The measurement of the rolling angle of the projectile is one of the key technologies for the terminal correction projectile. To improve the resolution accuracy of the rolling angle in the laser seeker weapon system, the imaging model of the detector, calculation model of the position and the signal-to-noise ratio (SNR) model of the circuit are built to derive both the correlation between the resolution error of the rolling angle and the spot position, and the relation between the position resolution error and the SNR. Then the influence of each parameter on the SNR is analyzed at large, and the parameters of the circuit are determined. Meanwhile, the SNR and noise voltage of the circuit are calculated according to the SNR model and the decay model of the laser energy. Finally, the actual photoelectric detection circuit is built, whose SNR is measured to be up to 53 dB. It can fully meet the requirement of 0.5° for the resolution error of the rolling angle, thereby realizing the analysis of critical technology for photoelectric detection of laser seeker signals.
The approach of estimating the number of signals based on information theoretic criteria has good performance in the assumption of white noise, but it always leads to false estimation of the coherent sources in colored noise. An approach combining the combined information theoretic criteria and eigenvalue correction, is presented to determine number of signals. The method uses maximum likelihood (ML) and information theoretic criteria to estimate coherent signals alternately, then eliminate the inequality of the eigenvalues caused by colored noise by correcting the noise eigenvalues. The computer simulation results prove the effective performance of the method.
This paper investigates the feedback control of hidden Markov process (HMP) in the face of loss of some observation processes. The control action facilitates or impedes some particular transitions from an inferred current state in the attempt to maximize the probability that the HMP is driven to a desirable absorbing state. This control problem is motivated by the need for judicious resource allocation to win an air operation involving two opposing forces. The effectiveness of a receding horizon control scheme based on the inferred discrete state is examined. Tolerance to loss of sensors that help determine the state of the air operation is achieved through a decentralized scheme that estimates a continuous state from measurements of linear models with additive noise. The discrete state of the HMP is identified using three well-known detection schemes. The sub-optimal control policy based on the detected state is implemented on-line in a closed-loop, where the air operation is simulated as a stochastic process with SimEvents, and the measurement process is simulated for a range of single sensor loss rates.
The non-minimum phase feature of tail-controlled missile airframes is analyzed. Three selection strategies for desired performance indexes are presented. An acceleration autopilot design methodology based on output feedback and optimization is proposed. Performance and robustness comparisons between the two-loop and classical three-loop topologies are made. Attempts to improve the classical three-loop topology are discussed. Despite the same open-loop structure, the classical three-loop autopilot shows distinct characteristics from a two-loop autopilot with PI compensator. Both the two-loop and three-loop topologies can stabilize a static unstable missile. However, the finite actuator resource is the crucial factor dominating autopilot function.
Fiber optical gyroscope (FOG) is a highly reliable navigation element, and the degradation trajectories of its two accuracy indexes are monotonic and non-monotonic respectively. In this paper, a flexible accelerated degradation testing (ADT) model is used for analyzing the bivariate dependent degradation process of FOG. The time-varying copulas are employed to consider the dynamic dependency structure between two marginal degradation processes as the Wiener process and the inverse Gaussian process. The statistical inference is implemented by utilizing an inference function for the margins (IFM) approach. It is demonstrated that the proposed method is powerful in modeling the joint distribution with various margins.
This paper introduces the time-frequency analyzed long short-term memory (TF-LSTM) neural network method for jamming signal recognition over the Global Navigation Satellite System (GNSS) receiver. The method introduces the long short-term memory (LSTM) neural network into the recognition algorithm and combines the time-frequency (TF) analysis for signal preprocessing. Five kinds of navigation jamming signals including white Gaussian noise (WGN), pulse jamming, sweep jamming, audio jamming, and spread spectrum jamming are used as input for training and recognition. Since the signal parameters and quantity are unknown in the actual scenario, this work builds a data set containing multiple kinds and parameters jamming to train the TF-LSTM. The performance of this method is evaluated by simulations and experiments. The method has higher recognition accuracy and better robustness than the existing methods, such as LSTM and the convolutional neural network (CNN).
