Unnikrishna Pillai

Signal decomposition for wind turbine clutter mitigation

This paper addresses the problem of dynamic clutter mitigation by focusing on the mitigation of the wind turbine clutter from the radar data. The basis pursuit and morphological component analysis approach are used to decompose the radar returns into the sum of oscillatory and transient components. The success of the morphological component analysis rely on sparsity, thus different transform domains needs to be identified correctly to represent each component sparsely. The method is illustrated on a radar data collected from a small custom built radar system to show the success of the proposed algorithm for wind turbine clutter mitigation.

Waveforms for simultaneous SAR and GMTI

In this paper we discuss the frequency-jump burst (FJB) waveform as a candidate for performing joint synthetic aperture radar (SAR) imaging and ground moving target indication (GMTI). We introduce the signal model for simulating SAR data and the method for image formation. The ambiguity function (AF) is used as an analysis tool for determining whether the waveform under investigation has the desired resolution. Image artefacts resulting from target motion are discussed and the degradations are quantified.

Dynamic Clutter Mitigation Using Sparse Optimization

The impact of wind farms on radar systems, such as air traf-fc control (ATC), air defense (AD), and weather radars, is a signifcant issue, especially considering that demand for wind power and wind farms is increasing dramatically. According to the American Wind Energy Association, more than 51,630 MW of wind power capacity are installed in the United States [1]. There are also more than 8,900 MW of wind power capacity under construction, involving more than 90 separate projects spanning 31 states and Puerto Rico [2].

Distribution of multi-look SAR and ATI using two phase centers

This paper derives a closed form expression for the distribution of the multi-look phase variable from elementary considerations for zero mean Complex Gaussian data, and further uses the results to justify the thresholding often used in along track interferometry (ATI). Further the target in clutter (TIC) problem is addressed as a hypothesis testing problem and the effect of the environment and target velocity on ATI phase are characterized along with the probability of detection/false alarm as a function of target to clutter ratio and its velocity.

Sparsity-based methods for interrupted radar data reconstruction

Missing radar data may be reconstructed by using the structure present in surrounding data to make intelligent estimates of values at missing locations. We formulate the interrupted radar data scenario as an l1-regularized least squares problem, and take advantage of the radar datas demonstrated sparsity in the discrete Fourier domain. Applying the split-variable augmented Lagrangian technique results in an iterative algorithm consisting of two alternating minimizations. The fast algorithm avoids explicit linear inverse solutions, and demonstrates good phase history reconstruction and improved imaging irrespective of the structure of the data loss. Experimental results are presented for synthetic aperture radar (SAR) image formation; however, the approach may also be used with other types of radar data.

Exploiting temporal proximity for moving target identification using bistatic/passive SAR

Geo-locating moving targets using sequential imaging while exploiting their spatio-temporal proximity is addressed in this paper for bistatic Linear Frequency Modulation (LFM) and Orthogonal Frequency-Division Multiplexing (OFDM) scenes. The approach consists of three major steps (i) Synthetic Aperture Radar/Along-Track Interferometry (SAR/ATI) imaging for moving target detection; (ii) target velocity estimation from ATI phase, and (iii) exploiting spatio-temporal connectivity using sub-aperture outputs for target geo-locations. The bistatic OFDM is more problematic than the bistatic LFM case because of the poor detection performance due to the presence of dominant target sidelobes. The results are demonstrated for various moving target sets in moderate clutter.

Statistical performance analysis for GMTI using ATI phase distribution

Synthetic aperture radar (SAR) imaging is often used to image an area using airborne platforms that generate a large aperture by virtue of the platform motion. Large apertures generate a large synthetic array providing fine cross-range resolution, and together with wide bandwidth waveforms that provide high range resolution, fine resolution images can be generated. SAR algorithms make use of coherent phase compensation from various pulses for focusing and the technique works exceedingly well for scenes containing stationary scattering centers. When moving targets are present, their images are smeared and shifted due to the motion, and to take advantage of this shift, nearby receiver plates are used to form multiple SAR images and together with along track interferometry (ATI), it generates a phase factor that can be used to detect moving target presence. This paper examines the distribution of the phase variable used in ATI for zero mean Complex Gaussian clutter/target data, and uses the results to address the target in clutter problem as a hypothesis testing problem to compute the probability of detection/false alarm as a function of target to clutter ratio and its velocity.

Moving target geolocation in bistatic/passive SAR images using ATI

In an effort to image and geolocate moving targets using transmitters of opportunity (Passive radar), three situations - Monostatic LFM, bistatic LFM and bistatic OFDM are considered. Here OFDM waveform plays the role of an ideal LFM waveform that has superior resolution. In the case of moving targets, the shift in their imaged locations by virtue of their motion are accounted for in terms of target radial velocity. This allows us to correctly geolocate the moving targets both in the monostatic and bistatic cases knowing the target signature location along with its radial velocity. The results are verified using realistic multiple moving targets in moderate clutter to account for various moving target scenarios.

Target geolocation in Gotcha data using panoramic processing

Any signal processing methodology when blindly applied to realistic data sets generates a significant number of false targets along with estimates for the true moving targets. In an effort to isolate the true movers from the false targets, a new approach exploiting spatio-temporal connectivity in addition to signal processing algorithms involving imaging and interferometry is proposed here to geolocate the movers in a measured data set.

Geolocation of moving targets in Gotcha data using multimode processing

A new methodology for geolocating slow moving targets using SAR images at multiple phase centers is shown here along with methods to minimize false targets. In an effort to isolate the true movers from the false targets, a new approach exploiting spatio-temporal connectivity in addition to signal processing algorithms involving imaging and interferometry is proposed here to geolocate the movers in a measured data set.