Time Estimation of Superimposed Coherent Multipath Signals using the EM Algorithm for Global Positioning System

Due to the broad bandwidth nature of the direct sequence spread spectrum (DSSS) signal, the application of narrowband interference (NBI) cancelation algorithms based on the Nyquist‐Shannon sampling theory are limited by the high sampling rate. To solve the sampling and processing difficulty in the traditional DSSS communication interference cancelation method, we used the compressive sensing (CS) to reduce the sampling rate of the DSSS communication system, and further proposed a compressed domain NBI elimination method in this study. We proved that NBI with a certain bandwidth and DSSS signal are both sparse in specific dictionaries and proposed the corresponding sparse dictionary construction method. Detailed analysis of the compressed domain features of NBI and the DSSS signal demonstrated that it is possible to extract the strong NBI components from the compressed measurements. Two compressed domain NBI cancelation algorithms were proposed based on this idea. The extracted NBI components were filtered out from the compressed measurements directly in the first algorithm, and then we achieved the DSSS signal demodulation in compressed domain using the separated DSSS signal components with the orthogonal matching pursuit (OMP) algorithm. A proposed extraction filter block sparse Bayesian learning (EFBSBL) framework algorithm was utilized to estimate the NBI from the compressed measurements in the second algorithm. The estimated NBI was eliminated from the received signal, and then we recompressed the DSSS signal after NBI cancelation and realized the DSSS signal demodulation in compressed domain with the OMP algorithm. Simulation results support our theoretical analysis results and reveal that the two proposed algorithms are both effective for NBI cancelation and significant interference cancelation performance are achieved.

Direct sequence spread spectrum (DSSS) communication has been extensively adopted in military and civilian fields because of its high processing gain, anti-jamming capability and low probability of interception (LPI). In this paper, non-cooperative interception of DSSS signal in multiple signals environment is presented. The proposed algorithm is based on the fluctuations of autocorrelation, and also banks upon proper thresholding criteria. This methodology comes out to be a robust practical method for blind detection of DSSS signals in a dense multiple signals environment. In the proposed approach, firstly DSSS signals are blindly detected by leveraging the variations in autocorrelation of the received signal. Then, the presence of desired DSSS signal is confirmed when correlation peaks cross the threshold at multiple positions after a fixed period. In a real-time environment, signals other than spread spectrum with much better signal-to-noise ratio (SNR) can also co-exist. Among these multiple signals, the high SNR signals disturb the threshold selection criteria for detection. Consequently, this leads to erroneous or miss detection of the DSSS signal. The proposed scheme aims to reduce the effect of better SNR signals on the blind detection of DSSS signal. In order to validate the proposed algorithm, QPSK and 16-QAM signals are considered to co-exist with the desired DSSS signal. Analytical and numerical analysis signify the efficiency of the proposed algorithm with acceptable computational complexity, making it suitable for real-time practical systems.

According to the demand of spread spectrum signal processing in lower SNR conditions, a novel blind estimation algorithm of the pseudo-noise (PN) sequence for the direct sequence spread spectrum (DSSS) signal is proposed based on the similarities among the DSSS signals and the eigenanalysis technique. At first, through segmentation processing and average cross-correlation calculation, the starting symbol of PN sequence is determined. Next, by utilizing the eigenanalysis technique PN sequence can be estimated accurately without any information of the DSSS signal. Simulation results show that compared with the existing algorithms under the same condition, the proposed algorithm not only overcomes the partial-encode problem, but also improves estimation performance by 2dB. Therefore, the proposed algorithm achieves an accurate estimation of PN sequence in lower SNR conditions.

Soft synchronization of direct sequence spread spectrum (DSSS) signals introduces the capability of despreading the signals with improving estimates of the spreading code. The technique presented here exploits the eigenstructure of a frequency-channelized DSSS signal to estimate the code. The estimate improves steadily with time, resulting in an improving estimate of the message signal. This allows commencement of despreading at an early stage when the code-estimate is still imperfect. This paper presents the development of the algorithm for a single user environment under infinite time-average assumptions. The performance of the technique under finite time-average conditions is demonstrated through computer simulations for single user as well as multi-user environments.

