In future, it would be beneficial to analyze the data and identify any features that can lead to target classification based on target’s size or reflectivity. Moreover, the human tongue click signal should be analyzed for moving targets since this work only focused on stationary targets. It would be beneficial to analyze the echoes received in two receivers while observing a moving target or approaching a stationary
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target, as opposed to only one receiver in this research. As the literature studies revealed that the humans approaching target do hear the change in pitch of sound which can lead to better target classification.
It is also essential to combine the features used in this research with any new features to verify if a better classification result can be obtained. This is suggested because, knowingly or unknowingly, human successfully brain process all of these information together while they are performing echolocation.
In future, if capable hardware is available, it would be useful to perform the same tests using bat click waveforms, since they are very similar in structure to human tongue click waveforms. The comparison and contrasts of the features observed by transmitting both signals may lead to valuable information for the purpose of target classification.
In future, after creating a similar dataset to MSTAR, the MI-based ATR can also be implemented using human tongue click waveforms and bat click waveforms, and their performance can be compared to the system developed in this research.
Finally, due to the multidisciplinary nature of this work, it is advisable to advance this research in collaboration with the experts of cognitive science and speech processing. It would also be advisable to investigate how speech processing concepts can be applied to the dataset created using human tongue click waveforms for the purpose of target classification.
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APPENDIX A
EXPERIMENTAL AND LABORATORY SETUP
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Figure 90: Metal sphere on a baffle stand
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Figure 92: Plastic box on a baffle stand
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