Introduction
There have been a lot of developments in modern warfare in today's era. But very few can be considered as important as detection of one's artillery based on certain conditions. An accurate measure of the enemy's artillery has led to fewer surprises in warfare than earlier. This has also led to a huge amount of improvement in one's own defense. One such type of detection is required in the case of submarine protection.
A Naval mine is a self explosive device placed in water to destroy the surface of the submarines. Mines are deposited and left to wait until they are triggered, Triggering happens when mines are approached or contacted by an enemy vessel; you don't need to bump into mines to set off.
Submarines are set to consistently send the signals,and to receive the signals reflected from the obstacles. This type of detection is very necessary in such an endangering case. Any type of misclassification can lead to a huge loss of life, not including the resources that will be destroyed. Generally, there are so many rocks on the bed of the ocean that they can be easily misclassified as a mine. Moreover, a mine can be easily concealed among the rocks to avoid detection.
So, a very accurate detection of mines is highly necessary for the safety of the submarine's passengers. Even if the vice versa happens , i.e., a rock is detected as a mine, it will still lead to the submarine using evasive measures, leading to loss of resources, which can prove fatal, considering the environment they are in.
The detection of mines were earlier done intuitively with the help of sonar signals. The sonar signals that were returned were manually used to determine whether the surface on which it was reflected upon was a mine or a rock. Now, with the advancement of technology, machine learning can be used for a better detection of mines.
Detection of mines
The data used for the experiments were sonar returns collected from a metal cylinder and a cylindrical shaped rock positioned on a sandy ocean floor. Both targets were approximately 5 ft in length and the impinging pulse was a wide-band linear FM chirp (ka = 55.6). Returns were collected at a range of 10 meters and obtained from the cylinder at aspect angles spanning 90 ° and from the rock at aspect angles spanning 180 °. A set of 208 returns (111 cylinder returns and 97 rock returns) were selected from a total set of 1200 returns on the basis of the strength of the secular return (4.0 to 15.0 dB signal-to-noise ratio).
This processed signals were then divided into 60 frequency bands and the records were normalized.These normalized records depict the strength of the returned signal on each of the frequency band. This saved a lot of time as the strength were now being depicted on a scale of 0 to 1.
This normalization of data also helped in the manual evaluation of the dataset. The raw data collected was recorded in scientific terms and was much more difficult to understand. However, since the data has now been normalized, it is much easier to realize the strength of the signal on each frequency band without even modeling. Still, detection using modeling techniques will still be considered a more viable option given the available technology nowadays.
Approach
The 60 samples of reflected frequencies are given in the data. In each frequency band the strength of the reflected frequency is very low. That indicates that the obstacle through which the reflected frequency received is not a mine.In the same way the strength of the reflected frequency is high when bumped on the some obstacle that can be predicted as a mine.All the frequency band collectively forming a pattern which is similar to a bell shaped curve (normal distribution).
A Regression is applied for the classification sonar returns two undersea targets, a mine and a similarly shaped rock. Logistic Regression achieved a classification 74.5% accuracy as high as on a training set of 145 returns. This performance was better than that of a Decision tree, which was 76.4%. and for improving the accuracy of the training set ,having done the feature engineering using Principle Component Analysis for dimensional reduction.Selected 20 components based on the Cumulative variance ratio and applied Logistic Regression, achieved the accuracy to 80.5% which is better then the previous model with all the features.
Neural networks with one added hidden layer gave a accuracy of 87% on test data, the network was trained with the number of neurons and increasing the epoch and default batch size. Support vector machine with 87% accuracy on test data.
Conclusion
Training and classification of sonar mines and rocks can be done by using machine learning algorithms. Hyper tuning of initial models has to be done for more accurate classification.