9:30, SAM-P4.1
APPLICATION OF STATE SPACE FREQUENCY ESTIMATION TECHNIQUES TO RADAR SYSTEMS
P. GULDEN, M. VOSSIEK, E. STORCK, P. HEIDE
Many applications for FMCW radar systems require the resolution of several closely spaced frequencies. In general the Fast Fourier Transform (FFT) is used for spectrum estimation, despite the inherent resolution problems. To overcome the resolution limitation the state space approach has been proposed in [1-3]. However, most experimental results in [1-3] are based on simulations. This paper points out the differ-ences of the "real world" signal structure to simulated signals, and describes the problems posed by real radar signals. For one of the key steps of the state space algorithm, model order selection, a novel algorithm based on a posteriori analysis is introduced. The feasibility of the new approach is verified with an actual 24 GHz level gauging FMCW radar system. Our approach yields stable and accurate results with a resolution approximately three times higher than the FFT resolution.

9:30, SAM-P4.2
REDUCTION OF INTERFERENCE IN AUTOMOTIVE RADARS USING MULTISCALE WAVELET TRANSFORM
M. SOBHY, E. ELSEHELY
Abstract: A technique is presented to minimise false decisions in automotive radars operating in close proximity. The technique also reduces the requirement on the power of the radar as signals can be detected with very low signal to noise ratios. The signal processing is achieved in real time using a field programmable array.

9:30, SAM-P4.3
DETERMINING THE IMPORTANCE OF LEARNING THE UNDERLYING DYNAMICS OF SEA CLUTTER FOR RADAR TARGET DETECTION
M. COWPER, C. UNSWORTH, B. MULGREW
Existing evidence for and against sea clutter being chaotic and nonlinearly predictable is briefly discussed. Despite the uncertainty surrounding the chaotic nature of sea clutter, and its nonlinear predictability, the purpose of this paper is to examine what the best design criterion is for a nonlinear predictor which is to be used to detect targets against clutter which is known to be chaotic: mean square error performance or capturing the chaotic clutter's underlying dynamics. Single pulse detection analysis using a Swerling I target and chaotic ``clutter'' is carried out using predictor-based detectors in an attempt to determine which criterion is most suitable. The predictor detectors are compared with standard detection strategies.

9:30, SAM-P4.4
SPECTRAL MOMENT ESTIMATION FOR WEATHER RADARS USING A WHITENING TRANSFORMATION ON OVERSAMPLED DATA
S. TORRES, D. ZRNIC
A method for estimation of Doppler spectral moments on pulsed weather radars is presented. This scheme operates on oversampled echoes in range; that is samples of in-phase and quadrature phase components are taken at a rate several times larger than the reciprocal of the transmitted pulse length. The aforementioned radar variables are estimated by suitably combining weighted averages of these oversampled signals in range with usual processing of samples (spaced at pulse repetition time) at a fixed range location. The weights in range are chosen such that the oversampled signals become uncorrelated and consequently the variance of estimates decreases significantly. Because estimates’ errors are inversely proportional to the volume scanning times, it follows that storms can be surveyed much faster than it is possible with current processing methods, or equivalently, for the current volume scanning time, accuracy of estimates can be greatly improved.

9:30, SAM-P4.5
CHARACTERIZATION OF THE SCATTERING CENTERS OF A RADAR TARGET WITH POLARIZATION DIVERSITY USING POLYNOMIAL ROOTING
Y. WANG, J. SAILLARD
In this paper, we propose a new high resolution method with polarization diversity for the characterization of the scattering centers of a radar target, using a stepped-frequency radar system. The proposed method is based on the polynomial rooting technique. It allows to optimally use the information contained in the polarization of the received wave to improve the resolution performance. Moreover, it can estimate the range and the polarization parameters of each scattering center in only one step, contrary to the classical methods where these two parameters are estimated in two separated procedures. Simulation results are presented to show the performance of this algorithm.

