A Multi-Dimensional Feature Fusion Recognition Method for Space Infrared Dim Targets Based on Fuzzy Comprehensive with Spatio-Temporal Correlation

Abstract

Space infrared (IR) target recognition has always been a key issue in the field of space technology. The imaging distance is long, the target is weak, and the feature discrimination is low, making it difficult to distinguish between high-threat targets and decoys. However, most existing methods ignore the fuzziness of multi-dimensional features, and their performance mainly depends on the accuracy of feature extraction, with certain limitations in handling uncertainty and noise. This article proposes a space IR dim target fusion recognition method, which is based on fuzzy comprehensive of spatio-temporal correlation. First, we obtained multi-dimensional IR features of the target through multi-time and multi-spectral detectors, then we established and calculated the adaptive fuzzy-membership function of the features. Next, we applied the entropy weight method to ascertain the objective fusion weights of each feature and computed the spatially fuzzified fusion judgments for the targets. Finally, the fuzzy comprehensive function was used to perform temporal recursive judgment, and the ultimate fusion recognition result was obtained by integrating the results of each temporal recursive judgment. The simulation and comparative experimental results indicate that the proposed method improved the accuracy and robustness of IR dim target recognition in complex environments. Under ideal conditions, it can achieve an accuracy of 88.0% and a recall of 97.5% for the real target. In addition, this article also analyzes the impact of fusion feature combinations, fusion frame counts, different feature extraction errors, and feature database size on recognition performance. The research in this article can enable space-based IR detection systems to make more accurate and stable decisions, promoting defense capabilities and ensuring space security.

A Space Infrared Dim Target Recognition Algorithm Based on Improved DS Theory and Multi-Dimensional Feature Decision Level Fusion Ensemble Classifier

“…The experimental results show that the recognition accuracy of the method is significantly better than that with a single feature, especially when the SNR of the data is low (when the observation duration is 15 s, the accuracy of the method can still achieve 90%). This gives full play [24,29,31,34,56] with different sample frequencies.…”

“…We evaluate the performance of the proposed method with five classical baseline algorithms: the traditional DST [31], the Murphy method [34], the Gao method [56], the Zhang method [24], and the Zhou method [29]. The Murphy method mainly averages the BPAs generated by multiple features for target discrimination and fuses the average BPAs several times to obtain the final recognition result.…”

“…However, in practical scenarios, the acquisition of feature data is easily disturbed by noise and other factors, resulting in feature data of targets being uncertain, and this uncertainty should be dealt with is still an open problem. To solve it, various theories have been proposed, such as fuzzy set theory [23,24], rough set theory [25], Z numbers theory [26], D-number theory [27], Bayesian theory [28,29], and Dempster-Shafer theory [30,31].…”