Abstract:To solve the problem of adaptive detection for distributed targets in partially homogeneous environment with outliers and limited samples, a class of adaptive detectors are designed based on geometric median in this paper. The first step is to construct a data selector based on geometric median generalized inner product and eliminate sample data containing outliers. The second step is to construct detection statistics of the generalized adaptive subspace detector using covariance matrix estimators, which are based on geometric median. The detectors utilize geometric median of the positive definite matrix space without any knowledge of prior probability distribution of sample data. The performance of the proposed two-step detectors is evaluated in terms of the probabilities of correct outliers excision, false alarm, and detection. Experiment results based on simulated and real data show that the proposed approach has better detection performance than the existing ones based on traditional covariance estimator.

Abstract:To investigate the application of RF system level chip—Radio Frequency System-on-Chip(RFSoC) in pulse radar system, a radar ranging system with high performance digital-analog hybrid signal processing capability is designed. The high-performance RFSoC development board—IW-RFSoC-49DR(including the design of the background interference filtering algorithm) is adopted, and the test environment is set in a laboratory with narrow space and disturbed multimetallic equipments. The results of the experiments show that the experimental data are significantly disturbed in an untreated, complex indoor environment; after implementing the background interference filtering algorithm, the display resolution of the frequency spectrum map has been significantly improved. As the test target distance increases from 3 m to 12 m, the ranging error decreases from 53 cm to 5 cm. RFSoC technology shows significant advantages in the design of pulsed radar system, realizing the high integration and low power consumption design, and laying a foundation for the subsequent design of portable radar based on RFSoC.

Abstract:The tracking accuracy of conventional Adaptive Interactive Multiple Model(AIMM) algorithm is poor in the process of maneuvering target tracking by passive radar. In combination with the characteristics of passive radar,the improved Adaptive Transition Probability Matrix-Interactive Multiple Model(ATPM-IMM) algorithm is proposed. Based on the ATPM-IMM algorithm, this algorithm uses the adaptive control window to revise the transition probability matrix again. It can automatically switch the maneuvering model according to the maneuvering situation of the target and improve the matching probability of real model. Simulation and experimental results show that the proposed algorithm can effectively improve the tracking accuracy of passive radar to maneuvering targets.

Abstract:Non-cooperative bistatic radar has a low signal-to-noise ratio in the echo due to its special detection method. In particular, the detection between frames in the radar scanning cycle for maritime moving targets is not stable, which will bring great difficulties for subsequent target tracking. The low threshold Constant False Alarm Rate(CFAR) detector is employed to match the detection results of radar range-Doppler dimension and range-azimuth dimension to obtain the corresponding mask map, and the potential moving targets are found. Then, a Double Backbone-YOLO(DB-YOLO) that fuses multi-dimensional feature information is proposed. The network adopts a dual-trunk structure, extracts the features of the moving target mask map and the same-scale P-display map under its mapping, and uses a deep separable convolution module to reduce the model parameters of the network. Finally, the comparison experiments with Faster RCNN, YOLOv5 and its common variant YOLOv5-ConvNeXt show that DB-YOLO effectively improves the target detection performance and ensures the inference speed, which lays a foundation for target tracking of noncooperative bistatic radar.

Abstract:To tackle with the problem of decreased recognition accuracy caused by imbalanced individual data distribution in Specific Emitter Identification(SEI), a dynamic weight model based methodis proposed for individual identification of radiation sources. A Dynamic Class Weight(DCW) model is built. A moderate initial weight value is obtained by using a meta learning algorithm through two-layer calculation with a small amount of sample data. Then, a new cost sensitive loss function is designed to calculate the backward adjustment of the distance between the predicted value and the true value, which gives the minority learning weight, and moderately increases the attention to the minority data. It is more friendly to the minority. It has obvious advantages in the processing of highly unbalanced data, which alleviates the calculation misleading of the majority of samples in the whole recognition process, thus improving the overall recognition accuracy.