Equipment development planning (EDP) is usually a long-term process often performed in an environment with high uncertainty. The traditional multi-stage dynamic programming cannot cope with this kind of uncertainty with unpredictable situations. To deal with this problem, a multi-stage EDP model based on a deep reinforcement learning (DRL) algorithm is proposed to respond quickly to any environmental changes within a reasonable range. Firstly, the basic problem of multi-stage EDP is described, and a mathematical planning model is constructed. Then, for two kinds of uncertainties (future capability requirements and the amount of investment in each stage), a corresponding DRL framework is designed to define the environment, state, action, and reward function for multi-stage EDP. After that, the dueling deep Q-network (Dueling DQN) algorithm is used to solve the multi-stage EDP to generate an approximately optimal multi-stage equipment development scheme. Finally, a case of ten kinds of equipment in 100 possible environments, which are randomly generated, is used to test the feasibility and effectiveness of the proposed models. The results show that the algorithm can respond instantaneously in any state of the multi-stage EDP environment and unlike traditional algorithms, the algorithm does not need to re-optimize the problem for any change in the environment. In addition, the algorithm can flexibly adjust at subsequent planning stages in the event of a change to the equipment capability requirements to adapt to the new requirements.
A new kind of adaptive polarization filtering algorithm in order to suppress the angle cheating interference for the active guidance radar is presented. The polarization characteristic of the interference is dynamically tracked by using Kaiman estimator under variable environments with time. The polarization filter parameters are designed according to the polarization characteristic of the interference, and the polarization filtering is finished in the target cell. The system scheme of adaptive polarization filter is studied and the tracking performance of polarization filter and improvement of angle measurement precision are simulated. The research results demonstrate this technology can effectively suppress the an- gle cheating interference in guidance radar and is feasible in engineering.
A new kind of secure communication system which combines the chaotic encryption means with the conventional encryption method is discussed. With the analysis results and the experiment data, the anti-attack ability of this communication system is significantly improved compared to that of the either method. At the same time, a new method of chaotic synchronization is proposed. With a small mixed discrete chaotic signal, it is quickly to synchronize the communication and a good security performance is ensured.
To analyze and optimize the weapon system of systems (WSOS) scheduling process, a new method based on robust capabilities for WSOS scheduling optimization is proposed. First, we present an activity network to represent the military mission. The member systems need to be reasonably assigned to perform different activities in the mission. Then we express the problem as a set partitioning formulation with novel columns (activity flows). A heuristic branch-and-price algorithm is designed based on the model of the WSOS scheduling problem (WSOSSP). The algorithm uses the shortest resource-constrained path planning to generate robust activity flows that meet the capability requirements. Finally, we discuss this method in several test cases. The results show that the solution can reduce the makespan of the mission remarkably.
The midcourse ballistic closely spaced objects (CSO) create blur pixel-cluster on the space-based infrared focal plane, making the super-resolution of CSO quite necessary. A novel algorithm of CSO joint super-resolution and trajectory estimation is presented. The algorithm combines the focal plane CSO dynamics and radiation models, proposes a novel least square objective function from the space and time information, where CSO radiant intensity is excluded and initial dynamics (position and velocity) are chosen as the model parameters. Subsequently, the quantum-behaved particle swarm optimization (QPSO) is adopted to optimize the objective function to estimate model parameters, and then CSO focal plane trajectories and radiant intensities are computed. Meanwhile, the estimated CSO focal plane trajectories from multiple space-based infrared focal planes are associated and filtered to estimate the CSO stereo ballistic trajectories. Finally, the performance (CSO estimation precision of the focal plane coordinates, radiant intensities, and stereo ballistic trajectories, together with the computation load) of the algorithm is tested, and the results show that the algorithm is effective and feasible.
The control law design for a near-space hypersonic vehicle (NHV) is highly challenging due to its inherent nonlinearity, plant uncertainties and sensitivity to disturbances. This paper presents a novel functional link network (FLN) control method for an NHV with dynamical thrust and parameter uncertainties. The approach devises a new partially-feedback-functional-link-network (PFFLN) adaptive law and combines it with the nonlinear generalized predictive control (NGPC) algorithm. The PFFLN is employed for approximating uncertainties in flight. Its weights are online tuned based on Lyapunov stability theorem for the first time. The learning process does not need any offline training phase. Additionally, a robust controller with an adaptive gain is designed to offset the approximation error. Finally, simulation results show a satisfactory performance for the NHV attitude tracking, and also illustrate the controller’s robustness.
