bayesian direction of arrival estimation for sparse arrays

We study the mean-squared-error (MSE) performance of Bayesian direction-of-arrival (DOA) estimation for sparse linear arrays in which prior belief about the target location is incorporated into the estimation process. We utilize a recent extension of the method of interval errors (MIE) to the case of maximum a posteriori (MAP) direction-of-arrival estimation to more accurately predict low-medium MSE values in the presence of prior mismatch. We also develop a misspecified Cramér-Rao bound on MAP estimation that can improve the performance of MIE. We specialize to log-periodic arrays to conduct a notional trade study in which we consider the trade in improved estimation performance potentially possible with larger sparser arrays vs the increased sensitivity to incorrectly specified priors.

Waveform-dependent Bayesian Cramér-Rao angle-estimation bounds and threshold SNR estimates for MIMO radars

Multiple-input multiple-output (MIMO) radars operate by simultaneously transmitting multiple independent waveforms. This facilitates improved angle-estimation performance by enabling the use of sparse antenna arrays without the ambiguities that occur when sparse arrays are used in conventional radars. Angle-estimation performance can be characterized in terms of the local error-performance bound given by the Cramér-Rao bound and in terms of the threshold point given by the SNR at which the estimator deviates significantly from the Cramér-Rao bound. In this paper, we extend results of Bliss, Forsythe, and Richmond on angle-estimation performance as a function of transmit waveform covariance for a MIMO radar. The analysis described in the above work is dependent upon an estimate or test location of a target position. Here, we provide a framework for a Bayesian extension that incorporates knowledge of the priors on the target position probability density. This information affects both the Cramér-Rao bound and the threshold SNR. Consequently, it affects waveform and system optimization.

Minimum entropy autofocus correction of residual range cell migration

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.

Coherent ground mapping of polar format images with applications to high-resolution wide-area SAR imaging

In this article, we consider some approaches to using the polar format algorithm for high-resolution wide-area synthetic aperture radar (SAR) imaging. We will broadly discuss two general approaches to extending the polar format algorithm to produce focused high-resolution imagery over wide areas. First, we will describe a fast backprojection-like algorithm based on coherently mapping polar formatted subapertures to the ground and coherently combining the ground-mapped images. Second, we will discuss an alternative approach to generating high-resolution wide-area imagery, which starts with an initial polar format image and then subsequently refines the image in subpatches in a coherently consistent manner across the image. Central to both methods is a general framework for coherently mapping polar format images to the ground.