IET Signal Processing最新文献

In non-cooperative scenarios, the spreading sequences or waveforms of the direct sequence spread spectrum (DSSS) signals is unknown to the receiver. This paper focuses on addressing the problem of blind estimation of the spreading waveform under multipath channels. In the scenario of direct signal path transmission, the spreading sequences can be directly obtained based on the estimated spreading waveforms. However, in the presence of multipath channels, the spreading waveform becomes the convolution of the spreading sequence and channel response, thus deconvolution should also be performed after estimating the spreading waveforms. In order to perform blind despreading and deconvolution of asynchronous multiuser DSSS signals under multipath channels, the authors propose to exploit the finite symbol characteristics of information and spreading sequences and then the iterative least square with projection method is adopted. Besides, the Cramer-Rao bound of spreading waveforms is derived in such a circumstance as a performance benchmark. The effectiveness of the proposed method is verified via simulation experiments.

It is difficult for a receiver to intercept the signals from a radar system that can emit low probability of intercept (LPI) polyphase coded signals. The traditional Wigner Hough transform (WHT) algorithm requires a large amount of computation and takes a long time to estimate the parameters of the LPI radar polyphase coded signals. To address this problem, an iterative angle search (IAS) algorithm, which when used in combination with the WHT algorithm significantly reduces the computational cost is proposed. When the signal-to-noise ratio is in the range of −4 to 20 dB, the carrier frequency, number of subcodes, and number of cycles of the carrier frequency per subcode of five polyphase coded signals, namely, Frank, P1, P2, P3, and P4, are accurately estimated in simulation experiments. Based on the selected IAS algorithm parameters, the estimation accuracy of the proposed method is the same as that of the traditional WHT algorithm. However, the operation time is only 5.14% of that of the traditional method. The IAS algorithm has certain application prospects. Experiments indicate that the proposed algorithm provides excellent performance and can rapidly and accurately estimate the parameters of LPI polyphase codes.

Human speech signals may contain specific information regarding a speaker's characteristics, and these signals can be very useful in applications involving interactive voice response (IVR) and automatic speech recognition (ASR). For IVR and ASR applications, speaker classification into different ages and gender groups can be applied in human–machine interaction or computer-based interaction systems for customised advertisement, translation (text generation), machine dialog systems, or self-service applications. Hence, an IVR-based system dictates that ASR should function through users' voices (specific voice-frequency bands) to identify customers' age and gender and interact with a host system. In the present study, we intended to combine a pitch detection (PD)-based extractor and a voice classifier for gender identification. The Yet Another Algorithm for Pitch Tracking (YAAPT)-based PD method was designed to extract the voice fundamental frequency (F₀) from non-stationary speaker's voice signals, allowing us to achieve gender identification, by distinguishing differences in F₀ between adult females and males, and classify voices into adult and children groups. Then, in vowel voice signal classification, a one-dimensional (1D) convolutional neural network (CNN), consisted of a multi-round 1D kernel convolutional layer, a 1D pooling process, and a vowel classifier that could preliminary divide feature patterns into three level ranges of F₀, including adult and children groups. Consequently, a classifier was used in the classification layer to identify the speakers' gender. The proposed PD-based extractor and voice classifier could reduce complexity and improve classification efficiency. Acoustic datasets were selected from the Hillenbrand database for experimental tests on 12 vowels classifications, and K-fold cross-validations were performed. The experimental results demonstrated that our approach is a very promising method to quantify the proposed classifier's performance in terms of recall (%), precision (%), accuracy (%), and F1 score.

It is anticipated that sixth-generation (6G) systems would present new security challenges while offering improved features and new directions for security in vehicular communication, which may result in the emergence of a new breed of adaptive and context-aware security protocol. Physical layer security solutions can compete for low-complexity, low-delay, low-footprint, adaptable, extensible, and context-aware security schemes by leveraging the physical layer and introducing security controls. A novel physical layer security scheme that employs the concept of radio frequency fingerprinting (RF-FP) for location estimation is proposed, wherein the RF-FP values are collected at different points with in the cell. Then, based on the estimated location, the nearest possible road-side unit for sending the information signal is located. After this, the effects on secrecy capacity (SC) and secrecy outage probability (SOP) in the presence of multiple eavesdropper per unit time are analysed. It has been shown via simulations that the proposed RF-FP scheme increases SC by up to 25% for the same signal-to-noise ratio (SNR) values as those of the benchmarks, while the SOP tends to decrease by up to 30% as compared to the benchmark scheme for the same SNR value. Thus, the proposed RF-FP-based location estimation provides much better results as compared to the existing physical layer security schemes.