Studies show that supply chain cooperation improves supply chain performance. However, it remains a challenge to develop and implement the realistic supply chain cooperation scheme. We investigate a two-echelon supply chain planning problem with capacity acquisition decision under asymmetric cost and demand information. A simple negotiation-based coordination mechanism is developed to synchronize production/order strategies of a supplier and a buyer. The coordination scheme shows how the supplier and the buyer modify their production and order policy in order to find a joint economic lot sizing plan, which saves the overall supply chain cost. The allocation of the cooperation benefit is determined by negotiation. Due to the complexity of the multiple periods, multiple level supply chain lot sizing with capacity decision, a heuristic algorithm is developed to find coordination solutions. Finally, the results of the numerical study indicate the performance of supply chain coordination scheme.
To enhance the resolution of parameter estimation with limited samples received by a short passive array, an iterative nonparametric algorithm for estimating the frequencies and direction-of-arrivals (DOAs) of signals is proposed. The cost function is constructed using l2-norm Gaussian entropy combined with an additional constraint, l2-norm constraint or linear constraint. By minimizing the cost functions in the temporal and the spatial dimensions using corresponding iteration algorithms respectively, the sparse discrete Fourier transforms (DFTs) of temporal and spatial samples are obtained to represent the extrapolated sequences with much larger sizes than the original samples. Then frequency and angle estimates are obtained by performing the traditional simple methods on the extrapolated sequences. It is shown that the proposed algorithm offers increased resolution and significantly reduced sidelobes compared with the periodogram and beamforming based methods. And it achieves high precision compared with the high-resolution method with lower computational burden. Some numerical simulations and real data processing results are presented to verify the effectiveness of the method.
A review on fault-tolerant control (FTC) for near space vehicle (NSV) is presented. First, the concept of near space is introduced, the background of NSV is emphasized, and the model characteristics of NSV in faulty case are investigated. Then, a comparison of different existing approaches is briefly carried out, and achievements on the current research in this field are also presented in the view of the practical application. Furthermore, several existing advanced FTC results for nonlinear flight control systems are given. Finally, the recent literature of NSV are presented to provide an overall view of future developments in this area.
This paper proposes a time-varying sliding mode control method to address nonlinear missile body kinematics based on the suboptimal control theory. The analytical solution of suboptimal time-varying sliding surface and the corresponding suboptimal control law are obtained by solving the state-dependent Riccati equation analytically. Then, the Lyapunov method is used to analyze the motion trend in sliding surface and the asymptotic stability of the closed-loop system is validated. The suboptimal control law is transformed to the form of pseudo-angle-of-attack feedback. The simulation results indicate that the satisfactory performance can be obtained and the control law can overcome the influence of parameter errors.
To solve the coupling effect of multiconductor transmission lines excited by external electromagnetic wave, the modified method of characteristics is proposed. The modified method of characteristics which can compute the terminal induced voltages excited by the external electromagnetic wave when the terminal networks or interconnection networks contain the dynamic elements is introduced. The simulation results indicate that the modified method can analyze the terminal induced voltages when the terminal networks or the interconnection networks contain the dynamic elements excited by the external electromagnetic wave. And the results are compared with the results acquired by FDTD method, the two results are completely same. So one effective modified method is implemented to compute the transmission lines.
WTA (weapon-target allocation) of air defense operation is a very complicated problem and current models focus on static and restricted WTA problem mostly. Based on the dynamic characteristics of air defense operational command and decision of warships' formation, a dynamic WTA model is established. Simulation results show that switch fire and repetition fire of anti-air weapon systems affect the result of the air defense operation remarkably and the dynamic model is more satisfying than static ones. Related results are gained based on the analysis of the simulation results and the results are accordant with the intuitionistic tactical judgment. The model is some reference for the research of air defense C?I system of warships' formation.
