Particle Filter Technology Research Articles

Improved Particle Filter Algorithm for Multi-Target Detection and Tracking.

In modern radar detection systems, the particle filter technique has become one of the core algorithms for real-time target detection and tracking due to its good nonlinear and non-Gaussian system state estimation capability. However, when dealing with complex dynamic scenes, the traditional particle filter algorithm exposes obvious deficiencies. The main expression is that the sample degradation is serious, which leads to a decrease in estimation accuracy. In multi-target states, the algorithm is difficult to effectively distinguish and stably track each target, which increases the difficulty of state estimation. These problems limit the application potential of particle filter technology in multi-target complex environments, and there is an urgent need to develop a more advanced algorithmic framework to enhance its robustness and accuracy in complex scenes. Therefore, this paper proposes an improved particle filter algorithm for multi-target detection and tracking. Firstly, the particles are divided into tracking particles and searching particles. The tracking particles are used to maintain and update the trajectory information of the target, and the searching particles are used to identify and screen out multiple potential targets in the environment, to sufficiently improve the diversity of the particles. Secondly, the density-based spatial clustering of applications with noise is integrated into the resampling phase to improve the efficiency and accuracy of particle replication, so that the algorithm can effectively track multiple targets. Experimental result shows that the proposed algorithm can effectively improve the detection probability, and it has a lower root mean square error (RMSE) and a stronger adaptability to multi-target situation.

Underwater Geomagnetic Localization Based on Adaptive Fission Particle-Matching Technology

The geomagnetic field constitutes a massive fingerprint database, and its unique structure provides potential position correction information. In recent years, particle filter technology has received more attention in the context of robot navigation. However, particle degradation and impoverishment have constrained navigation systems’ performance. This paper transforms particle filtering into a particle-matching positioning problem and proposes a geomagnetic localization method based on an adaptive fission particle filter. This method employs particle-filtering technology to construct a geomagnetic matching positioning model. Through adaptive particle fission and sampling, the problem of particle degradation and impoverishment in traditional particle filtering is solved, resulting in improved geomagnetic matching positioning accuracy. Finally, the proposed method was tested in a sea trial, and the results show that the proposed method has a lower positioning error than traditional particle-filtering and intelligent particle-filtering algorithms. Under geomagnetic map conditions, an average positioning accuracy of about 546.44 m is achieved.

Retracted: Industrial Internet of Things Model Driven by Particle Filter and Network Communication Technology.

[This retracts the article DOI: 10.1155/2022/9026017.].

Industrial Internet of Things Model Driven by Particle Filter and Network Communication Technology.

In this paper, a better particle filter algorithm is put forth to address the issues of particle filter sample exhaustion and weight degradation. The algorithm frames the received signal and separates the signals in two steps based on the slow-varying properties of system parameters in practical applications, such as phase shift and transmission delay. In addition, the network model and energy consumption model are built while the sensor data is being collected and processed using the industrial IoT's communication mechanism and algorithm. The repeater is chosen as the node with the lowest transmission energy consumption, and the industrial field's sensor data is gathered via the fog server node. The simulation results demonstrate that the proposed algorithm's accuracy rate is 95.54 percent, higher than that of the comparison algorithm. The enhanced algorithm suggested in this paper can simultaneously achieve improved parameter estimation performance and achieve signal separation with low bit error rates. Additionally, the communication system and algorithm can efficiently gather the sensing information from the industrial field, and the indicators like energy consumption and the first dead node are better than other algorithms. It offers an innovative method for enhancing industrial field application.

An enhanced particle filter technology for battery system state estimation and RUL prediction

The particle filter (PF) technique can model nonlinear degradation features of battery’s system, and conduct battery state estimation based on noisy measurements. However, PF has some limitations in system state estimation related to sample degeneracy and impoverishment. In addition, its posterior probability density function cannot be updated during the prognostic period due to the absence of new battery measurements. In this work, an enhanced PF technology is proposed to deal with these problems so as to improve PF modeling accuracy for battery state-of-health monitoring and remaining useful life (RUL) prediction. Specifically, an enhanced particles method is proposed to reduce the impact of sample degeneracy and impoverishment in state estimation. An evolving fuzzy predictor is adopted and fused into the enhanced PF structure to deal with the lack of new battery measurements during the prognostic period. The effectiveness of the proposed enhanced PF technology is validated through simulation tests.