This article deals with peak to average power (PAPR) reduction in a single and multi-user orthogonal chirp division multiplexing (OCDM) context. Two methods for PAPR reduction based on the selection of the frequency variation (up or down) of the chirps are first presented in a single user system. The first technique consists in considering two OCDM signals generated with up and down chirps, respectively, and selecting the one offering lowest PAPR. The second PAPR reduction method is based on usual clipping, and in that case the chirp selection aims to reduce the clipping noise. An adapted receiver is presented, based on the maximum likelihood estimation of the frequency variation (up or down) of the chirp. Then, a general procedure for multi-user OCDM transmission is introduced, where a sub-band of the available bandwidth is dedicated to each user, whose frequency of the chirps varies within this sub-band. Next, the PAPR reduction techniques are generalised to this multi-user OCDM system. Moreover, a performance analysis of the first PAPR reduction method is developed, and it is shown through simulations that theoretical and numerical results match for both Nyquist rate and oversampled signals. It is also shown that the chirp selection reduces the clipping noise, and improves the bit error rate performance compared with clipping only.

While Doppler resilient complementary waveforms (DRCWs) have previously been considered to suppress range sidelobes within a Doppler interval of interest in radar systems, their ability to provide Doppler resilience can be further improved. A new singular value decomposition (SVD)-based DRCW construction is proposed, in which both transmit pulse trains (made up of complementary pairs) and receive pulse weights are jointly considered. Besides, using the proposed SVD-based method, a theoretical bound is derived for the range sidelobes within the Doppler interval of interest. Moreover, based on the SVD solutions, a challenging non-convex optimization problem is formulated and solved to maximise the signal-to-noise ratio (SNR) with the constraint of low range sidelobes. It is shown that, compared with existing DRCWs, the proposed SVD-based DRCW has better Doppler resilience. Further, the new optimised SVD-based DRCW has a higher SNR while maintaining the same Doppler resilience.

The most important challenges of classifying Motor Imagery tasks based on the EEG signal are low signal-to-noise ratio, non-stationarity, and the high subject dependence of the EEG signal. In this study, a framework for multi-class decoding of Motor Imagery signals is presented. This framework is based on information theory and hybrid deep learning along with transfer learning. In this study, the OVR-FBDiv method, which is based on the symmetric Kullback—Leibler divergence, is used to differentiate between features of different classes and highlight them. Then, the mRMR algorithm is used to select the most distinctive features obtained from the filters of symmetric KL divergence. Finally, a hybrid deep neural network consisting of CNN and LSTM is used to learn the spatial and temporal features of the EEG signal along with the transfer learning technique to overcome the problem of subject dependence in EEG signals. The average value of Kappa for the classification of 4-class Motor Imagery data on BCI competition IV dataset 2a by the proposed method is 0.84. Also, the proposed method is compared with other state-of-the-art methods.

The globally optimal generalised sequential fusion (GSF) algorithm in the sense of linear minimum variance for multi-sensor stochastic uncertain systems is investigated by the authors. Specifically, in the GSF algorithm, the estimation of measurement noise is considered, and m_a (m_a ≥ 1) sensors' measurement data are fused at the ath reception instant, which makes it very flexible and suitable for practical applications. The centralised and sequential fusion algorithms are special cases of the proposed GSF algorithm. Furthermore, for any m_a, a = 1, 2, …, M, the estimated values of the GSF algorithm remain invariant and globally optimal. Moreover, the independence between the estimated values and fusion order is proved in the proposed GSF algorithm. Finally, simulation results are given to demonstrate the usefulness of the developed algorithm.

Next-generation 6G networks will fully drive the development of the industrial Internet of Things. Steel surface defect detection as an important application in industrial Internet of Things has recently received increasing attention from the military industry, the aviation industry and other fields, which is closely related to the quality of industrial production products. However, many typical convolutional neural networks-based methods are insensitive to the problem of unclear boundaries. In this article, the authors develop a region-based fully convolutional networks with deformable convolution and attention fusion to adaptively learn salient features for steel surface defect detection. Specifically, deformable convolution is applied into selectively replace the standard convolution in the backbone of the region-based fully convolutional networks, which performs significantly in scenarios with unclear defect boundaries. Moreover, convolutional block attention module is utilised in region proposal network to further enhance detection accuracy. The proposed architecture is demonstrated on two popular steel defect detection benchmarks, including NEU-DET and GC10-DET, which can effectively present the performance of steel surface defect detection by abundant experiments. The mean average precision on two datasets reaches 80.9% and 66.2%. The average precision of defect crazing, inclusion, patches, pitted-surface, rolled-in scale and scratches on NEU-DET is 58.2%, 82.3%, 95.7%, 85.6%, 75.9%, and 87.9% respectively.

Non-orthogonal multiple access (NOMA) technique introduces spectrum cooperation among different users and devices, which improves spectrum efficiency significantly. Energy-limited devices benefit from the backscatter (BAC) technique to transmit signals without extra energy consumption. The combination of NOMA and BAC provides a promising solution for Internet of Things (IoT) networks, where massive devices simultaneously transmit and receive signals. This study investigates a system model with two NOMA downlink users and an uplink device. The aim is to maximise the data rate of the uplink device by optimising the power allocation coefficient and the backscattering coefficient. Meanwhile the quality of service requirements of two NOMA users are guaranteed. The closed-form solution of two optimisation variables is derived, and an alternating algorithm is also proposed to solve the formulated optimisation problem efficiently. The proposed system verifies the feasibility of IoT devices being added into existing networks and provides a promising solution for wireless communication networks in the future.