In this paper, we investigate the possibility of improving the blind detection performance of direct sequence spread spectrum (DSSS) signals for cognitive radios (CRs). We consider a scenario where a wide range of frequency spectrum needs to be monitored by a single CR, and the presence of spread spectrum signals need to be identified reliably in a cost effective manner. We employ compressive sensing to achieve realistic sensing time without imposing excessive sampling-rate requirements on the analog-to-digital converter of the CR. We assume that the number, the center frequencies, and the spreading codes of the DSSS signals are unknown, but only the spread signal's bandwidth is known. We propose a three-step algorithm for the CR. First, the power spectral density (PSD) of the wideband spectrum is estimated using compressed samples, and then detection is performed by thresholding to detect spectrum occupancy based on the estimated PSD. In the third step, knowledge of the spread signal's bandwidth, and the estimated PSD are used to perform fusion by looking at spectrum occupancy at adjacent frequency bins using a sliding window. We present some simulation results illustrating the performance gain in detection achieved by introducing the fusion step. This is a useful result since it allows us to detect with improved performance the presence of multiple DSSS signals distributed over a very wideband spectrum, without requiring the knowledge of each signal's spreading code.

W399 the development of underwater acoustic communications (UWAC), jamming methods for UWAC becomes much more attentive in communication countermeasure system. Direct sequence spread spectrum (DSSS) signal as a way of reliable covert communications is widely used for UWA channels. In this paper, single-tone jamming, narrowband jamming and correlated jamming based on PN code PSK modulation for UWA DSSS communication system are compared under different signal-to-noise ratio first, which assumes that the jammer knows perfect parameters in DSSS signal. Besides, performance of the above three jamming methods are compared by simulations under the blind estimates of carrier frequency, chip length, period of PN code and PN code itself, which demonstrates the influence of the estimation error on these jamming methods.

In this paper, we consider the problem of Doppler tracking and compensation for a direct sequence spread spectrum (DSSS) signal in underwater acoustic (UWA) communication. Since the dynamic property of the UWA channel and the long duration of DSSS signals result in significant Doppler spread that severely distorts the propagated signal, Doppler tracking and compensation are required. Based on the ultra-wideband property of UWA signal, the Doppler spread not only results in the frequency shift, but also changes the signal duration. Therefore, the accurate estimation of the signal expansion/compression in the time domain can reflect the Doppler spread. Accordingly, we present a Doppler tracking and compensation algorithm for a DSSS signal operating on the correlation output of passband signal at a symbol-by-symbol basis. Note that the carrier frequency of UWA communication is around several kilohertz, and thus the time delay estimation can be performed on the passband to improve the accuracy. Furthermore, the prior information of Doppler limit is used to refine the resolution of delay estimation and achieve sequential estimation. To compensate the correlation magnitude distortion induced by the velocity variation, the local reference signal is selected adaptively based on the filtered Doppler factor. Simulation results demonstrate that the proposed passband Doppler tracking algorithm achieves a superior performance compared with the conventional receiver.

In this paper, a computationally efficient algorithm is presented for joint maximum likelihood (ML) timing and carrier phase estimation of OFDM systems employing M-PSK modulation scheme with additive Gaussian noise, based on the expectation-maximization (EM) algorithm. A nondata-aided (NDA) scheme is considered for the joint timing and phase synchronizer which maximizes the low SNR limit of the likelihood function averaged over the M-PSK signal constellation. For this, an EM algorithm is derived which estimates the timing offset and the phase rotations of each subcarrier iteratively and which converges to the true ML estimation of the unknown timing and phase. It is shown that the algorithm becomes independent of the signal-to-noise ratio for both low and high SNR cases. The algorithm is applied to the QPSK modulated OFDM systems and it is concluded that for SNR values greater than 10 dB the convergence is achieved in first iteration and for SNR values less than 10 dB, at most in three iterations. It is also concluded that the convergence is independent of the initial starting points.

Direct-Sequence Spread Spectrum (DS-SS) signals are widely used in modern communications. The detection and parameter estimation of DS-SS signals in non- cooperative context have been paid many attentions. Up to now, most of the proposed methods consider only short-code DS-SS signals, and long-code DS-SS signals have hardly any been studied. In this paper, a method for blind estimation of the symbol period of a long-code DS-SS signal is proposed. The proposed method is based on further processing of the autocorrelation estimator. Simulation results are provided to illustrate the approach.

The detection and parameter estimation of spread spectrum signals under non-cooperative conditions are key problems to which more attentions are paid in the field of signal processing. A cyclic-cumulant based DS-SS (direct sequence spread spectrum) signal detection scheme is developed in this paper by extending the asymptotically optimal chi-squared test for the presence of cyclostationarity to DS-SS signal detection. The explicit formula of the second-order cyclic-cumulant is derived, and the influence of time interval on the cycle-frequency estimation from the cyclic-cumulant is analyzed in detail. On the base of this, a new cyclic-cumulant based DS-SS signal parameter estimation method is proposed, which can perform blind detection and blind parameter estimation without a priori knowledge of the signal such as pseudorandom sequence and frequencies. Moreover, the method also avoids multi-dimensional searching in parameter estimation. Simulation results have proved the validity of the method.

