The human ear is aware of a wide range of sound signal amplitudes, so signal-based amplitude marking techniques have their own complexity. This article uses Watermarking to insert the message in the audio coverage and its main purpose is to keep this information hidden for others. There are many benchmarks for evaluating insertion and extraction algorithms that, by performing multiple attacks on an algorithm, increase the ability of the method. (Resilience, Transparency and Capacity for Use). Although the LSB method is superior to other encryption techniques, it is highly vulnerable to all kinds of attacks and attacks, including Additive White Gaussian Noise. This article introduces a new idea for Watermarking voice data encryption, based on the LSB method, which follows similar bits with bits instead of pasting information. The message will be in 16-bit samples, given the introduction of the distortion reduction algorithm for the changes we have made to the signal bits for the receiver's awareness, which could be a new way of causing F Of its resistance additive white Gaussian noise and LSB standard will also improve the transparency of the procedure and the method for reducing the capacity has been used. Manuscript profile

With the end of World War II and the rapid advent of war technology, wars have shifted to electronic warfare, and today a nation can win wars that are more powerful in the field of telecommunications and electronic warfare. The purpose of this article is to promote new content in the field of electronic warfare and we try to introduce and introduce new techniques of electronic attacks on victim radar systems. With the exception of false targets created by point or sweep jams, most false targets are created with automatic responders or repeaters. An automatic response generator for producing false targets includes a receiver, a variable delay circuit, a signal generator, a power amplifier, and an antenna. Upon receiving a pulse from the threatening radar, the transponder waits for the time corresponding to the target man's distance to the false target, and then sends a similar echo signal to the radar. . As soon as the pulse is received from the threatening radar, the transponder delays it sufficiently to cause the difference in distance to create a false target. Then, a pulse similar to the RF echo pulse of the radar is returned to it. Manuscript profile

This article compares the quality and complexity of LPC, CELP, and MELP standard audio encoders. These standards are based on linear predictive and are used in sound (speech) processing. These standards are powerful high-quality speech coding methods that provide highly accurate estimates of audio parameters and are widely used in the commercial (mobile) and military (NATO) communications industries. To compare LPC, CELP, and MELP audio encoders in two male and female voice modes and four voice models: quiet, Audio recorded without sound by the microphone, MCE, office, and two noise models 1% and 05% were used. The simulation results show the complexity of MELP is higher than LPC and CELP in terms of both processor and memory requirements. The MELP analyzer requires 72% of its total processing time. This additional memory is, of course, due to the vector quantization tables that MELP uses for the linear spectral frequencies (LSFs) and the Fourier magnitude. Also, according to the quality comparison test using the MOS index, MELP has the highest score, followed by CELP and LPC. Manuscript profile

Using chaff to deflect missile guidance radar or missile seeker is a common and effective defense method in military vessels. To deal with this defensive method, focus on specific characteristics of the target and chaff signals. These features should be able to perform properly in different operating conditions of the radar or different environmental conditions that change the behavior of the radar’s return signals. But there is no feature that can distinguish the target from the target with appropriate accuracy in all conditions. In this article, a structure is presented for detecting chaff and target in a radar and has been able to improve the accuracy of target detection in presence of chaff. Also, to improve the performance of the radar with a cognitive approach, its transmitted waveform is optimally selected and changed at each stage. For this purpose, a feedback neural network with LSTM layers has been used. The general structure of the proposed method uses pre-processing on the received radar signals and extracts symmetry characteristics, Doppler spread and AGCD from it to contain the information for separating the target and chaff. Then, to remove the effect of noise on the features. Finally, these features are used to correctly distinguish the target from the chaff in a feed-forward neural network with fully connected layers. At the end, the effectiveness of this method is compared to the previous methods. It can be seen that the performance of the proposed system has made a significant improvement in accuracy of detection. Manuscript profile