KEYWORDS: Matrices, Time-frequency analysis, Interpolation, Detector arrays, Array processing, Sensors, Signal to noise ratio, Signal processing, Signal detection, Signal attenuation
An underdetermined direction-of-arrival (DOA) estimation method is proposed for wideband non-stationary signals based on atomic norm minimization. In scenarios where signals exhibit completely overlapped time-frequency distributions (TFDs), the spatial time-frequency distribution (STFD) matrix becomes rank deficient, making it impossible to separate signals based on their time-frequency characteristics. This proposed method uses multiple snapshots atom norm minimization to achieve an ideal average STFD matrix and enables underdetermined DOA estimations of wideband nonstationary signals with completely overlapped TFDs, without aperture loss or the need for arrays to possess spatial translation invariant structures. Moreover, the proposed method involves interpolating the sparse array into a virtual uniform linear array to enhance array processing degree freedom. Simulation results illustrate the efficacy of the proposed method.
KEYWORDS: Matrices, Sensors, Detector arrays, Time-frequency analysis, Covariance matrices, Signal to noise ratio, Interpolation, Signal detection, Signal attenuation, Principal component analysis
An underdetermined direction-of-arrival (DOA) estimation method is proposed for wideband non-stationary signals with completely overlapped time-frequency distributions (TFDs). In this case, signals cannot be separated based solely on their time-frequency characteristics because of a rank-deficient spatial time-frequency distribution (STFD) matrix. The proposed method uses a moving array to recover the rank of the STFD matrix. Meantime, it vectorizes the matrix to construct a differential virtual co-array, and employs matrix rank minimization to fill the holes of virtual array, thereby enhancing the array processing degree of freedom. This method enables underdetermined DOA estimation of wideband non-stationary signals with completely overlapped TFDs, without suffering from aperture loss or requiring arrays with spatial translation invariant structures. Simulation results demonstrate the effectiveness of the proposed method.
Wearable sensors have become essential platforms for research on human activity recognition (HAR) due to their advantages such as compact size, low power consumption, and non-invasive nature, ensuring continuous and privacy-conscious data acquisition. In this paper, a HAR architecture using Machine Learning (ML) technique based on data collected from wearable sensors is proposed to perform high performance for accurate recognition of human activities in real-life scenarios. To address the challenge of accurately distinguishing similar forms of daily activities, a feature library consisting of 55 feature functions has been constructed, and the Maximum Relevance Minimum Redundancy (mRMR) algorithm is employed to select the most informative and relevant features for activity classification. Experimental results indicate that the combination of data from multiple sensors and the dynamic selection of features significantly improve the performance of the HAR system, as they provide a more comprehensive and diverse set of information. The numerical results show that the human activity recognition framework proposed in this paper can achieve an accuracy of 98.53% on the self-collecting dataset.
A direction of arrival estimation method based on short time fractional Fourier transform (STFRFT) is proposed for wideband non-stationary radar and communication integration signals based on phase modulation. This algorithm utilizes the aggregation characteristics of integration signals in the fractional Fourier transform domain and the rotation characteristics of fractional Fourier transform (FRFT) to transform wideband non-stationary signals in the original time-frequency domain into narrowband non-stationary signals in the fractional Fourier transform domain, thereby obtaining a time-invariant fractional Fourier transform domain steering vector, and using the MUSIC algorithm for DOA estimation, effectively avoiding the impact of focusing error on the estimation results. The simulation results demonstrate the effectiveness of the proposed algorithm.
KEYWORDS: Compressed sensing, Covariance matrices, Quantization, Reconstruction algorithms, Signal processing, Matrices, Radar signal processing, Analog to digital converters, Signal attenuation, Received signal strength
The one-bit analog-to-digital converters (ADC) can drastically reduce the system complexity and power consumption, which has attracted considerable research interest in the last decade. In this paper, the problem of one-bit quantized signal direction-of-arrival (DOA) estimation via sparse array is considered. The proposed method first gives an approximate reconstruction method to extend the aperture of sparse array, then a compressive sensing method is presented to obtain accurate DOA estimation. The simulation results demonstrate that the proposed method is well suited for scenarios of few antenna elements and small snapshots.
In this paper, a directional polarization modulation method based on a time-modulated vector array is proposed for secure communication. The polarization state of the transmitted signal is used to represent the communication information to realize the directional polarization modulation in the desired direction. The vector antenna is divided into two sub-arrays. Each sub-array is controlled by a different time sequence to turn on the antenna. The correlation between the horizontal and vertical components of the polarization signal is destroyed. The communication direction is not affected, but the desired polarization state cannot be synthesized in the non-communication direction. The simulation results show that the desired polarization state is received in the communication direction, the polarization information of the signal in the non-communication direction is disturbed, and the information leakage from the time-modulated array due to sideband radiation is avoided.
Directional modulation (DM) based on phased array (PA) can realize angle-dependent secure transmission. In this paper, frequency agile array (FAA) based DM technique is proposed to achieve range-angle-dependent secure transmission. Different from the conventional frequency diverse array (FDA), whose frequency offsets applied to the array is fixed, FAA can achieve a distortionless constellation at the target location and randomly distorted constellations at other locations by changing the frequency offsets at the symbol rate. Two frequency offset selection schemes are presented. The first scheme randomly selects the frequency offset applied to each element from a given set, and the received signal is randomly distorted both in amplitude and phase except the target location. The second scheme selects the optimal frequency offsets with lower sidelobe based on ant colony algorithm (ACO). Further, the sidelobe level is relaxed appropriately to seek multiple near optimal solutions on the basis of the optimal frequency offsets. The simulation results show that the proposed method generates higher bit error rate (BER) at non-target locations and narrower information beamwidth near the target location, which provides better secure transmission performance compared with the conventional FDA.
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