IET Signal Processing最新文献

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.

An off-grid weighted sparse Bayesian learning algorithm based on grid fission for direction of arrival estimation is proposed. The existing grid fission algorithms can use fewer grid points with variant intervals to estimate the true DOAs. However, their learning processes are based on the traditional sparse Bayesian algorithm, which only assigns the same prior distribution assumption to the signals on all grids, but ignores the difference of signal distribution of different grid points. It will result in inaccurate fission location and fission direction because of the insufficient resolution of the spatial spectrum, reducing the estimation accuracy. Moreover, the fission strategy will cost much computation time due to the increase of grid points. To solve these problems, the proposed algorithm utilises the orthogonality of signal subspace and noise subspace to design the weights for prior signal distribution assumption, making the peaks of spatial spectrum more pronounced and easy to distinguish, using more accurate estimated DOAs and off-grid parameter to determine the fission location and direction. In addition, the fission process deletes redundant grid points to simplify calculations. Compared with the existing grid fission algorithms, the proposed method has superior performance in estimation accuracy and computational time.

Due to the reduction in costs of manufacture and launch of Low-Earth orbit (LEO) satellites, LEO satellite communication (SATCOM) has become a promising solution to provide high data rates and low latency connectivity for future communications. However, due to the fast movement of LEO satellites, it has to face a large Doppler effect, leading to high inter-carrier interference (ICI) power and severe received symbol error rate. To tackle this problem, a novel optimisation framework is proposed that boosts the achievable rate in LEO SATCOM networks by utilising passive beamforming of intelligent reflecting surface (IRS) to compensate for the Doppler effect. First, a carrier-to-interference-noise ratio (CINR) was derived for each subcarrier and a joint optimisation problem of both power allocation is formulated to each subcarrier and passive beamforming of IRS. Moreover, a simultaneous iterative-water-filling-algorithm (SIWFA) and semidefinite programing (SDP) are utilised, thereby striking a balance that minimises ICI power and maximises desired signal power. Numerical results demonstrate the superior achievable rate performance of the proposed IRS-aided Doppler compensation for LEO SATCOM networks compared to benchmark schemes.

The existing deep learning-based face super-resolution techniques can achieve satisfactory performance. However, these methods often incur large computational costs, and deeper networks generate redundant features. Some lightweight reconstruction networks also present limited representation ability because they ignore the entire contour and fine texture of the face for the sake of efficiency. Here, the authors propose a convenient alternating projection network (CAPN) for efficient face super-resolution. First, the authors design a novel alternating projection block cascaded convolutional neural network to alternately achieve content consistency and learn detailed facial feature differences between super-resolution and ground-truth face images. Second, the self-correction mechanism enabled the convolutional layer to capture faithful features that facilitate adaptive reconstruction. Moreover, a convenient connection operation can reduce the generation of redundant facial features while maintaining accurate reconstruction information. Extensive experiments demonstrated that the proposed CAPN can effectively reduce the computational cost while achieving competitive qualitative and quantitative results compared to state-of-the-art super-resolution methods.

Depression diagnosis based on speech signals has the advantages of non-invasiveness, low cost, and few restrictions on portability. The research on the recognition of the depression state is carried out based on the acoustic information in the speech signal. Aiming at the interview dialogue speech in the consultation environment, a hierarchical attention temporal convolutional network (HATCN) acoustic depression recognition model is proposed. For sentence acoustic feature learning, a regional attention mechanism is introduced to extract multi-scale sentence features; for segment acoustic feature extraction, the traditional attention mechanism is used to calculate, which is in line with human cognitive mechanism. In addition, a periodic focal loss function is introduced to address the imbalance of positive and negative samples in depression diagnosis. Experiments show that the proposed acoustic depression recognition model has a certain improvement in recognition performance compared with other methods. At the same time, the influence of noise on the recognition of acoustic depression in the real consultation environment is analysed through experiments, and the data enhancement is carried out utilising speech noise, which proves the effectiveness of the data expansion of speech noise.

In passive radar and joint communication and radar sensing (JCRS), target sensing with the multiuser multiple input multiple output orthogonal frequency division multiplexing (MU-MIMO-OFDM) modulation signal is gaining increasing interest. Multiple transmit nodes emitting the MU-MIMO-OFDM modulation signals at the same carrier frequency and one receiver collecting the target-reflected signals for target location and velocity estimation are considered. This is a typical scenario when using the fifth-generation (5G) communication network signal for target sensing. In this scenario, the echo signals corresponding to different transmit nodes are not resolved in the receiver, and the modulated data symbols cannot be removed from the received signals. Most traditional parameter estimation methods in passive radar and JCRS may not be suitable here. A location and velocity estimation method with the received echo signals is proposed. Specifically, the location parameters are extracted directly from the received echo signals. The location estimation is cast into a block sparse vector reconstruction problem. The variational Bayesian sparsity learning (VBSL) method is exploited for the reconstruction of the block sparse vector. Accelerated VBSL methods are developed for improving the computational efficiency. Simulations verify the effectiveness of the proposed methods.