Abstract-This paper studies the binary hypothesis test of detecting the presence or absence of a target in a highly cluttered environment by using time reversal. In time reversal, the backscatter of a signal transmitted into a scattering environment is recorded, delayed, energy normalized, and retransmitted through the medium. We consider two versions of the test-target channel frequency response assumed known or unknown-and, for each version, contrast two approaches: conventional detection (where no time reversal occurs) and time reversal detection. This leads to four alternative formulations for which we derive the optimal detector and the generalized likelihood ratio test, when the target channel frequency response is known or unknown, respectively. We derive analytical expressions for the error probabilities and the threshold for all detectors, with the exception of the time reversal generalized likelihood ratio test. Experiments with real-world electromagnetic data for two channels (free space with a target immersed in 20 scatterers and a duct channel) confirm the analytical results and show that time reversal detection provides significant gains over conventional detection. This gain is explained by the empirical distribution or type of the target channel frequency response-richer scattering channels induce types with heavier tails and larger time reversal detection gains.
Abstract-We introduce a new ambiguity function for general parameter estimation problems in curved exponential families. We focus the presentation on passive and active radar and sonar location mechanisms. The new definition is based on the Kullback directed divergence and reflects intrinsic properties of the model. It is independent of any specific algorithms used in the processing of the signals. For the active single target problem, we show that our definition is equivalent to Woodward's radar narrowband ambiguity function. However, the new ambiguity is much broader, handling radar/sonar problems when there are unknown parameters (e.g., unknown power level in active systems), when the signals are random (e.g., passive systems), when the signals are wideband, or when there are model mismatches. We illustrate the new ambiguity in localization problems in multipath channels.
We present in this paper a multiframe Bayesian algorithm for detection and tracking of heavily cluttered rigid bodies with random translational and rotational motion. Monte Carlo simulations with synthetic targets and clutter show that the proposed algorithm achieves substantial performance gains over the common association of a maximum likelihood position estimator and a linearized Kalman-Bucy filter.
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