IET Signal Processing最新文献

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.

In recent years, the development of microelectrode arrays and multichannel recordings has provided opportunities for high-precision detection in signal processing. The study of neuronal frontal potentials has been rapidly emerging as an important component in brain-computer interface and neuroscience research. Neuronal spike detection provides a basis for neuronal discharge analysis and nucleus cluster identification; its accuracy depends on feature extraction and classification, which affect neuronal decoding analysis. However, improving the detection accuracy of spike potentials in highly noisy signals remains a problem. IThe authors propose a heuristic adaptive threshold spike-detection algorithm that removes noise and reduces the phase shift using a zero-phase Butterworth infinite impulse response filter. Next, heuristic thresholding is applied to obtain spike points, remove repetitions, and achieve robust spike detection. The proposed algorithm achieved an average accuracy of 95.40% using extracellular spiked datasets and effectively detected spikes.

In this article, the distributed filtering of absolute and relative measurements for the cooperative localisation of multiple vehicles is investigated. A novel two-stage approach that uses two unbiased cooperative filters for the sequential processing of integrated measurements is proposed. The first filter sequentially estimates each vehicle state by replacing extra neighbouring states with corresponding estimates obtained only from absolute measurements and by adding relative measurements. The second filter considers extra neighbouring states as auxiliary coloured noise. The proposed filters have low communication loads and computational complexity because of the sequential processing of the absolute and relative measurements. Unlike existing cooperative filters, the proposed two-stage structure makes the filters robust against the presence of unreliable links between neighbouring vehicles. We present simulation results demonstrating the effectiveness and accuracy of the proposed filters when applied to vehicles performing two-dimensional manoeuvres in three network topologies: a ring, line, and mesh.

In order to improve the detection probability of weak targets, tracking radar using sum and difference beams often adopt the method of long-time coherent integration. However, the multidimensional migration of time-varying targets will lead to the decline of parameter estimation accuracy. To solve this problem, this article proposes a refined angle estimation method for time-varying targets with the traditional sum and difference beam echo model, this method compensates and searches the angle parameters of the targets based on subarray rotation invariant and focus process. In addition, this article also studies the masking problem of highly dynamic proximity group targets detection, and proposes an adaptive weighted LMS-CLEAN based on Least Mean Square criterion, which effectively reduces the influence of masking effect on the parameter estimation accuracy of weak targets. Firstly, the proposed algorithm performs angle search and phase compensation on the pulse compression echo of sum and difference channels based on subarray rotation invariant. Secondly, focus the search matrix, reconstruct the strong target echo, and stripe it from both channels by adaptive weighting. Lastly, repeat the above steps until parameters of all targets are achieved precisely. The proposed two algorithms maintain a very low computational effort while effectively reducing the parameter estimation error, and are highly promising for engineering applications. In order to verify the effectiveness of the proposed algorithm, this article also provides some numerical experiments to compares with two existing algorithms in error performance, anti-noise performance, and computational complexity.

With the increasing demand of applications for the spread spectrum technique, especially the demand for data transmission rates and spectral efficiency, the advantages of the traditional direct sequence spread spectrum (DSSS) system are limited. Therefore, multi-ary spread spectrum (M-ary) technology, parallel combinatory spread spectrum (PCSS) technology, and code index modulation (CIM) technology have been proposed. Although these three new technologies can improve the data rate, they all face the problem of the large consumption of pseudo-code resources. In order to solve the problem of pseudo-code resources, a bit-activation code index modulation (BA-CIM) method is proposed. At the transmitter, considering the good correlation among multiple pseudo-codes, the corresponding pseudo-code activation principle is established, and the corresponding spreading pseudo-code is activated by using the status of each bit of the index data according to the pseudo-code activation principle. Then, multicode superposition processing is carried out to spread the modulation data. At the receiver, the corresponding activation pseudo-code is obtained using the maximum peak-to-average ratio (MPAR) and secondary peak-to-average ratio (SPAR) judgement mechanisms to decode the multibit index data. Compared with existing methods, the proposed BA-CIM method can not only achieve a better bit error rate performance but also use the least pseudo-code resources. Moreover, BA-CIM has the best comprehensive performance improvement and is far superior to other methods. This research can provide technical support for the application of efficient spread spectrum communication.

The fixed window function used in the short-time Fourier transform (STFT) does not guarantee both time and frequency resolution, exerting a negative impact on the subsequent study of time-frequency analysis (TFA). To avoid these limitations, a post-processing method that enhances the time-frequency resolution using a deep-learning (DL) framework is proposed. Initially, the deconvolution theoretical formula is derived and a post-processing operation is performed on the time-frequency representation (TFR) of the STFT via deconvolution, a theoretical calculation to obtain the ideal time-frequency representation (ITFR). Then, aiming at the adverse influence of the window function, a novel fully-convolutional encoder-decoder network is trained to preserve effective features and acquire the optimal time-frequency kernel. In essence, the generation of the optimal time-frequency kernel can be regarded as a deconvolution process. The authors conducted the qualitative and quantitative analyses of numerical simulations, with experimental results demonstrate that the proposed method achieves satisfactory TFR, possesses strong anti-noise capabilities, and exhibits high steady-state generalisation capability. Furthermore, results of a comparative experiment with several TFA methods indicate that the proposed method yields significantly improved performance in terms of time-frequency resolution, energy concentration, and computational load.