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The association of target track files established and maintained by physically separated sensors based solely upon metrics data does not provide sufficient performance to meet some weapon system requirements. Association algorithms that match tracks based on similarities in the Fourier power spectra derived from the targets' signatures are routinely employed to improve post-mission confidence in track associations made between airborne IR tracking sensors and ground or ship-based radar frequency (RF) sensors that view common target suites during live exercises. The problem with Fourier techniques is that long viewing times are required to obtain usable power spectral density estimates; our mission scenarios impose a time constraint that is about one order of magnitude less. Faster algorithms are required for real-time embedded interceptor and BMC4I applications. This paper documents preliminary results we have obtained using a prototype, wavelet-based algorithm to automate the rapid, one-to-one association of RF and IR target tracks using features extracted from short signature histories maintained in the track files. The primary physical phenomenon that is exploited by the algorithm is the frequency content in the target signature, which is induced by the target's body dynamics. No a priori knowledge of the expected signature dynamics is assumed. Given a group of targets that are observed by two or more sensors, each sensor independently establishes and maintains its own track files while viewing the targets from physically isolated platforms. The sensors may have different sample rates and may operate in different regions of the RF spectrum; concurrent viewing is not required. The algorithm automatically detects and extracts signature glints as a preprocessing step, saving the glint as a preprocessing step, saving the glint data in separate 'channels' for further processing and employs wavelet shrinkage to attenuate any white noise that may be present in the signature data. Individual sensor track files are treated as separate pattern classes within an adaptive, statistical pattern recognition framework. The class feature vectors are formed from the magnitudes of the wavelet packet crystal coefficients. Clustering transformations are applied to each 'class', Fisher's linear discriminant is employed to minimize the intraclass scatter while maximizing the interclass scatter, and a modified Mahalanobis distance is then used as a metric to quantify the similarity between each possible pair of classes. Evidence is provided that suggests the wavelet packet crystal energies used by the association algorithm may be of some utility in the detection of closely spaced objects. The algorithm and the analyses are discussed within the context of a two-sensor scenario, but the algorithm is equally applicable to multiple sensor applications.
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