Aerospace and Electronic Systems Magazine July 2018 - 41

Cheng et al.

Figure 10.

Schematic diagram of the training algorithm of the MNN.

3. Close linking of the grid computing theory and fuzzy logic,
allowing processing of many data types, such as uncertain data
and interval data, which is not the case with the classical neural
network

Figure 11.

Superbox generated by the MNN on a spiral data set.

4. Greater efficiency because the grid computing is based only on
addition and subtraction, unlike classical neural network computing that involves complex multiplication operations that are
more time consuming
5. Hardware that is more easily implemented
In this study, the training process of the MNN, i.e., the determination of connection weights between input layer and hidden layer
for given training samples, was conducted in a way that generates a
plurality of similar samples that cover the n-dimensional hyperbox
to achieve the correct classification of samples. A schematic diagram of the training process for a MNN is presented in Figure 10.
After the training phase, the superbox B1 that contains only
the first class of samples was obtained. To get the second class
of samples, the superbox B2 cluster, we swapped the class labels
of two types of samples and repeated the training algorithm. The
constructive morphological neural network (CMNN) training algorithm proposed in this article is shown in Figure 11.
Namely, the proposed algorithm was applied to a spiral data
set. In Figure 11, the plus sign represents the first class, and the dot
represents the first class of the superbox; the green (lighter) background represents the superbox of the second class of samples. In
the training phase, the algorithm achieved convergence at 13th
step and generated 48 superboxes, which covered two classes of
samples. The proposed training algorithm can be easily extended
to most class classification tasks.

ANTIJAMMING TECHNOLOGY IN INFRARED TARGET TRACKING
With the aim of considering all types of interference in the tracking process, a new detection and tracking algorithm based on selflearning is proposed. Therefore, the tracking performance of the
infrared imaging system is improved using target feature information, the related parameters are updated online, and antijamming
JULY 2018

Figure 12.

Stability tracking algorithm based on online learning.

ability is greatly enhanced. This algorithm does not require prior
knowledge about the target, and it can automatically detect changes of target state in the tracking process. Moreover, the proposed
tracking algorithm has stable tracking ability for time-sensitive targets. The tracking process is presented in Figure 12.

IEEE A&E SYSTEMS MAGAZINE

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