Aiming at the suppression of enemy air defense (SEAD) task under the complex and complicated combat scenario, the spatiotemporal cooperative path planning methods are studied in this paper. The major research contents include optimal path points generation, path smoothing and cooperative rendezvous. In the path points generation part, the path points availability testing algorithm and the path segments availability testing algorithm are designed, on this foundation, the swarm intelligence-based path point generation algorithm is utilized to generate the optimal path. In the path smoothing part, taking terminal attack angle constraint and maneuverability constraint into consideration, the Dubins curve is introduced to smooth the path segments. In cooperative rendezvous part, we take estimated time of arrival requirement constraint and flight speed range constraint into consideration, the speed control strategy and flight path control strategy are introduced, further, the decoupling scheme of the circling maneuver and detouring maneuver is designed, in this case, the maneuver ways, maneuver point, maneuver times, maneuver path and flight speed are determined. Finally, the simulation experiments are conducted and the acquired results reveal that the time-space cooperation of multiple unmanned aeriel vehicles (UAVs) is effectively realized, in this way, the combat situation suppression against the enemy can be realized in SEAD scenarios.
Navigation via signals of opportunity (NAVSOP) is able to realize positioning by making use of hundreds of different signals that are all around us. A method to realize NAVSOP for low earth orbit (LEO) satellites is proposed in this paper, in which the global navigation satellite system (GNSS) authorized signals are utilized as the signal of opportunity (SOP). At first, the carrier recovery technique is studied under the premise that the pseudo-code is unknown. Secondly, a method based on characteristics of Doppler frequency shift is proposed to recognize the navigation satellites. Thirdly, the extended Kalman filter (EKF) is utilized to estimate the orbital parameters by using carrier phase measurements. Finally, the proposed method is evaluated by using signals generated by a satellite navigation data simulator. The simulation results show that the proposed method can successfully realize navigation via GNSS authorized signals.
Many practical systems in physics,biology,engineer- ing and information science exhibit impulsive dynamical behaviors due to abrupt changes at certain instants during the dynami- cal processes.The problems of finite-time stability analysis are investigated for a class of Markovian switching stochastic sys- tems,in which exist impulses at the switching instants.Multiple Lyapunov techniques are used to derive sufficient conditions for finite-time stochastic stability of the overall system.Furthermore, a state feedback controller,which stabilizes the closed loop sys- tems in the finite-time sense,is then addressed.Moreover,the controller appears not only in the shift part but also in the diffu- sion part of the underlying stochastic subsystem.The results are reduced to feasibility problems involving linear matrix inequalities (LMIs).A numerical example is presented to illustrate the proposed methodology.
The guaranteed cost control for a class of uncertain discrete-time networked control systems with random delays is addressed. The sensor-to-controller (S-C) and contraller-to-actuator (C-A) random network-induced delays are modeled as two Markov chains. The focus is on the design of a two-mode-dependent guaranteed cost controller, which depends on both the current S-C delay and the most recently available C-A delay. The resulting closed-loop systems are special jump linear systems. Sufficient conditions for existence of guaranteed cost controller and an upper bound of cost function are established based on stochastic Lyapunov-Krasovakii functions and linear matrix inequality (LMI) approach. A simulation example illustrates the effectiveness of the proposed method.
The realization of the parameter estimation of chirp signals using the fractional Fourier transform (FRFT) is based on the assumption that the sampling duration of practical observed signals would be equal to the time duration of chirp signals contained in the former. However, in many actual circumstances, this assumption seems unreasonable. On the basis of analyzing the practical signal form, this paper derives the estimation error of the existing parameter estimation method and then proposes a novel and universal parameter estimation algorithm. Furthermore, the proposed algorithm is developed which allows the estimation of the practical observed Gaussian windowed chirp signal. Simulation results show that the new algorithm works well.