Smart City Moving Target Tracking Algorithm Based on Quantum Genetic and Particle Filter

In the application of moving target tracking in smart city, particle filter technology has the advantages of dealing with nonlinear and non-Gaussian problems, but when the standard particle filter uses resampling method to solve the degradation phenomenon, simply copying the particles will cause local optimization difficulties, resulting in unstable filtering accuracy. In this paper, a particle filter algorithm combined with quantum genetic algorithm (QGA) is proposed to solve the above problems. Aiming at the problem of particle exhaustion in particle filter, the algorithm adopts the method of combining evolutionary algorithm. Each particle in particle filter is regarded as a chromosome in genetic algorithm, and the fitness of each chromosome corresponds to the weight of particle. For each particle state with weight, the particle is first binary coded with qubit and quantum superposition state, and then quantum rotation gate is used for selection, crossing, mutation, and other operations, after a set number of iterations, the final particle set with accuracy and better diversity. In this paper, the filter state estimation and RMSF of N=50 and N=100 for nonlinear target tracking and the comparison of real state and state estimation trajectory in time-constant model under nonlinear target tracking are given. It can be seen that in nonlinear state, the quantum genetic and particle filter (QGPF) algorithm can achieve a higher accuracy of state estimation, and the filtering error of QGPF algorithm at each time is relatively uniform, which shows that the algorithm in this paper has better algorithm stability. Under the time-constant model, the algorithm fits the real state and realizes stable and accurate tracking.

Video Analysis Technology and Its Application in Badminton Sports Training

Based on the continuous development of computer vision and graphics technology, pattern recognition and image processing technology, intelligent video analysis technology combines these related technologies to establish a mapping relationship between image description and surveillance image. Intelligent video analysis technology has a wide range of applications, such as in military and economic fields. In the process of badminton training, when the trainer explains the action essentials for the athletes, the video replay and analysis management methods are less difficult, lack of science and intuition and it is difficult to meet the trainer’s demand for sports performance evaluation. The analysis and recognition process of badminton video are studied. Based on particle filter prediction, adaptive threshold algorithm is used to achieve adaptive separation of moving targets. The particle filter technology is used to track the trajectory of the athletes and the motion model is constructed. Based on this, the position of the important joint points in the subsequent motion video frames is predicted, so as to track the subsequent motion video frames. When the motion recognition in the badminton training video is used, the conditional random field method is mainly used, which makes the motion recognition rate higher and the false separation rate lower.

On LQG control design for network systems with/without acknowledgments using a particle filtering technology

This paper focuses on the behavior of a linear quadratic Gaussian (LQG) controller for discrete-time systems utilizing industrial communication protocols. A new design of LQG problem is investigated for constrained networked control systems by using a new class of a quadratic cost function with a communication cost. This paper is considered two industrial communication protocols namely: the transportation control protocol (TCP), in which the sender should receive an acknowledgment regarding the delivery of the sending packets and the user datagram protocol (UDP) where the acknowledgment signal is absence. In the latter one, the driver (actuation) does not afford acknowledgment to inform the controller/estimator regarding the reception of the action packets. In such setting, the UDP-like protocol can react nonlinearly, where the absence of the acknowledgment creates a nonlinear optimization control law. In this sense, a LQG with standard Kalman filtering is failed because the separation principle can not reached. Under this setting, a suboptimal methodology can be successfully implemented to compensate the absence of communication and acknowledgment packets. The prescribed performance of networked system is sustained in the presence of communication constraints. The probabilities of the data signal dropouts are expressed in terms of random Bernoulli processes. Then, simulation results are provided to demonstrate the validity of the proposed schemes and to compare them with others from literature.