Abstract:The background noise in the echo spectrum of High Frequency Surface Wave Radar(HFSWR) is complex, the clutter accounts for a small proportion and the ionospheric clutter has different forms and positions, therefore, it is difficult to automatically recognize the clutters. Based on DeeplabV3+ deep learning algorithm, an automatic identification method of ionospheric clutter and sea clutter is proposed for HFSWR. Selecting the lightweight MobileNetV2 backbone feature network, adding the channel attention mechanism module SENet, the focused learning of clutter labels is realized, and the loss weight of various labels in the training set is optimized. The model pre-training transfer method is employed to pre-train the network to tackle with the problem of too small sample space. The experimental results on the measured data set show that the proposed method can realize the automatic recognition of ionospheric clutter and sea clutter in HFSWR, and can obtain more accurate and finer clutter recognition results than the original DeeplabV3+ algorithm. The mean Intersection over Union(mIoU) and Accuracy(ACC) of sea clutter recognition results are increased by 2.9% and 5.1% respectively, and the mIoU and ACC of ionospheric clutter recognition results are increased by 3.0% and 4.9% respectively.

Abstract:The sensing and detection of the biomolecule Terahertz(THz) spectrum fingerprint is performed based on Complementary Metal Oxide Semiconductor(CMOS) controllable metamaterials, using a Spintronic THz Emitter device. The spintronic THz spectrum fingerprint of three different biological samples were benchmarked with results using THz photoconductive antennas. The results show that the feasibility of measuring biological samples with spin terahertz source is verified. At the same time, a frequency based biomolecular THz sensing scheme is proposed by utilizing CMOS controllable metamaterials. Finite element models are built based on the performance test and the biosensing process of the CMOS controllable metamaterial devices. Five CMOS controllable metamaterials were designed with center frequencies related to the absorption peaks of the biomolecules under test. The simulation results show that the resonance frequency has a red shift with the increase of voltage, and the maximum red shift is up to 40 GHz. This paper provides experimental and theoretical foundations for building miniaturized and integrated biomolecular THz sensing systems.

Abstract:Microwave, millimeter wave and terahertz noise sources are mainly employed to generate noise signals, and they are the core components of noise figure measurement system. This paper has summarized the development of noise sources at home and abroad in recent years. The exiting noise sources are divided into five categories: based on digital and analog circuit technologies, based on semiconductor diode technology, based on field effect transistor technology, based on black body and heater circuit technologies, based on photoelectric fusion technology. The main technical characteristics of these five types of noise sources are analyzed. The research status of different noise sources are compared, and the application prospects and development trends are outlined.

Abstract:Frequency Selective Surface(FSS) has been widely used in many multi-channel receiving systems, where large angle incidence and multi-polarization receiving are required in many cases. A frequency selective surface is designed using a cross-dipole combined with a square patch to transmit (55±5) GHz and reflect (33±5) GHz signals. The signal for (55±5) GHz is in perpendicular polarization, and for (33±5) GHz it is in parallel polarization. The prototype is fabricated using Printed Circuit Board(PCB) technology and measured by free space method. It is demonstrated that good agreement with simulation is obtained.

Abstract:The geostationary orbit microwave radiometer antenna is composed of three large diameter reflectors. The orbit heat flow is complex. The reflector and its supporting structure will undergo large-scale deformation due to severe temperature changes. This leads to a decrease in the efficiency of the main beam and a deviation of the beam pointing. The single-freedom tolerance analysis of the antenna reflector is carried out. Based on the fully automatic multi-freedom tolerance analysis system, the relationship between antenna mechanical error and electrical performance is obtained. And the correctness of the tolerance value of each freedom is verified based on the Monte Carlo tolerance analysis method. Finally, the near-field test system is employed to test the main beam efficiency and beam pointing under the antenna tolerance state. Comparing and analyzing the measured tolerance value with the semi-physical simulation value and design value verifies the correctness of the tolerance analysis.