The Global Positioning System (GPS) has been used in a variety of applications for which new and more restrictive requirements in the design of the receiving antenna have been introduced. In particular, for high-precision GPS applications, a receiving antenna with superior rejection to multipath signals is required. Furthermore, in order to satisfy the demanded precision and reliability, a high performance GPS antenna must be capable of operation at the two GPS frequencies (1.57542 GHz and 1.2276 GHz). One effective design (see Boccia, L. et al., Microwave and Optical Technology Letters, 2001) demonstrates that consistent multipath mitigation can be obtained with a single frequency shorted annular patch. A stacked shorted elliptical patch, operating at the two GPS bands, is presented. The antenna combines the shorted ring radiation properties, which have proved to comply adequately with multipath mitigation requirements, with dual frequency operations, resulting in a compact high performance dual frequency GPS antenna.

The complex electromagnetic environment will limit the efficacy of communication equipment. It is critical to construct a complex electromagnetic environment to test communication equipment in order to maximize its capability. One of the most important methods for constructing a complex electromagnetic environment is signal reconstruction. This paper proposes a VAE‐GAN‐based method for reconstructing direct sequence spread spectrum (DSSS) signals. In this method, the deep residual shrinkage network (DRSN) and self‐attention mechanism are added to the encoder and discriminator of VAE‐GAN. In feature learning, the DRSNs can reduce the redundant information caused by noise in the collected signal. The self‐attention mechanism can establish the long‐distance dependence between the input sequences, making it easier for the network to learn the samples’ pseudonoise (PN) sequence features. In addition, feature loss is applied to the encoder and generator to improve network stability during training. The results of the experiments indicate that this method can reconstruct DSSS signals with the characteristics of the target signal.

At present, the blind detection of DSSS (direct sequence spread spectrum) signals is mainly based on the cyclostationarity of the signals and uses the statistics analysis of the second or higher order eigenvalues as a cut-in sticking point for resolving the problem, it is quite easily influenced by the low signal to noise ratio and restricted by the implementation of the algorithm. In order to resolve the problem of the blind detection, the paper proposes a blind spread spectrum pseudo random sequence detection method: multi-chip allied correlation detection, and a blind chip synchronization method: multi-codeword correlation smoothing match. The proposals of the paper resolve the bottleneck problems in the blind detection of DSSS signals: the blind spread spectrum pseudo random sequence estimation and the chip synchronization search in the context of low signal to noise ratio. The proposed methods not only avoid the need of the second or higher order processing of many blind detection methods, reduce the complexity of the blind detection of DSSS signals greatly, but also overcome the shortcoming of many blind detection methods while dealing with the spread spectrum transmission far below the noise level. The paper may be a foundation for achieving the detection of the DSSS signals in non-cooperative context.

Security has long been a challenging problem in wireless networks mainly due to its broadcast nature of the communication. It opens up simple and yet effective measures to thwart useful communications between legitimate radios. Spread spectrum technologies such as direct sequence spread spectrum (DSSS) have been developed as effective countermeasures against, for example, jamming attacks. This paper surveys previous research on securing a DSSS channel even further using physical layer attributes - Keyless DSSS mechanisms and Watermarked DSSS (WDSSS) schemes. The former has been motivated by the fact that it is still an open question to establish and share the secret spread sequence between the transmitter and the receiver without being noticed by adversaries. The basic idea of the latter is to exploit the redundancy inherent in DSSS’s spreading process to embed watermark information. It can be considered a counter measure (authentication) for an intelligent attacker who obtains the spread sequence to generate fake messages. This paper also presents and evaluates an adaptive DSSS scheme that takes both jam resistance and communication efficiency into account

The vulnerability of quantization-based data hiding schemes to amplitude scaling requires the formulation of countermeasures to this relatively simple attack. Parameter estimation is one possible approach, where the applied scaling is estimated from the received signal at the decoder. This estimate can be used to correct the mismatch with respect to the quantization step assumed by the decoder prior to decoding. In this work we present a method for joint iterative decoding and maximum likelihood estimation of the scaling factor on DC-DM. The estimation method exploits the reliabilities provided by the near-optimal iterative decoding process in order to successively refine the estimate. The complexity of this problem is tackled using the expectation maximization algorithm.