With the development of artificial electromagnetic metamaterials, the new target stealth technology represented by metasurface stealth can achieve fast modulation within the pulse, which poses a huge challenge to the traditional radar detection method based on pulse integration. In this paper, the echo model of the metasurface stealth target is constructed, and the short-term coherence of the echo is mined through windowed time-frequency analysis. The time-frequency spectrogram of the target echo is correlated with the time-frequency spectrogram of the transmitted signal to obtain one-dimensional correlation matching coefficients. Target detection is achieved based on coefficient peaks. This paper proves through simulation that the time-frequency correlation matching method can obtain good detection performance under different pulse modulation speeds.
Radar signal sorting is an important issue in radar electronic warfare, and its accuracy directly affects the identification of radar radiation sources and the effectiveness of electronic reconnaissance systems. This article proposes an efficient radar sorting algorithm. Firstly, the number of pulses in small intervals is counted, and the number of pulses in the interval is calculated. Then, the potential pulse interval is searched for, and the corresponding pulse string of the signal with the potential pulse interval is obtained using an improved sequence retrieval algorithm based on vector dot product. The interference between pulse strings with different PRIs (Pulse Repetition Intervals) is reduced by using the method of successive elimination. Simulation experiments show that the algorithm greatly reduces the calculation time while maintaining high accuracy in radar sorting.
In harsh environments, the detected target track is easily interrupted to form fragmented track segments, which creates a great challenge for track management and tracking. The traditional track segment association (TSA) method is based on a hypothetical target motion model and utilizes a large amount of a priori information to complete the association task. When the hypothetical a priori model does not match the actual motion pattern, the inference time is too long, and the performance decreases significantly. In this article, we propose a track segment association algorithm based on Gaussian regression analysis, in which the new and old segments of the track are back predicted to the associated moment for pairwise discrimination respectively. The Hungarian algorithm is introduced to solve the correlation problem of the track, and the track correlation problem is transformed into an allocation problem by establishing the correlation matrix, and the Hungarian algorithm is used to find the optimal solution. The results show that the proposed method can effectively improve the accuracy of the discrimination.
KEYWORDS: Signal filtering, Tunable filters, Electronic filtering, Sensors, Data modeling, Matrices, Unmanned aerial vehicles, Data processing, Covariance matrices, Signal processing
In agriculture, obtaining the remaining fertilizer weight is crucial for achieving accurate fertilizer application when using drones for spreading fertilizer. To address the problem of inaccurate weighing of remaining fertilizer caused by environmental and system factors during the fertilizer application process, a fusion filtering algorithm suitable for dynamic weighing of fertilizer is proposed. First, a second-order model is established between the wheel speed and the fertilizer application rate. The three coefficients in the second-order mapping relationship formula between the fertilizer weight and the wheel speed are used as the four-dimensional state variables of the Kalman filter, with the wheel speed on the drone set as the control variable. Then the weight data measured by the weighing sensor is sent to the Kalman filter for primary filtering. At the same time, the sliding average filtering algorithm is used to smooth out the oscillation phenomenon in the filtering results. Simulation experiments and real data processing results show that the drone dynamic weighing fusion algorithm, based on the four-dimensional Kalman filter and sliding average filtering has good noise reduction and smoothing effects on fertilizer weight data.
Post-disaster networking is the foundation and the primary work of emergency rescue. In this paper, we adopt the rapid deployment method of artificial bee colony (ABC) based on rapid depth-first search (RDFS) to keep the UAV cluster in the relay state, and use rapid depth-first search in the period of employment bees, observation bees and scout bees, respectively, to quickly find out the connectivity links between the ground control centre and the ground nodes, and by optimizing the depth-first search algorithm, we can stop searching as soon as any link is found, which greatly improves the relaying efficiency and shortens the relaying deployment time, and identifies an optimal relaying deployment strategy to improve the throughput. Simulation experiments show that compared with the deployment method before optimization, the rapid deployment method of artificial bee colony based on rapid depth-first search after optimization improves the throughput of the network by up to 12 times.