Prediction Prognosis for State of Charge, State of Health, and Remaining Useful Life for a Lfp Battery Management System (BMS)

Discovering the precise state of charge (SOC), state of health (SOH), and remaining useful life (RUL) while monitoring depth of discharge (DOD) for Lithium ion (Li-ion) batteries can be computational enormous. Electrical engineers using equivalent circuit models (ECM) or enhanced equivalent circuit models (EECM) have achieved an acceptable level of accuracy modelling the current, voltage, and temperature behaviors to get SOC, SOH, RUL, and DOD. ECM/EECM’s are computationally fast but do not fully account for the electrochemical material that makes up the battery in their mathematical equations or isothermal characteristics that continuously change during charging and discharging or at varying C-rates while cycling. Unfortunately, the number of factors and variables needed to compute the system of differential algebraic equations for the nonlinear physical-based models to achieve this level of precise data is computationally too expensive for real-time systems. In today’s environment, the use of filters has provided a bridge between empirical and physical based models. This paper examines an ECM and a porous electrode pseudo two-dimensional (P2D) model using particle filter (PF) technology for a series, parallel, and series/parallel combination battery switching microcontroller based battery management system (BMS). Introduction Reconfigurable battery packs have the advantage of altering the pack topology to adapt to changes in load requirements. The series/parallel battery configuration allows a low cost solution for networks under constraint to meet the energy and power demand placed on an electrical system without adding additional batteries and incurring additional cost. Part of the system is dependent on the fast charging parallel converter which allows the network to balance each cell and accurately estimate the battery SOC. Kim, Qiao, and Qu[1] series cell array negated parallel configurations in their series cell arrays. Physical based models have the advantage of performing comprehensive analysis on the effects of both the solid phase and the liquid phase. Modelling, porous electrode theory coupled with transport phenomena and electrochemical reactions represented by coupled nonlinear partial differential equations (PDE) in one or two dimensions gives physical based models an advantage over equivalent circuit model (ECM) based models. Santhanagopalan[2] and Rahimian[3] have used the single-particle model (SPM) implementing Kalman filtering methods to estimate SOC of Li-Ion cells. An area of concern is the computational complexity of the physical based models. Experimental Method State of Charge (SOC) The SOC, a measure of remaining capacity in the battery, helps to ensure during charging battery cells are not over/under charged and during discharge it is a quick gauge to show how much capacity is remaining capable of supporting the load. Eq-1 where, Q is the capacity. In battery cells, capacity can be represented as a voltage at specific time. Fig-1 State of Health (SOH) The SOH, an indication of where the battery is at in its life cycle, is used to measure capacitance of the used battery relative to the capacitance of a new battery. Using capacity fade analysis to demonstrate the loss of capacity during the life of the cell indicates if the battery is being maximized. This allowed us to monitor the remaining capacitance available in the battery cell at a given time. Eq-2 where, c is the capacity, α is the deterioration rate multiplier. α varies depending on power density, energy density, and temperature components. Fig-2 Remaining Useful Life (RUL) The RUL, an approximation of the cyclability of a battery pack, enables an estimation of the number of duration individual battery cells within the series/parallel battery scheme are useable, thus approximating the life cycle of the entire BMS. Eq-3 where, β is the cycle deterioration factor. Fig-3 References Kim, T., et al. “Series-Connected Self-Reconfigurable Multicell Battery.” 26th AAPEC&E, Mar. 2011, IEEE. pp. 1382-1387.Santhanagopalan, S., et al. "Review of Models for Predicting the Cycling Performance of Lithium Ion Batteries." JPS 156.2 (2006): 620-628.Rahimian, S.K., et al. “State of Charge and Loss of Active Material Estimation of a Lithium Ion Cell under Low Earth Orbit Condition Using Kalman Filtering Approaches.” JES (2014): A860-A872. Figure 1

Autonomous positioning control of manipulator and fast surface fitting based on particle filter and point cloud library technology

The real-time calculations of the positioning error, error correction, and state analysis have always been a difficult challenge in the process of autonomous positioning. In order to solve this problem, a simple depth imaging equipment (Kinect) is used, and a particle filter based on three-frame subtraction to capture the end-effector’s motion is proposed in this article. Further, a back-propagation neural network is adopted to recognize targets. The point cloud library technology is used to collect the space coordinates of the end-effector and target. Finally, a three-dimensional mesh simplification algorithm based on the density analysis and average distance between points is proposed to carry out data compression. Accordingly, the target point cloud is fitted quickly. The experiments conducted in the article demonstrate that the proposed algorithm can detect and track the end-effector in real time. The recognition rate of 99% is achieved for a cylindrical object. The geometric center of all particles is regarded as the end-effector’s center. Furthermore, the gradual convergence of the end-effector center to the target centroid shows that the autonomous positioning is successful. Compared to traditional algorithms, both moving the end-effector and a stationary object can be extracted from image frames using a thesis. The thesis presents a simple and convenient positioning method, which adjusts the motion of the manipulator according to the error between the end-effector’s center and target centroid. The computational complexity is reduced and the camera calibration is eliminated.