Abstract:Conventional radar altimetry parameter estimation algorithms often suffer from overfitting due to the high dimensionality of the parameters to be estimated. To this end, a novel Proximal Hamiltonian Monte Carlo(PHMC) algorithm is proposed to estimate the elevation parameters in a statistical way. More specifically, Laplace distribution is employed to characterize the sparse prior to achieve the confidence estimation for the elevation parameters. This prior can depict the terrain scenes with abrupt elevation changes. However, due to the non-conjugation between the sparse prior and Gaussian likelihood function, the hierarchical Bayesian is employed to obtain the closed-form solution of posterior distribution function. To overcome the difficulty of the Bayesian inference of high-dimensional posterior, the Hamiltonian Monte Carlo(HMC) is utilized to solve the parameter estimation problem in fully Bayesian inference. Since the potential energy obtained by posterior distribution does not satisfy the differentiable requirement of HMC, the proximal operator is applied to provide the sub-gradient to estimate parameters. Comparisons with the results using synthesis and practical data have demonstrated the superiority of the proposed PHMC over other conventional algorithms.

Abstract:A Blind Source Separation(BSS) algorithm based on Empirical Mode Decomposition-Non-Linear Principal Component Analysis(EMD-NLPCA) is proposed after studying the BSS algorithm for underdetermined non-linear mixed signals. Firstly, EMD is applied to the observed signal, then high-order statistics are introduced after reconstructing the signal. The principal component analysis is carried out to complete the signal separation. This algorithm can not only deal with the undetermined environment but also solve the problem of non-linear mixing. In the simulation, the results of the algorithm are compared with those of the sparse component analysis, which proves that the proposed algorithm is correct and more universal than the sparse component analysis. Finally, the algorithm is applied to the separation of driving audio signals of unmanned aerial vehicle engines, and it works well.

Abstract:Detecting and locating buried objects from complex and diverse Ground Penetrating Radar(GPR) imaging is labor-intensive and time intensive. A method based on deep learning is proposed. The quantitative analysis on arbitrary targets is performed by using the Fully Convolutional One-Stage(FCOS) object detection algorithm. The target area is tracked and labeled with clustering tags. The precise location of underground target is obtained by the curve fitting. The information is reconstructed for the buried underground target. The simulation results show that this method avoids the complex calculation required by the traditional processing algorithm, and can quickly detect the target. The position and dielectric properties of the target are estimated with high precision, and the positioning error in depth is below 3 cm. Therefore, this method effectively realizes the reconstruction of the position, depth and size of the target in the underground scene.

Abstract:Although modulation classification based on deep neural network can achieve high Modulation Classification(MC) accuracies, catastrophic forgetting will occur when the neural network model continues to learn new tasks. In this paper, we simulate the dynamic wireless communication environment and focus on breaking the learning paradigm of isolated automatic MC. We innovate a research algorithm for continuous automatic MC. Firstly, a memory for storing representative old task modulation signals is built, which is employed to limit the gradient update direction of new tasks in the continuous learning stage to ensure that the loss of old tasks is also in a downward trend. Secondly, in order to better simulate the dynamic wireless communication environment, we employ the mini-batch gradient algorithm which is more suitable for continuous learning. Finally, the signal in the memory can be replayed to further strengthen the characteristics of the old task signal in the model. Simulation results verify the effectiveness of the method.

Abstract:Single Event Upset(SEU) effects are a common cause of processor failures in aerospace environments, necessitating effective protective designs to enhance the reliability of high-altitude equipment in the fields of aviation and astronautics. Traditional embedded reliability protection designs typically employ either single hardware or software approaches: implementing Triple Modular Redundancy(TMR) through software requires substantial CPU resources; employing hardware circuits does not facilitate error reporting.This paper focuses on the PPC460 processor as the target system, discussing an advanced reliability enhancement design method utilizing Field-Programmable Gate Array(FPGA) technology for the PPC460 processor. The approach integrates an extended Hamming code encoding and decoding algorithm, parity checking, and Triple Modular Redundancy techniques. By synergistically combining software and hardware strategies, it improves the correctness of data within the storage space, reduces CPU resource consumption, and effectively realizes high-security, high-reliability, and interference-resistant protection for critical data on the PPC460 processor in special complex environments.