Accurately estimating rotor rotation frequency of Unmanned Aerial Vehicle (UAV) is of great significance for UAV detection and recognition. For the UAV target echo model of LFMCW (Linear Frequency Modulated Continuous Wave) radar, this paper proposes an auto-correlation and cepstrum to estimate the rotor rotation frequency of UAV, which derives the mapping relationship between the rotor rotation frequency of UAV and the periodic delay in the radar echo cepstrum output, and more effectively estimates the rotor frequency of multi-rotor UAV by weighted equilibrium, making up for the shortages of traditional methods. The effectiveness of the method is verified by simulation and real scene experiments.

For the Inverse Synthetic Aperture Radar (ISAR) imaging, the ISAR image obtained by the Range-Doppler (RD) or time-frequency analysis methods can not display the target's real shape due to its azimuth relating to the target Doppler frequency, thus the cross-range scaling is required for ISAR image. In this paper, a fast cross-range scaling method for ISAR is proposed to estimate the Rotational Angular Velocity (RAV). Firstly, the proposed method utilizes efficient Pseudo Polar Fast Fourier Transform (PPFFT) to transform the rotational motion of two ISAR images from two different instant time into translation in the polar angle direction. Then, a new cost function called integrated correction is defined to obtain the RAV coarse estimation. Finally, the optimal RAV can be estimated using the Bisection method to realize the cross-range scaling. Compared with the available algorithms, the proposed method avoids the problems of precision loss and high computational complexity caused by interpolation operation. The results of computer simulation and real data experiments are provided to demonstrate the validity of the proposed method.

Inspired by the idea of multi-antenna interferometric processing in Interferometric Inverse Synthetic Aperture Radar (InISAR), by utilizing an L-shaped three-antenna imaging model, a Three-Dimensional (3-D) interferometric imaging and micro-motion feature extraction method for rotating space targets is proposed. Based on the integration of micro-Doppler (m-D) effect theory and multi-antenna interferometry processing technology, the m-D curves corresponding to different scatterers are obtained on the time-frequency plane and separated via Viterbi algorithm effectively, and then the projected coordinates of scatterers along the direction of baselines are reconstructed by interferometric processing. The height information of scatterers is solved by ellipse fitting, and 3-D imaging for the rotating space target is realized. Meanwhile, some 3-D micro-motion features are exactly extracted during imaging. Simulation results validate the effectiveness and the robustness of the method.

Incoherent scatter spectrum plays an important role in studying the physical parameters of the ionosphere. The conventional theoretical model of incoherent scatter spectrum for derivation and calculation is extremely complicated and the model of the autocorrelation function can not be obtained . In this paper, the simplified model of ionospheric incoherent scatter spectrum is re-derived and the corresponding autocorrelation function is proposed. In the procedure of traditional incoherent scattering radar signal processing, the autocorrelation function is imbalance at different delays. This is mainly because the range resolution of zero-lag is very low, which affects the estimated performance of ionospheric scatter spectrum. Focus on this problem, a method based on data fitting is proposed to estimate the autocorrelation at zero-lag. Considering the computational complexity, a fast implementation method by polynomial functions is proposed to approach the autocorrelation function. Finally, experimental results on real echo data demonstrate the correctness and efficiency of the proposed method, which is of great significance for ionospheric detection.

Multiband fusion imaging can effectively improve the range resolution of Inverse Synthetic Aperture Radar (ISAR) imaging. The traditional Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) spectral estimation signal fusion algorithm uses only the complex measured data without using their conjugate data. This paper proposes to modify the unitary ESPRIT method, which is based on synthesizing complex observation data and its conjugate data, to achieve unitary ESPRIT based multiband fusion ISAR imaging. The unitary ESPRIT method makes full use of the information of complex observations, which is more beneficial to multiband frequency spectrum estimation and ISAR imaging. Furthermore, for the correction of Migration Through Resolution Cell (MTRC) of scatterers in multiband fusion, the traditional processing flow is adjusted and optimized. The migration through range cell correction and the migration through Doppler cell correction are performed before and after the multiband fusion respectively, which avoids the influence of the fast time frequency - slow time coupling in the echo and the phase compensation on the spectrum fusion processing, thereby a better multiband fusion ISAR image is obtained. Simulation and real data experimental results show that the proposed methods can not only get high quality ISAR images, but also have good antinoise performance and higher computational efficiency.

