Detection Of Direct-sequence Spread-spectrum Signals Research Articles

DSSS Signal Detection Based on CNN.

With the wide application of direct sequence spread spectrum (DSSS) signals, the comprehensive performance of DSSS communication systems has been continuously improved, making the electronic reconnaissance link in communication countermeasures more difficult. Electronic reconnaissance technology, as the fundamental means of modern electronic warfare, mainly includes signal detection, recognition, and parameter estimation. At present, research on DSSS detection algorithms is mostly based on the correlation characteristics of DSSS signals, and autocorrelation algorithm is the most mature and widely used method in practical engineering. With the continuous development of deep learning, deep-learning-based methods have gradually been introduced to replace traditional algorithms in the field of signal processing. This paper proposes a spread spectrum signal detection method based on convolutional neural network (CNN). Through experimental analysis, the detection performance of the CNN model proposed in this paper on DSSS signals in various situations has been compared and analyzed with traditional autocorrelation detection methods for different signal-to-noise ratios. The experiments verified the estimation performance of the model in this paper under different signal-to-noise ratios, different spreading code lengths, different spreading code types, and different modulation methods and compared it with the autocorrelation detection algorithm. It was found that the detection performance of the model in this paper was higher than that of the autocorrelation detection method, and the overall performance was improved by 4 dB.

Detection of Direct Sequence Spread Spectrum Signals Based on Deep Learning

Direct spread spectrum communication has the advantages of strong anti-jamming ability and low probability of interception, which plays an essential role in both civil and military communications. The detection of direct sequence spread spectrum (DSSS) signal becomes very difficult because of its low power spectral density. In this paper, the detection of DSSS signals under non-cooperative conditions is carried out based on the classification technology of deep learning. Firstly, a detection scheme based on convolutional neural network (CNN) is proposed, in which the neural network is used to learn the features of DSSS signal and noise automatically without extracting the features in advance. In addition, we also propose a hybrid CNN-correlation (CORR)-based detection scheme in order to reduce the computational complexity. In this scheme, the autocorrelation of the received signal is truncated and used as the input of the neural network for training and inference. A large number of simulation results show that the detection performance of the proposed two schemes is significantly better than the traditional autocorrelation-based detection method in various scenarios.

The case against alphabet awareness for detection of direct-sequence spread-spectrum signals

In this paper we investigate the detection of a direct-sequence spread-spectrum signal with QPSK coded chips, whose spreading sequence is unknown to the detector, and whose spreading code period is longer than the detector observation window. We propose a likelihood-ratio-test detector that takes advantage of knowledge of the signal alphabet, whose complexity grows linearly with the observation window length, and that makes use of multiple antennas. The proposed alphabet-aware detector outperforms multi-antenna alphabet-unaware detectors, especially when the SNR is high and the observation window is small. However, the performance advantage is not large, it diminishes further when the SNR is low, and it comes at the cost of higher computational complexity. In most cases, simpler alphabet-unaware detectors such as the energy detector provide comparable detection performance with less complexity. Due to the i.i.d. signal model this paper utilizes, the results in this work are also applicable to single-carrier unspread QPSK signals, with direct-sequence spread-spectrum signals as a specific case in which the SNR is low.

Adaptive Detection of Direct-Sequence Spread-Spectrum Signals Based on Knowledge-Enhanced Compressive Measurements and Artificial Neural Networks.

The direct-sequence spread-spectrum (DSSS) technique has been widely used in wireless secure communications. In this technique, the baseband signal is spread over a wider bandwidth using pseudo-random sequences to avoid interference or interception. In this paper, the authors propose methods to adaptively detect the DSSS signals based on knowledge-enhanced compressive measurements and artificial neural networks. Compared with the conventional non-compressive detection system, the compressive detection framework can achieve a reasonable balance between detection performance and sampling hardware cost. In contrast to the existing compressive sampling techniques, the proposed methods are shown to enable adaptive measurement kernel design with high efficiency. Through the theoretical analysis and the simulation results, the proposed adaptive compressive detection methods are also demonstrated to provide significantly enhanced detection performance efficiently, compared to their counterpart with the conventional random measurement kernels.

New superregenerative architectures for direct-sequence spread-spectrum communications

The superregenerative receiver has been used for many decades as a low-cost and low-power receiver in short-range narrow-band communications. In this paper, we present two new architectures that make use of the superregeneration principle to achieve noncoherent detection of direct-sequence spread-spectrum signals. The local pseudorandom code generator is clocked by the quench oscillator, making the quench frequency equal to the chip rate. Under this condition, it is possible to take advantage of the characteristic broad reception bandwidth and the pulsating nature of the receiver to filter and despread the signal. The two superregenerative architectures, operating under periodic and pseudorandom quench, respectively, are analyzed and compared. Theoretical predictions are confirmed by experimental results in the ISM band of 2.4 GHz.

Generalized Q-functions for application to noncoherent serial detection of spread-spectrum communication signals

This correspondence considers performance analysis of a particular sequential hypothesis test based on noncoherent correlation from overlapping data, which has application in acquisition for spread-spectrum communications. The main contribution of the correspondence is to define a special function (GQ-function) and to express the false alarm and detection probabilities in terms of these functions. The evaluation of these functions in terms of a truncated series is also discussed and compared with standard techniques. An upper bound of the approximation error is derived to determine the number of terms for a given accuracy. Some examples of application to serial detection of direct-sequence spread-spectrum signals are provided, evidencing the possible reduction of the mean acquisition time allowed by multidwell serial tests.

Robust spreading code detection in multipath

It is demonstrated that the detection of direct-sequence spread-spectrum signals by triple correlation is robust in the presence of multipath. The detection performance is illustrated by means of a typical receiver operating curve.

An efficient direct-sequence signal detector based on Dempster-Shafer theory

The detection of direct-sequence spread-spectrum signals is studied. Consideration is given to the environment of broadband noise jamming with the assumption that jammer state information is unknown. It is shown how the Dempster-Shafer theory can be applied to this problem; in particular, the authors treat the unknown jammer state information as uncertain and use the concept of 'confidence discount' and Dempster's combining rule to derive a heuristic detector for the problem. They show that the resulting detector has a better performance than the hard-decision detector and the soft-decision detector when the jammer is significant. The degradation relative to the soft-decision detector is smaller than the hard-decision detector when the jammer is low.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>