A model combining neural networks and transfer functions (neuro-TF) is developed as an efficient way to parametric modeling electromagnetic(EM) responses. For coding metasurface, a new pole-residue tracking technique is improved to make the orders of pole-residue-based transfer functions consistent. However, the technique causes non-unique problems between poles/residues and corresponding EM responses. To address this issue, multiple cost functions are designed and a multitask neural network framework is constructed. During the training, two tasks are trained alternately in the same networks to speed up convergence by sharing the same representations such as weights and bias. Ultimately, poles/residues and corresponding EM responses are obtained concurrently. Compared with other existing neuro-TF network modeling methods, the proposed model can obtain accurate poles/residues as well as predict EM responses generated by transfer functions simultaneously in challenging applications of coding metasurface.
Traditional forward prediction of coding metasurface is highly time-consuming due to repetitive numerical calculations. In this paper, an advanced pole-residue-based neuro-transfer function (neuro-TF) technique is proposed for parametric modeling and predicting electromagnetic (EM) response of coding metasurface with respect to the changes in coding values representing the geometry of metasurface. In the proposed model, neural networks are trained to learn the mapping between poles and residues of the pole-residue transfer functions and coding values of metasurfaces, and an objective function based on joint learning is designed for the model optimization to increase the accuracy and efficiency of the model. A soft-sharing model called customized gate control (CGC) is brought in to jointly predict the poles and residues. In order to further improve the model performance and accelerate the learning process, we propose an objective function, in which the pole prediction is introduced as auxiliary task to serve for the main task of response predicting. The weights of losses of tasks are respectively determined by homoscedastic uncertainty reflecting the training difficulty of each task from the perspective of output observation noise. The proposed method allows the existence of real pole-residue pairs as well as pairs in complex format, which makes its application less limited compared to existing researches. Experiment result shows that the proposed model achieves great generalization and improves the accuracy of EM response prediction.
We investigate the response time of silicon-based thermo-optic switches under different device configurations. We design two tunable thermo-optic switches on a silicon-on-insulator (SOI) chip. One uses a waveguide embedded phase shifter based on direct heating due to electric current flow through waveguide. The other traditional switch structure has a metallic heater on top of the waveguide. Owing to direct current injection to heat the waveguide, which avoids the heat conduction from heater to waveguide, the switching time would reduce significantly. The experimental result shows that the direct heating device realizes a fast response time close to 1.5μs. As a comparison, the traditional heater-on-top device’s response time is over 10μs. That is to say, switching time of the direct-current-injection device is over ten times less. The insertion loss of both devices are reasonable. The fast heating device shows a potential for applications in the future optica interconnects.
We propose a sparsity measure for third-order tensor, called all-dimension tensor nuclear norm (AD-TNN). In specific, we exploit the tensor-singular value decomposition and tensor nuclear norm (TNN), considering TNN along every dimension and then construct our AD-TNN measurement. Based on AD-TNN, we construct a model for multispectral images denoising. We also employ the alternating direction method of multipliers (ADMM) to solve our model. Experimental results show that our method outperforms all the compared methods under comprehensive quantitative performance measures.
In inverse synthetic aperture radar (ISAR) imaging, the migration through resolution cells (MTRCs) will occur when the rotation angle of the moving target is large, thereby degrading image resolution. To solve this problem, an ISAR imaging method based on segmented preprocessing is proposed. In this method, the echoes of large rotating target are divided into several small segments, and every segment can generate a low-resolution image without MTRCs. Then, each low-resolution image is rotated back to the original position. After image registration and phase compensation, a high-resolution image can be obtained. Simulation and real experiments show that the proposed algorithm can deal with the radar system with different range and cross-range resolutions and significantly compensate the MTRCs.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.