The general method for inversion of Digital Surface Model (DSM) in forest region has great errors due to the inestimable waves’ penetration depth. For this problem, an approach to inversion of high-precision DSM is proposed. First, the phases of high and low scattering phase centers of the waves in forest are obtained by maximizing the phase separation of the coherence optimization. Then, the normal height variation models of the high and low scattering centers with extinction factors are constructed. According to the models, the least penetration depth of the waves in forest is acquired. Eventually, by implementing the interferometric technique on the phase of high scattering phase center, a coarse DSM is retrieved, and a high-precision DSM is developed by compensating the least penetration depth to the coarse one. The validation of the method is investigated by simulated datasets of PolSARpro under different tree species and different forest heights and by airborne real datasets. It shows that the proposed method can improve the accuracy on the inversion of DSM effectively in forest region.

Surface defects such as gaps have a significant impact on the stealth performance of the aircraft. According to the scattering mechanism of surface defects such as gaps, a method of evaluating the surface defect targets based on vector cancellation is proposed. The carrier is regarded as the target background and is subtracted the scattering effect of the carrier, especially the strong scattering angle of the carrier, and then the scattering characteristic data of the surface defect class target in the whole angle range are obtained, which solves the problem that the complete scattering characteristic can not be obtained using the conventional method. The comparison between the numerical calculation and the experimental results shows that the vector cancellation method can effectively evaluate the electromagnetic scattering characteristics of defective targets. After vector cancellation, the scattering of the carrier is greatly reduced, and the calculation or measurement accuracy of the defective target is effectively improved. At the same time, due to reducing the influence of the scattering of the carrier itself, this method avoids the requirement of the carrier size and ultra-low scattering characteristics, and reduces effectively the processing cost of the carrier.

At present, long-code with the characteristic of high bit rate and long period is widely used in satellite navigation system as military signal. To overcome the shortcomings of the indirect acquisition method for long-code, the limitations of the extended replication overlay acquisition algorithm and the mean acquisition algorithm, an improved time-frequency capture method based on the Extended replica Folding Acquisition Search Technique (XFAST) and the mean algorithm is proposed. The feasibility of the algorithm is simulated and the performance of the algorithm is analyzed. The simulation experiment shows the superiority of the proposed algorithm.

A low power and cost BeiDou-reflectometry used to retrieve Significiant Wave Height (SWH) and wind is designed and implemented. To improve the retrieval accuracy, a correction method based on the power function of the elevation angle sinusoidal and a delay correlation for the rapid change of wind speed is proposed. Moreover, combined observation of multi-satellite signals and single-side filtering for the observable are performed to improve further the retrieval accuracy. The experiment results of observating SWH and wind speed using reflected BeiDou signals show that designed and developed system could implement long-term and stable observation; the retrieval accuracies of SWH and wind speed retrieved by propsoed retrieval models and improvement methods of the retreival accuracy are 0.13 m and 1.28 m/s which are 0.13 m and 0.78 m/s higher than the methods proposed by Soulat et al.

Keystone transform is an effective broadband array signal pre-processing method, but it has a main problem of array data missing. In order to solve this problem, an enhanced Keystone transform algorithm, which combines the autoregression model with traditional Keystone transform, is proposed in this paper for sonar broadband adaptive beamforming. After phase alignment of broadband array signal using traditional Keystone transform, autoregression models for each frequency are constructed to compensate the missing array data. Then, a robust adaptive beamforming approach is utilized to obtain the target bearing results. The results of simulation studies indicate that the proposed broadband adaptive beamforming algorithm based on enhanced Keystone transform outperforms the beamforming algorithms based on traditional Keystone transform, steered minimum variance and frequency focusing.

Magnetic Anomaly Detection (MAD) is a widely used passive target detection method. Its applications include surface warship target monitoring, underwater moving targets, and land target detection and identification. It is of great significance to research on the reliability detection method of weak magnetic anomaly signals based on geomagnetic background. This paper proposes a single sensor detection method based on the fractal characteristics of target magnetic anomaly signal based on the study of the differences in geomagnetic background and fractal characteristics of magnetic anomaly signals and conducts actual field test verification. The experimental results show that the method can accurately distinguish the geomagnetic background interference and magnetic anomaly signals, and can detect the weak magnetic anomaly signals in the geomagnetic background noise.

