Martin Hurtado

Polarimetric Detection of Targets in Heavy Inhomogeneous Clutter

Polarization diversity has proved to be a useful tool for radar detection, especially when discrimination by Doppler effect is not possible. In this paper, we address the problem of improving the performance of polarimetric detectors for targets in heavy inhomogeneous clutter. First, we introduce a new polarimetric radar model that includes the realistic dependence of the clutter reflections on the transmitted signal. Then, we develop a polarimetric detection test that is robust to inhomogeneous clutter. We run this polarimetric test against synthetic and real data to assess its performance in comparison with existing polarimetric detectors. Finally, we propose a polarimetric waveform-design algorithm to further improve the target-detection performance. A numerical analysis is presented to demonstrate the potential performance improvement that can be achieved with this algorithm.

Adaptive Polarized Waveform Design for Target Tracking Based on Sequential Bayesian Inference

In this paper, we develop an adaptive waveform design method for target tracking under a framework of sequential Bayesian inference. We employ polarization diversity to improve the tracking accuracy of a target in the presence of clutter. We use an array of electromagnetic (EM) vector sensors to fully exploit the polarization information of the reflected signal. We apply a sequential Monte Carlo method to track the target parameters, including target position, velocity, and scattering coefficients. This method has the advantage of being able to handle nonlinear and non-Gaussian state and measurement models. The measurements are the output of the sensor array; hence, the information about both the target and its environment is incorporated in the tracking process. We design a new criterion for selecting the optimal waveform one-step ahead based on a recursion of the posterior Cramer-Rao bound. We also derive an algorithm using Monte Carlo integration to compute this criterion and a suboptimal method that reduces the computation cost. Numerical examples demonstrate both the performance of the proposed tracking method and the advantage of the adaptive waveform design scheme.

Target Estimation, Detection, and Tracking

In this article, we explore the adaptive design of radar polarization waveforms for optimal performance, when the statistical properties of the target and clutter are unknown. We focus on a closed-loop system that sequentially estimates the target and clutter scattering parameters, and then uses these estimates to select the polarization of the subsequent waveforms. We demonstrate that the radar system performance is significantly improved when the polarization of the transmitted signal is optimally and adaptively selected to match the polarimetric aspects of the target and environment. In particular, we provide an overview of our recent results showing that the adaptive design of the radar signal polarization enables achieving optimal performance in several operating modes, including detection, estimation, and tracking.

Performance analysis of passive low-grazing-angle source localization in maritime environments using vector sensors

We consider the problem of passive estimation of source direction-of-arrival (DOA) and range using polarization-sensitive sensor arrays, when the receiver array and signal source are near the sea surface. The scenario of interest is the case of low-grazing-angle (LGA) propagation in maritime environments. We present a general polarimetric signal model that takes into account the interference of the direct field with the field reflected from smooth and rough surfaces. Using the Cramer-Rao bound (CRB) and mean-square angular error (MSAE) bound, we analyze the performance of different array configurations, which include an electromagnetic vector sensor (EMVS), a distributed electromagnetic component array (DEMCA), and a distributed electric dipole array (DEDA). By computing these bounds, we show significant advantages in using the proposed diversely polarized arrays compared with the conventional scalar-sensor arrays.

Adaptive OFDM radar for detecting a moving target in urban scenarios

We address the problem of detecting a moving target in an urban canyon using an orthogonal frequency division multiplexing (OFDM) radar and exploiting the multipath reflections. The multipath propagation increases the spatial diversity of the radar system and provides different Doppler shifts over different path. In addition, the use of broadband OFDM signal provides frequency diversity to the system. We develop a parametric measurement model that accounts for the multipath components at multiple frequencies as well as Doppler shifts. Then, we develop a statistical detection test and evaluate its performance characteristics. Based on this, we propose an algorithm to optimally design the spectral weights for the next transmitting waveform. We present a few numerical examples to illustrate our analytical results. We demonstrate the achieved performance improvement due to the exploitation of multipath propagation, OFDM signalling, and adaptive waveform design.

MIMO radar detection of targets in compound-Gaussian clutter

Multiple-input multiple-output (MIMO) radars with widely-separated transmitters and receivers are useful to discriminate a target from clutter using the spatial diversity of the scatterers in the illuminated scene. We consider the detection of targets in compound-Gaussian clutter fitting for example scatterers with heavy-tailed distributions for high-resolution and/or low-grazing-angle radars in the presence of sea or foliage clutter. First, we introduce a data model using the inverse gamma distribution to represent the clutter texture. Then, we apply the parameter-expanded expectation-maximization (PX-EM) algorithm to estimate the clutter texture and speckle, as well as the target parameters.We develop a generalized likelihood ratio (GLR) test target detector using the estimates and show the advantages of MIMO using Monte Carlo simulations.

Enhanced Sparse Bayesian Learning via Statistical Thresholding for Signals in Structured Noise

In this paper we address the problem of sparse signal reconstruction. We propose a new algorithm that determines the signal support applying statistical thresholding to accept the active components of the model. This adaptive decision test is integrated into the sparse Bayesian learning method, improving its accuracy and reducing convergence time. Moreover, we extend the formulation to accept multiple measurement sequences of signal contaminated by structured noise in addition to white noise. We also develop analytical expressions to evaluate the algorithm estimation error as a function of the problem sparsity and indeterminacy. By simulations, we compare the performance of the proposed algorithm with respect to other existing methods. We show a practical application processing real data of a polarimetric radar to separate the target signal from the clutter.

Bat-inspired adaptive design of waveform and trajectory for radar

We propose to design jointly the waveform and trajectory of a radar mounted on a moving platform, in order to improve the system performance for tracking maneuvering targets. Inspired by bats, we develop an adaptive algorithm that chooses the optimal pulse repetition interval (PRI) and path of the radar. Our method automatically schedules a low PRI when it recognizes that the target executes a maneuvering action. Simultaneously, it selects the radar trajectory which provides the best estimation of the target parameters. We derive our approach under a framework of sequential Bayesian filtering and implement it with a particle filter. We consider a library of target state models associated with different PRI values and use multiple model to schedule the optimal PRI. We apply the posterior Cramer-Rao bound to measure the system performance and decide on the optimal radar path.We demonstrate the advantages of the adaptive radar scheme using numerical examples.

An in-situ method to measure the radiation pattern of a GPS receiving antenna

This paper describes an inexpensive and simple alternative method to measure the radiation pattern of a 1575.42 MHz GPS receiving antenna. The results are compared with those derived from simulations and measurements using the extrapolation method. The agreement between the different techniques is acceptably good. Our method does not require the antenna to be taken out of operation, allowing in-situ measurements. Hence, it provides a means to measure the radiation pattern under real operating conditions and using only a GPS receiver connected to a personal computer to collect data.

Radar detection algorithm for GARCH clutter model

We propose a GARCH model to represent the clutter in radar applications. We fit this model to real sea clutter data and we show that it represents adequately the statistics of the data. Then, we develop a detection test based on this model. Using synthetic and real radar data, we evaluate its performance and we show that the proposed detector offers higher probability of detection for a specified value of probability of false alarm than tests based on Gaussian and Weibull models, especially for low signal to clutter ratios.