9:30, SAM-P4.6
IDENTIFICATION OF GROUND TARGETS FROM SEQUENTIAL HRR RADAR SIGNATURES
X. LIAO, P. RUNKLE, Y. JIAO, L. CARIN
An approach to identifying ground targets from sequential high-range-resolution (HRR) radar signatures is presented. A hidden Markov model (HMM) is employed to model the sequential information contained in multi-aspect target signatures. Dominant range-amplitude features are extracted via RELAX for dimension reduction. A new distance measure is incorporated into the HMM to allow a direct matching operation in the feature domain without requiring interpolation. The approach is applied to the dataset of ten MSTAR targets and is shown to yield an average identification rate of 90.3% using sequential information from 6 degree angular spans.

9:30, SAM-P4.7
ROBUST ALTITUDE ESTIMATION FOR OVER-THE-HORIZON RADAR USING A STATE-SPACE MODEL FOR MULTIPATH FADING
R. ANDERSON, S. KRAUT, J. KROLIK
In previous work, a matched-field estimate of aircraft altitude from multiple over-the-horizon radar dwells was presented. This approach exploits the altitude dependence of direct and surface reflected returns off the aircraft and the relative phase changes of these micro-multipath arrivals across radar dwells. Since this previous approach assumed high dwell-to-dwell predictability, it is sensitive to mismatch between modeled versus observed micro-multipath phase and amplitude changes from dwell-to-dwell. In this paper, a generalized matched-field altitude estimate is presented based on a state-space model that accounts for random ionospheric and target-motion effects which degrade the dwell-to-dwell predictability of target returns. The new formulation results in an efficient, robust recursive maximum likelihood altitude estimate. Simulation and real data results suggest that the proposed technique can achieve an accuracy within 5,000 ft. using 10-20 dwells, even with relatively high levels of uncertainty in modeling of dwell-to-dwell changes in the target return.

9:30, SAM-P4.8
REFRACTIVITY ESTIMATION FROM RADAR CLUTTER BY SEQUENTIAL IMPORTANCE SAMPLING WITH A MARKOV MODEL FOR MICROWAVE PROPAGATION
J. KROLIK, S. VASUDEVAN
This paper addresses the problem of estimating range-varying parameters of the height-dependent index of refraction over the sea surface in order to predict ducted microwave propagation loss. Refractivity estimation is performed using a Markov model for microwave radar clutter returns from the sea surface. Specifically, the parabolic approximation for numerical solution of the wave equation is used to formulate the problem within a non-linear recursive Bayesian state estimation framework. Solution for the conditional expectation of range-varying refractivity, given log-amplitude clutter versus range data, is achieved using a sequential importance sampling technique. Simulation results are presented which demonstrate the ability of this approach to synoptically estimate range-varying refractivity parameters by "through-the-sensor" remote sensing.

9:30, SAM-P4.9
A PARAMETRIC APPROACH TO HOT CLUTTER CANCELLATION
P. PARKER, A. SWINDLEHURST
Many reduced dimension STAP algorithms have been developed for airborne radar applications which rely on a stationary Doppler component of the interference in order to maintain acceptable performance. Two cases where this assumption is violated are when the ground clutter contains intrinsic clutter motion (ICM) and when hot clutter is present. In addition to the non-stationary Doppler component, hot clutter contains non-zero correlations in fast-time (across range bins) as well. This paper will present an algorithm designed to mitigate both ground clutter and hot clutter in the same step using a two dimensional vector autoregressive model to whiten the data in space, fast-time, and slow-time. This is an extension of the Space-Time AutoRegressive (STAR) filter that we have previously proposed. Using a simulated data set for circular array STAP augmented with synthetic hot clutter, we demonstrate that the extensions we present do result in a significant performance increase over the standard STAR filter. In addition we also show that the STAR filters have a narrower clutter notch than the optimized pre-Doppler filter when a finite sample support is used to train the filters.