[1] This paper describes estimation of low-altitude atmospheric refractivity from radar sea clutter observations. The vertical structure of the refractive environment is modeled using five parameters, and the horizontal structure is modeled using six parameters. The refractivity model is implemented with and without an a priori constraint on the duct strength, as might be derived from soundings or numerical weather-prediction models. An electromagnetic propagation model maps the refractivity structure into a replica field. Replica fields are compared to the observed clutter using a squared-error objective function. A global search for the 11 environmental parameters is performed using genetic algorithms. The inversion algorithm is implemented on S-band radar sea-clutter data from Wallops Island, Virginia. Reference data are from range-dependent refractivity profiles obtained with a helicopter. The inversion is assessed (1) by comparing the propagation predicted from the radar-inferred refractivity profiles and from the helicopter profiles, (2) by comparing the refractivity parameters from the helicopter soundings to those estimated, and (3) by examining the fit between observed clutter and optimal replica field. This technique could provide near-real-time estimation of ducting effects. In practical implementations it is unlikely that range-dependent soundings would be available. A single sounding is used for evaluating the radar-inferred environmental parameters. When the unconstrained environmental model is used, the ''refractivity-from-clutter,'' the propagation loss generated and the loss from this single sounding, is close within the duct; however, above the duct they differ. Use of the constraint on the duct strength leads to a better match also above the duct.

Matched-field source localization methods that employ deterministic full-wave acoustic propagation models can be seriously degraded due to the presence of random inhomogeneities in the ocean channel. In this paper, a minimum variance (MV) matched-field beamformer is presented that achieves greater robustness to random inhomogeneities in the sound-speed profile between the source and receiver. The proposed modification of the MV beamformer consists of employing multiple linear constraints derived from predicted pressure fields obtained using a set of perturbed sound-speed profiles. In order to investigate the nature of wave-front variations due to random sound-speed perturbations, a normal mode model based on adiabatic and first-order perturbation approximations is examined. The signal wave-front spatial correlation implied by this model suggests that the coherence among modes can remain high even in a fluctuating ocean environment. This in turn implies that the dimension of the signal perturbation constraint space for the MV beamformer can be small for typical soundspeed variations at moderate source ranges. Given the signal constraint space, design of the MV beamformer with sound-speed perturbation constraints is achieved by selecting its quiescent response to maximize the average signal-to-noise ratio gain against spatially uncorrelated noise. This leads to a computationally efficient realization of the beamformer that avoids the need to repeatedly compute perturbed pressure fields. Simulation experiments using a realistic deep-water Pacific Ocean environment are presented, which suggest that robust unambiguous low-frequency source location estimates can be achieved in the presence of mesoscale inhomogeneities given only knowledge of the second-order statistics of the random i'ange-dependent sound-speed profile plus a single environmental measurement at the receiving array.

-An experimental study is performed on electromagnetic time reversal in highly scattering environments, with a particular focus on performance when environmental conditions change. In particular, we consider the case for which there is a mismatch between the Green's function used on the forward measurement and that used for time-reversal inversion. We examine the degradation in the time-reversal image with increasing media mismatch, and consider techniques that mitigate such degradation. The experimental results are also compared with theoretical predictions for time reversal in changing media, with good agreement observed.

Absfmt-This paper presents an approsch for reducing the threshold observation time required to achieve high-resolution localization of multiple broad-band sources. The proposed techniques are based on a space-time statistic called the steered covariance matrix (STCM). The STCM, like the well-known cross-spectral density matrix (CSDM), has asymptotic properties which facilitate h%h-resolutlw source iocalization. In broad-band settings, however. the STCM has the advantage that it can be estimated with much greater statistical stability than the CSDM. In this paper, the STCM Is used in coqjunctbn with minimum variance and linear predktive spectnl estimation to obtain the steered minimum variance (STMV) and steered linear pndition (STLP) methods. Analytical and simulation results are presented which suggest the STMV and STLP methods exhibit lower threshoid observation times than their CSDM-based counterparts.

[1] Estimation of the range-and height-dependent index of refraction over the sea surface facilitates prediction of ducted microwave propagation loss. In this paper, refractivity estimation from radar clutter returns is performed using a Markov state space model for microwave propagation. Specifically, the parabolic approximation for numerical solution of the wave equation is used to formulate the refractivity from clutter (RFC) problem within a nonlinear recursive Bayesian state estimation framework. RFC under this nonlinear state space formulation is more efficient than global fitting of refractivity parameters when the total number of range-varying parameters exceeds the number of basis functions required to represent the height-dependent field at a given range. Moreover, the range-recursive nature of the estimator can be easily adapted to situations where the refractivity modeling changes at discrete ranges, such as at a shoreline. A fast range-recursive solution for obtaining range-varying refractivity is achieved by using sequential importance sampling extensions to state estimation techniques, namely, the forward and Viterbi algorithms. Simulation and real data results from radar clutter collected off Wallops Island, Virginia, are presented which demonstrate the ability of this method to produce propagation loss estimates that compare favorably with ground truth refractivity measurements.

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This paper evaluates the potential for exploiting multipath propagation for improved radar detection of moving ground targets in dense urban environments. In particular, the radar coverage offered by exploiting non-line-of-sight (NLOS) propagation from an airborne radar platform is considered. A quasi-analytical model for NLOS propagation assuming multiple specular reflections off buildings is derived to evaluate radar coverage as a function of target range, aircraft altitude, building height and separation, and relative target position on a street. A statistical characterization of the urban environment is used to compute probabilities of line-ofsight versus non-line-of-sight propagation and surveillance sweep width. Comparison of sweep width for LOS versus at least one NLOS path indicates that NLOS could offer a significant improvement in coverage rate. In essence, NLOS propagation permits radar surveillance at longer ranges with lower altitude radar platforms. In addition to radar coverage, the Cramer-Rao Lower Bound (CRLB) for estimation of target position in a specular multipath environment is derived. The CRLB indicates that, in theory, range accuracy improves with multiple paths and that only three paths are required to jointly estimate target range, position within the street, and street width.

Minimum variance (MV) adaptive beamforming has been widely proposed for matched-field processing because it provides a means of suppressing ambiguous beampattern sidelobes. A difficulty with MV methods, however, is their sensitivity to signal wavefront mismatch. In this work, the performance of three robust MV methods and the Bartlett beamformer is evaluated using vertical array data from the Mediterranean Sea collected by the NATO SACLANT Centre. The three MV methods considered are: 1) the reduced MV beamformer (RMV), 2) the MV beamformer with neighborhood location constraints (MV-NLC), and 3) the MV beamformer with environmental perturbation constraints (MV-EPC). While the Bartlett, RMV, and MV-NLC methods assume the ocean environment is known precisely, the MV-EPC method models the environment as being random with known statistics. Experimental and companion simulation results indicate that for modest environmental uncertainty, the MV-EPC beamformer achieves a higher probability of correct localization and better sidelobe performance than the other three methods.