Conventional PI control encounters some problems when dealing with large lag process in the presence of parameter uncertainties. For the typical first-order process, an observerbased linear active disturbance rejection control (LADRC) scheme is presented to cope with the difficulties, and a reduced-order observer scheme is proposed further. Some quantitative dynamic results with regard to non-overshoot characteristics are obtained. Finally, the performance boundaries of LADRC and PI control are explicitly compared with each other, which shows that the former is more superior in most cases.
The maximum likelihood (ML) estimator demonstrates remarkable performance in direction of arrival (DOA) estimation for the multiple input multiple output (MIMO) sonar. However, this advantage comes with prohibitive computational complexity. In order to solve this problem, an ant colony optimization (ACO) is incorporated into the MIMO ML DOA estimator. Based on the ACO, a novel MIMO ML DOA estimator named the MIMO ACO ML (ML DOA estimator based on ACO for MIMO sonar) with even lower computational complexity is proposed. By extending the pheromone remaining process to the pheromone Gaussian kernel probability distribution function in the continuous space, the proposed algorithm achieves the global optimum value of the MIMO ML DOA estimator. Simulations and experimental results show that the computational cost of MIMO ACO ML is only 1/6 of the MIMO ML algorithm, while maintaining similar performance with the MIMO ML method.
Radar detection of small targets in sea clutter is a particularly demanding task because of the nonstationary characteristic of sea clutter. The track-before-detect (TBD) filter is an effective way to increase the signal-to-clutter ratio (SCR), thus improving the detection performance of small targets in sea clutter. To cope with the nonstationary characteristic of sea clutter, an easily-implemented Bayesian TBD filter with adaptive detection threshold is proposed and a new parameter estimation method is devised which is integrated into the detection process. The detection threshold is set according to the parameter estimation result under the framework of information theory. For detection of closely spaced targets, those within the same range cell as the one under test are treated as contribution to sea clutter, and a successive elimination method is adopted to detect them. Simulation results prove the effectiveness of the proposed algorithm in detecting small targets in nonstationary sea clutter, especially closely spaced ones.
To study the diagnostic problem of Wire-OR (W-O) interconnect fault of PCB (Printed Circuit Board), five modified boundary scan adaptive algorithms for interconnect test are put forward. These algorithms apply Global-diagnosis sequence algorithm to replace the equal weight algorithm of primary test, and the test time is shortened without changing the fault diagnostic capability. The descriptions of five modified adaptive test algorithms are presented, and the capability comparison between the modified algorithm and the original algorithm is made to prove the validity of these algorithms.
With the rapidly growing abuse of drones, monitoring and classification of birds and drones have become a crucial safety issue. With similar low radar cross sections (RCSs), velocities, and heights, drones are usually difficult to be distinguished from birds in radar measurements. In this paper, we propose to exploit different periodical motions of birds and drones from high-resolution Doppler spectrum sequences (DSSs) for classification. This paper presents an elaborate feature vector representing the periodic fluctuations of RCS and micro kinematics. Fed by the Doppler spectrum and feature sequence, the long to short-time memory (LSTM) is used to solve the time series classification. Different classification schemes to exploit the Doppler spectrum series are validated and compared by extensive real-data experiments, which confirms the effectiveness and superiorities of the proposed algorithm.
The source location based on the hybrid time difference of arrival (TDOA)/frequency difference of arrival (FDOA) is a basic problem in wireless sensor networks, and the layout of sensors in the hybrid TDOA/FDOA positioning will greatly affect the accuracy of positioning. Using unmanned aerial vehicle (UAV) as base stations, by optimizing the trajectory of the UAV swarm, an optimal positioning configuration is formed to improve the accuracy of the target position and velocity estimation. In this paper, a hybrid TDOA/FDOA positioning model is first established, and the positioning accuracy of the hybrid TDOA/FDOA under different positioning configurations and different measurement errors is simulated by the geometric dilution of precision (GDOP) factor. Second, the Cramer-Rao lower bound (CRLB) matrix of hybrid TDOA/FDOA location under different moving states of the target is derived theoretically, the objective function of the track optimization is obtained, and the track of the UAV swarm is optimized in real time. The simulation results show that the track optimization effectively improves the accuracy of the target position and velocity estimation.