In order to resist the malware sandbox evasion behavior, improve the efficiency of malware analysis, a code-evolution-based sandbox evasion technique for detecting the malware behavior is proposed. The approach can effectively accomplish the detection and identification of malware by first extracting the static and dynamic features of malware software and then differentiating the variations of such features during code evolution using sandbox evasion techniques. With the proposed algorithm, 240 malware samples with sandbox-bypassing behaviors can be uncovered successfully from 7 malware families. Compared with the JOE analysis system, the proposed algorithm improves the accuracy by 12.5% and reduces the false positive to 1%, which validates the proposed correctness and effectiveness.

Side channel attacks have serious threat to the hardware security of Advanced Encryption Standard (AES), how to resist the side channel attack becomes an urgent problem. Byte substitution operation is the only nonlinear operation in AES algorithm, so it is very important for the whole encryption algorithm to improve its security. In this paper, a countermeasure against side-channel attack is proposed based on random addition-chain for AES by replacing the fixed addition-chain with random addition-chain to realize the inverse operation of multiplication in a finite field GF(2⁸). The impact of the random addition-chain on the security and effectiveness of the algorithm is studied. Experimental results show that the proposed random addition-chain based algorithm is more secure and effective than the previous fixed addition-chain based algorithms in defending against side channel attacks.

Existing attribute-based deduplication schemes can support neither auditing of cloud storage data nor revocation of expired users. On the other hand, they are less efficient for deduplication search and users decryption. In order to solve these problems, this paper proposes an efficient deduplication and auditing Attribute-Based Encryption (ABE) scheme. A third-party auditor is introduced to verify the integrity of cloud storage data. Through an agent auxiliary user revocation mechanism, the proposed scheme supports the revocation of expired users. Effective deduplication search tree is put forward to improve the search efficiency, and the proxy decryption mechanism is used to assist users to decrypt. Finally, the security analysis shows that the proposed scheme can achieve IND-CPA security in the public cloud and PRV-CDA security in the private cloud by resorting to the hybrid cloud architecture. The performance analysis shows that the deduplication search is more efficient and the computation cost of user encryption is smaller.

One of the major drawbacks of the conventional Multiuser Differential Chaos Shift Keying is the poor Bit Error Rate (BER), a MultiUser Noise Reduction Differential Chaos Shift Keying (MU-NRDCSK) system is proposed. At the transmitter, M/P chaotic samples are transmitted and then duplicated P times as a reference signal, all users share the same reference signal, and information signals are delayed by different times to distinguish different users. At the receiver, the received signal is averaged by a moving average filter, and then the resultant filtered signal is correlated to different time-delated replica. The scheme can enhance the performance of BER by reducing the variance of noise terms in the system. The theoretical BER formula of this new scheme is derived in Additive White Gaussian Noise (AWGN) channel and Rayleigh channel. Theoretical analysis and the simulation results show that the theoretical formula and the simulation result are in good agreement. The MU-NRDCSK scheme can enhance the performance of BER better and has good development prospects and research value in the chaotic communication field.

Imbalanced workload distribution results in low resource utilization of many-core crypto-platform. Dynamic workload allocation can improve the resource utilization with some overhead. Therefore, a higher frequency of workload balancing is not equivalent to higher gains. This paper establishes a mathematical model for gain rate and frequency of workload balancing. Based on this model, a collision-free workload balancing policy is proposed for many-core crypto systems, and a hierarchical "expandable-portable" engine is put forward, which consists of "Inter-cluster micro-network and intra-cluster ring-array" adopting hardware job queue technology. Experiment results show that the proposed workload-balancing engine is 4.06, 7.17, 23.01% and 2.15 times higher than the software technology based on " job stealing” in terms of performance, delay power consumption, resource utilization and workload balance; 1.75, 2.45, 10.2%, and 1.41 times better compared with the hardware technology based on "job stealing". By contrast with the ideal hardware technology, the average throughput of encryption algorithms is only decreased by 5.67% (the lowest 3%). The experiment also proves the scalability and portability of the proposed technique.

Virtualization is a new technology that can effectively solve the low resource utilization and service inflexibility problem in the current Wireless Sensor Network (WSN). For the resource competition problem in virtualized WSN, a multi-task resource allocation strategy based on Stackelberg game is proposed. According to the different Quality of Service (QoS) requirements of the business carried by Virtual Sensor Network Request (VSNR), the importance of multiple VSNRs is quantified. Then, the optimal price of WSN and the optimal resource requirements of VSNRs are obtained by using distributed iteration method. Finally, the resource corresponding to multiple VSNRs is acquired according to optimal price and optimal resource allocation determined by Nash equilibrium. The simulation results show that the proposed strategy can not only meet the diversified needs of users, but also improve the resource utilization of nodes and links.

