{'fraction_non_alphanumeric': 0.024455313472654512, 'fraction_numerical': 0.0, 'mean_word_length': 5.578947368421052, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Procrustes algorithm for multisensor track fusion

The association or "fusion" of multiple-sensor reports allows the generation of a highly accurate description of the environment by enabling efficient compression and processing of otherwise unwieldy quantities of data. Assuming that the observations from each sensor are aligned in feature space and in time, this association procedure may be executed on the basis of how well each sensor's vectors of observations match previously fused tracks. Unfortunately, distance-based algorithms alone do not suffice in those situations where match-assignments are not of an obvious nature (e.g., high target density or high false alarm rate scenarios). Our proposed approach is based on recognizing that, together, the sensors' observations and the fused tracks span a vector subspace whose dimensionality and singularity characteristics can be used to determine the total number of targets appearing across sensors. A properly constrained transformation can then be found which aligns the subspaces spanned individually by the observations and by the fused tracks, yielding the relationship existing between both sets of vectors ("Procrustes Problem"). The global nature of this approach thus enables fusing closely-spaced targets by treating them--in a manner analogous to PDA/JPDA algorithms - as clusters across sensors. Since our particular version of the Procrustes Problem consists basically of a minimization in the Total Least Squares sense, the resulting transformations associate both observations-to-tracks and tracks-to--observations. This means that the number of tracks being updated will increase or decrease depending on the number of targets present, automatically initiating or deleting "fused" tracks as required, without the need of ancillary procedures. In addition, it is implicitly assumed that both the tracker filters' target trajectory models and the sensors' observations are "noisy", yielding an algorithm robust even against maneuvering targets. Finally, owing to the fact that Procrustes Association yields the optimal linear associator, the combined sensor and fused track information minimizes tracking Kalman Filter residuals, hence providing very accurate track updates.