An optimal minimum mean square error successive interference cancellation (OMMSE SIC) scheme for Groupwise space-time block coding (G-STBC) multiple-input multiple-output (MIMO) systems is presented. In such a system, transmit antennas are partitioned into several STBC encoding groups and each group transmits independent data stream which is individually STBC encoded. On the receiver side, by exploring the temporal constraint provided by STBC, an equivalent channel model similar to the one in standard vertical Bell laboratories layered space-time (V- BLAST) systems is generated. Then OMMSE SIC algorithm is performed to detect all the transmitted information. Simulation compares the proposed scheme with non-ordering MMSE SIC scheme and the corresponding equal data rate scheme in V-BLAST systems with the same receive antennas' number. Result shows that the proposed scheme has better performance than non-ordering MMSE SIC scheme and by introducing more transmit antennas and adopting the OMMSE SIC scheme, better performance also can be achieved than corresponding V-BLAST systems.
Constrained by complex imaging mechanism and extraordinary visual appearance, change detection with synthetic aperture radar (SAR) images has been a difficult research topic, especially in urban areas. Although existing studies have extended from bi-temporal data pair to multi-temporal datasets to derive more plentiful information, there are still two problems to be solved in practical applications. First, change indicators constructed from incoherent feature only cannot characterize the change objects accurately. Second, the results of pixel-level methods are usually presented in the form of the noisy binary map, making the spatial change not intuitive and the temporal change of a single pixel meaningless. In this study, we propose an unsupervised man-made objects change detection framework using both coherent and incoherent features derived from multi-temporal SAR images. The coefficients of variation in time-series incoherent features and the man-made object index (MOI) defined with coherent features are first combined to identify the initial change pixels. Afterwards, an improved spatiotemporal clustering algorithm is developed based on density-based spatial clustering of applications with noise (DBSCAN) and dynamic time warping (DTW), which can transform the initial results into noiseless object-level patches, and take the cluster center as a representative of the man-made object to determine the change pattern of each patch. An experiment with a stack of 10 TerraSAR-X images in Stripmap mode demonstrated that this method is effective in urban scenes and has the potential applicability to wide area change detection.
A construction method based on the p-plane to design high-girth quasi-cyclic low-density parity-check (QC-LDPC) codes is proposed. Firstly the good points in every line of the p-plane can be ascertained through filtering the bad points, because the designed parity-check matrixes using these points have the short cycles in Tanner graph of codes. Then one of the best points from the residual good points of every line in the p-plane will be found, respectively. The optimal point is also singled out according to the bit error rate (BER) performance of the QC-LDPC codes at last. Explicit necessary and sufficient conditions for the QC-LDPC codes to have no short cycles are presented which are in favor of removing the bad points in the p-plane. Since preventing the short cycles also prevents the small stopping sets, the proposed construction method also leads to QC-LDPC codes with a higher stopping distance.
A novel modified optimization technique known as the multi-objective micro particle swarm optimization (MO-MicPSO) is proposed for polyphase coded signal design. The proposed MO-MicPSO requires only a small population size compared with the standard particle swarm optimization that uses a larger population size. This new method is guided by an elite archive to finish the multi-objective optimization. The orthogonal polyphase coded signal (OPCS) can fundamentally improve the multiple input multiple output (MIMO) radar system performance, with which the radar system has high resolution and abundant signal channels. Simulation results on the polyphase coded signal design show that the MO-MicPSO can perform quite well for this high-dimensional multi-objective optimized problem. Compared with particle swarm optimization or genetic algorithm, the proposed MO-MicPSO has a better optimized efficiency and less time consumption.
In this paper, a static weapon target assignment (WTA) problem is studied. As a critical problem in cooperative air combat, outcome of WTA directly influences the battle. Along with the cost of weapons rising rapidly, it is indispensable to design a target assignment model that can ensure minimizing targets survivability and weapons consumption simultaneously. Afterwards an algorithm named as improved artificial fish swarm algorithm-improved harmony search algorithm (IAFSA-IHS) is proposed to solve the problem. The effect of the proposed algorithm is demonstrated in numerical simulations, and results show that it performs positively in searching the optimal solution and solving the WTA problem.