Considering that it is difficult to balance efficiency and resource utilization of Service Chain (SC) mapping problem in Software Defined Network (SDN)/Network Function Virtualization (NFV) environment, this paper proposes a collaborative mapping method for SC based on matching game. Firstly, it defines a SC mapping model named MUSCM to maximize the utility of network resources. Secondly, it divides the SC mapping problem into Virtual Network Function (VNF) deployment and connection parts. As for the VNF deployment part, an algorithm is designed to collaborate the selection of the SC and the service node based on many-to-one matching game, improving the mapping efficiency of SC and utilization of physical resource effectively. On the basis of it, an algorithm is designed based on segment routing strategy to accomplish the traffic steering between VNF instances to finish the VNF connection part, reducing the link transmission delay effectively. The experiment result shows that, compared with the classical algorithm, this algorithm ensures the mapping request received rate, and at the same time, it reduces the average transmission delay of the service chain and improves the physical resources utilization of the system effectively.

In cloudlet enhanced Fiber-Wireless (FiWi) access network, there is a problem that the traditional energy saving mechanism does not match the offload traffic. An offload collaboration sleep mechanism with load transfer is proposed. By analyzing the load of the optical network unit and combining the transmission delay of the multi-hop in the wireless domain and the sending time of the report frame of the target optical network unit, the proposed mechanism can determine the sleeping and the destination optical network unit to complete load transfer. Then, the optical network unit jointly considers the arrival time of the returned data of the edge severs and the sending time of the control frame in the wireless domain to select the optimal sleep duration and reduce the controlling overhead. Simulation results show that the proposed mechanism can effectively reduce the network energy consumption while ensuring the delay performance of offload traffic.

For the full-duplex two-way relay network, a two-way relay transmission scheme that is robust to the relay residual self-interference signal is proposed. Firstly, the residual self-interference signal of the relay is analyzed, the infinite self-interfering signal is modeled as an equivalent multipath signal, and the cyclic prefix of OFDM is used to combat the equivalent multipath phenomenon to reduce the residual self-interference signal impact. Based on the equivalent multipath scheme, the paper aims at maximizing the SINR of the system, and deduces the optimal amplification factor solving method of the relay in bidirectional full-duplex relay transmission. Finally, the simulation verifies the correctness of the optimal amplification factor of relay, and the effectiveness of the proposed two-way relay transmission scheme is verified through simulation.

This paper investigates the design of hybrid analog and digital precoder and combiner for multi-user millimeter wave MIMO systems. Considering the problem of signal interference between multiple users due to diffuse scattering of signal propagation, a robust hybrid precoding algorithm based on Successive Interference Cancellation (SIC) is proposed. By deducing the orthogonal decomposition formula of the channel matrix to eliminate the interference from the known users’ signals, the multi-user links optimization problem with nonconvex constraints can be decompose into multiple single-user link optimization problems. The phase extraction algorithm is then used to search each user’s optimal transmission link one by one, and the multi-user hybrid precoding matrix is obtained in combination with Minimum Mean Square Error (MMSE) criterion. Simulation results show that the proposed algorithm has significant performance advantages compared with the existing hybrid precoding algorithms under severe interference conditions.

To solve problem of the high delay caused by the change of physical network topology under the 5G access network C-RAN architecture, this paper proposes a scheme about dynamic deployment of Service Function Chain (SFC) in access network based on Partial Observation Markov Decision Process (POMDP). In this scheme, the system observes changes of the underlying physical network topology through the heartbeat packet observation mechanism. Due to the observation errors, it is impossible to obtain all the real topological conditions. Therefore, by the partial awareness and stochastic learning of POMDP, the system dynamically adjust the deployment of the SFC in the slice of the access network when topology changes, so as to optimize the delay. Finally, point-based hybrid heuristic value iteration algorithm is used to find SFC deployment strategy. The simulation results show that this model can support to optimize the deployment of SFC in the access network side and improve the access network’s throughput and resource utilization.

Due to the probabilistic failure of the optical fiber of the underlying network in the virtual environment, traditional full protection configures one protection path at least which leads to high resource redundancy and low acceptance rate of the virtual network. In this paper, a Security Awareness-based Diverse Virtual Network Mapping (SA-DVNM) strategy is proposed to provide security guarantee in the event of failures. In SA-DVNM, the physical node weight formula is designed by considering the hops between nodes and the bandwidth of adjacent links, besides, a path-balanced link mapping mechanism is proposed to minimize the overloaded link. For improving the acceptability of virtual network, SA-DVNM strategy designs a resource allocation mechanism that allows path cut when a single path is unavailable for low security. Considering the difference of time delay to ensure the security of delay-sensitive services, a multipath routing spectrum allocation method based on delay difference is designed to optimize the routing and spectrum allocation for SA-DVNM strategy. The simulation results show that the proposed SA-DVNM strategy can improve the spectrum utilization and virtual optical network acceptance rate in the probabilistic fault environment, and reduce the bandwidth blocking probability.

