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It is difficult for optical correlator to recognize the target with large rotation distortion by using existing filters. To solve this problem, a new neural network model is constructed based on the physical recognition process of planar integrated 2F optical correlator. The new optical filter is optimized by training the neural network. The planar integrated 2F optical correlator can output sharp correlation peaks by using the new filter to recognize targets with arbitrary rotation distortion. Compared with the traditional OTSDF filter, the average increase of correlation peak index is up to 1402.4%, while the recognition performance of distortion invariant is also better than the new NNCRF filter. The simulation and experimental results show that the optical filter designed in this paper can effectively solve the problem of weak recognition ability of optical correlator for rotating distortion targets, especially for large angle rotating distortion targets.

In this paper, an optical correlator which adopts planar integrated 2F structure and optimal tradeoff synthetic discriminant function filter is proposed to improve the system integration and the recognition ability. The specialized digital microlens is designed for the correlator to replace the physical lens. By the theoretical analysis, the structural design conditions and parameters of the correlator are given, and the system volume is 63.1 cm3 which is only half of the planar integrated 4F optical correlator. Moreover, simulations and experimental research works reveal that the correlator can achieve stable recognition to the input object of distortion with scaling (52%, 150%), rotation (-50°, +42°), and existed scaling (72%, 130%) and rotation (-36°, +38°) simultaneously. Theoretical analysis and experimental results indicate that the planar integrated optical correlator with compact structure and high integration has good performance of distortion invariant recognition.

A neural network model is constructed according to 4F optical correlator. New optical filter is obtained by training the neural network. For recognition of distortion targets, the correlation peaks from correlator applying new filter are averagely increased up to 306.7% than applying the OTSDF filter, and correlation peaks are higher, sharper and easier to be distinguished, which indicates optical correlator with new filter have better performance in distortion invariant recognition.