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Infrared cone-shaped spatial target recognition based on an improved MKELM.

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This study introduces an improved multiple kernel extreme learning machine (MKELM) for infrared spatial target recognition using noisy radiation intensity data. The method enhances recognition accuracy and robustness in long-range detection scenarios.

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Area of Science:

Infrared target recognition
Machine learning applications
Signal processing

Background:

Long-range infrared detection often yields noisy radiation intensity sequences.
Accurate spatial target recognition is crucial for various applications.
Existing methods struggle with noisy data and limited input types.

Purpose of the Study:

To propose a novel infrared cone-shaped spatial target recognition method.
To address the challenge of noisy radiation intensity sequences in long-range detection.
To improve recognition accuracy and robustness using advanced machine learning.

Main Methods:

Incorporation of Variational Mode Decomposition (VMD) and reconstruction for radiation intensity sequences.
Optimization of parameters using the Whale Optimization Algorithm (WOA).
Development of an improved Multiple Kernel Extreme Learning Machine (MKELM) for target recognition.

Main Results:

The proposed method demonstrates effectiveness on a simulated infrared radiation intensity sequence dataset.
Enhanced recognition accuracy was achieved compared to baseline methods.
The method exhibits improved robustness against noise in the input data.

Conclusions:

The novel MKELM-based approach effectively recognizes infrared cone-shaped targets from noisy sequences.
VMD, WOA, and MKELM integration provides a robust solution for long-range detection challenges.
The findings highlight the potential of this method for practical infrared surveillance systems.