A wide area difference calibration algorithm based on Virtual Reference Station (VRS) for tri-satellite Time Difference Of Arrival (TDOA) geolocation system is proposed to solve the problem that traditional difference calibration algorithm can not eliminate the location error caused by ephemeris error completely, especially when the emitter source is far away from the calibration station. Firstly, TDOA measurements of the VRS, which is in the vicinity of emitter source, is estimated by using TDOA measurements of reference station. Then, in order to remove the effect of ephemeris error and synchronization error on location error, TDOA measurements of the VRS is subtracted from that of emitter source. Simulation results demonstrate that the proposed algorithm can almost eliminate the effect of ephemeris error on location error of tri-satellite TDOA geolocation system in wide area.

Device-free passive localization is a key issue of the intruder detection, environmental monitoring, and intelligent transportation. The existing device-free passive localization method can obtain the multidimensional measurement information by channel state information, but the existing scheme can not fully exploit the frequency diversity on multiple channels to improve the localization performance. This paper proposes a Compressive Sensing (CS) based multi-target device-free passive localization algorithm using multidimensional measurement information. It takes advantage of the frequency diversity of multidimensional measurement information to improve the accuracy and robustness of localization results under the CS framework. The dictionary is built according to the saddle surface model, and the multi-target device-free passive localization problem is modeled as a joint sparse recovery problem based on multiple measurement vectors. The target location vector is estimated based on the multiple sparse Bayesian learning algorithm. Simulation results indicate that the proposed algorithm can make full use of the multidimensional measurement information to improve the localization performance.

For the fact that current gridless Direction Of Arrival (DOA) estimation methods with two-dimensional array suffer from unsatisfactory performance, a novel girdless DOA estimation method is proposed in this paper. For two-dimensional array, the atomic L0-norm is proved to be the solution of a Semi-Definite Programming (SDP) problem, whose cost function is the rank of a Hermitian matrix, which is constructed by finite order of Bessel functions of the first kind. According to low rank matrix recovery theorems, the cost function of the SDP problem is replaced by the log-det function, and the SDP problem is solved by Majorization-Minimization (MM) method. At last, the gridless DOA estimation is achieved by Vandermonde decomposition method of semidefinite Toeplitz matrix built by the solutions of above SDP problem. Sample covariance matrix is used to form the initial optimization problem in MM method, which can reduce the iterations. Simulation results show that, compared with on-grid MUSIC and other gridless methods, the proposed method has better Root-Mean-Square Error (RMSE) performance and identifiability to adjacent sources; When snapshots are enough and Signal-Noise-Ratio (SNR) is high, proper choice of the order of Bessel functions of the first kind can achieve approximate RMSE performance as that of higher order ones, and can reduce the running time.

The signal source position can only be estimated by passive monitoring of the signal in terms of that the signal monitored by the spectrum monitoring system can not be controlled and there is no prior knowledge. To address this issue, based on Received Signal Strength Indication Difference (RSSID) and using Kalman filtering, a location algorithm is proposed to improve its localization accuracy. The proposed algorithm transforms the RSSID between two base stations into the ratio of the distance from the location of the signal source to the two base stations, and the distances to construct the matrix of location equations is obtained according to the ratio, and then the least square method to find the signal source position is obtained. The simulation results show that the proposed algorithm has better performance than the classical RSSI localization algorithm, reducing the impact of environmental factors on the positioning accuracy, and better meet the positioning service needing fewer parameters. This algorithm can be effectively applied to the spectrum monitoring system. In addition, Kalman algorithm can effectively improve the system's positioning accuracy, and achieve the expected positioning effect.

This paper focuses on the sensor selection optimization problem in Time Difference Of Arrival (TDOA) passive localization scenario. Firstly, the localization accuracy metric is given by the error covariance matrix of classical closed-form solution, which is introduced to convert the TDOA nonlinear equations into pseudo linear equations. Secondly, the problem of sensor selection can be mathematically transformed into the non-convex optimization problem, to minimize the trace of localization error covariance matrix under the condition that the number of active sensors is given. Then, the non-convex optimization problem is relaxed and transformed into a positive semi-definite programming problem so that the optimal subset of positioning nodes can be solved quickly and effectively. Simulation results validate that the performance of proposed sensor selection method is very close to the exhausted-search method, and overcomes the shortcomings of the high computation complexity and poor timeliness of the exhausted-search method.

To improve accuracy and reliability of the traditional turbine-vital capacity meter, a novel four-line turbine-detection method is presented for the high precision and high reliability Chronic Obstructive Pulmonary Disease (COPD) monitoring system. On the hardware, a four-line breath signal acquisition circuit is designed following the four-line turbine-type detection method, which improves the resolution of the optical path through reasonable components arrangement. On the software, a linear regression algorithm is used to obtain early screening and diagnostic indicators such as Forced Vital Capacity (FVC), Peak Expiratory Flow (PEF) and so on. The standard Fluke air flow analyzer is used for data calibration, compared with the traditional medical turbine-type lung function meter: FVC average relative error is reduced from 1.98% to 1.47% and PEF average relative error is reduced from 2.04% to 1.02%. It is showed that the expiratory parameters of the four-line turbine-type COPD monitoring system is more accurate and reliable than that of  the traditional COPD system which is suitable for early screening and accurate diagnosis of COPD. Combined with pulse oxygen saturation, End-tidal CO2, it can be used to achieve the medical care for COPD and play an important role to early detect and control of disease for moderate or severe COPD patients.

In order to improve the robustness of MLAPG algorithm, a person re-identification algorithm, called Equid-MLAPG algorithm is proposed, which is based on the equidistance measurement learning strategy. Due to the imbalanced distribution of positive and negative sample pairs in the mapping space, sample spacing hyper-parameter of MLAPG algorithm is more affected by the distance of negative sample pairs. Therefore, Equid-MLAPG algorithm tends to map the positive sample pair to be a point in the transform space. That is, the distance of a positive sample pair in the transform space is mapped to be zero, resulting in no intersection in the distribution of positive and negative sample pairs in the transform space when algorithm convergences. Experiments show that the Equid-MLAPG algorithm can achieve better experimental results on commonly used person re-identification datasets with better recognition rate and wide applicability.

To solve the low performance problem of the existing Modulated Wideband Converter (MWC)-based sub-Nyquist sampling recovery algorithm, this paper proposes a support recovery algorithm based on the kernel space of sampling value and a random compression rank-reduction idea. Combining them, a high-performance sampling recovery algorithm is achieved. Firstly random compression transforms are used to convert the sampling equation into several new multiple-measurement-vector problems, without changing the sparsity of the unknown matrix. Then the orthogonal relationship between the kernel space of sampling value and the support vectors of sampling matrix is utilized to obtain joint sparse support set of the unknown. The final recovery is performed by the pseudo inversion. The proposed method is analyzed and verified by theory and experiment. Numerical experiments show that, compared with the traditional recovery algorithm, the proposal can improve the recovery success rate, and reduce the channel number required for high-probability recovery. Furthermore, in general, the recovery performance improves with the rise of compression times.

In view of the problem that the Cardinalized Probability Hypothesis Density (CPHD) probability hypothesis density filtering algorithm based on the Pairwise Markov Chains (PMC) model (PMC-CPHD) is not suitable for implementation, the PMC-CPHD algorithm is modified into a polynomial form to facilitate implementation, and the Gauss Mixture (GM) implementation of the improved algorithm is given. The experimental results show that the given GM implementation realizes multitarget tracking effectively, and improves the stability of the target number estimation compared with the GM implementation of the probability hypothesis density filtering algorithm based on the PMC model (PMC-PHD).

Honeypot technology is a network trap in cyber defense. It can attract and deceive attackers and record their attack behavior, so as to study the target and attack means of the adversary and protect real service resources. However, because of the static configuration and the fixed deployment in traditional honeypots, it is as easy as a pie for intruders to identify and escape those traps, which makes them meaningless. Therefore, how to improve the dynamic characteristic and the camouflage performance of honeypot becomes a key problem in the field of honeypot. In this paper, the recent research achievements in honeypot are summarized. Firstly, the development history of honeypot in four stages is summed up. Subsequently, by focusing on the key honeypot mechanism, the analysis on process, deployment, counter-recognition and game theory are carried out. Finally, the achievements of honeypot in different aspects are characterized and the development trends of honeypot technology is depicted.