Using Filtering Techniques on a Physical Based Battery Models to Estimate State of Charge, State of Health, and Remaining Useful Life in a LiFePO4 Battery Management System (BMS)

Discovering the precise state of charge (SOC), state of health (SOH), and remaining useful life (RUL) while monitoring depth of discharge (DOD) for Lithium ion (Li-ion) batteries can be computational enormous. Electrical engineers using equivalent circuit models (ECM) or enhanced equivalent circuit models (EECM) have achieved an acceptable level of accuracy modelling the current, voltage, and temperature behaviors to get SOC, SOH, RUL, and DOD. ECM/EECM’s are computationally fast but do not fully account for the electrochemical material that makes up the battery in their mathematical equations or isothermal characteristics that continuously change during charging and discharging or at varying C-rates while cycling. Unfortunately, the number of factors and variables needed to compute the system of differential algebraic equations for the nonlinear physical-based models to achieve this level of precise data is computationally too expensive for real-time systems. In today’s environment, the use of filters has provided a bridge between empirical and physical based models. This paper examines an ECM and a porous electrode pseudo two-dimensional (P2D) model using particle filter (PF) technology for a series, parallel, and series/parallel combination battery switching microcontroller based battery management system (BMS). Introduction Reconfigurable battery packs have the advantage of altering the pack topology to adapt to changes in load requirements. The series/parallel battery configuration allows a low cost solution for networks under constraint to meet the energy and power demand placed on an electrical system without adding additional batteries and incurring additional cost. Part of the system is dependent on the fast charging parallel converter which allows the network to balance each cell and accurately estimate the battery SOC. Kim, Qiao, and Qu[1] series cell array negated parallel configurations in their series cell arrays. Physical based models have the advantage of performing comprehensive analysis on the effects of both the solid phase and the liquid phase. Modelling, porous electrode theory coupled with transport phenomena and electrochemical reactions represented by coupled nonlinear partial differential equations (PDE) in one or two dimensions gives physical based models an advantage over equivalent circuit model (ECM) based models. Santhanagopalan[2] and Rahimian[3] have used the single-particle model (SPM) implementing Kalman filtering methods to estimate SOC of Li-Ion cells. An area of concern is the computational complexity of the physical based models. Fig-1 Experimental Method State of Charge (SOC) The SOC, a measure of remaining capacity in the battery, helps to ensure during charging battery cells are not over/under charged and during discharge it is a quick gauge to show how much capacity is remaining capable of supporting the load. Eq-1 where, Q is the capacity. In battery cells, capacity can be represented as a voltage at specific time. Fig-2 State of Health (SOH) The SOH, an indication of where the battery is at in its life cycle, is used to measure capacitance of the used battery relative to the capacitance of a new battery. Using capacity fade analysis to demonstrate the loss of capacity during the life of the cell indicates if the battery is being maximized. This allowed us to monitor the remaining capacitance available in the battery cell at a given time. Eq-2 where, c is the capacity, α is the deterioration rate multiplier. α varies depending on power density, energy density, and temperature components. Fig-3 Fig 3a shows that as the discharge rate increases the capacitance decreases. Fig 3b indicates that colder temperatures have an adverse effect on the current and at 0o F some capacitance is lost during the electrochemical process. Fig 3b shows that there are little losses from 60o – 100o F. Remaining Useful Life (RUL) The RUL, an approximation of the cyclability of a battery pack, enables an estimation of the number of duration individual battery cells within the series/parallel battery scheme are useable, thus approximating the life cycle of the entire BMS. Eq-3 where, β is the cycle deterioration factor. Fig-4 Fig 4 illustrates that singular variables introduced into the process could have minor effects on the overall battery cell if monitored by the BMS. The introductions of multiple factors at the same time could have extreme effects on the BMS system. Ref Kim, T., et al. “Series-Connected Self-Reconfigurable Multicell Battery.” 26th AAPEC&E, Mar. 2011, IEEE. pp. 1382-1387.Santhanagopalan, S., et al. "Review of Models for Predicting the Cycling Performance of Lithium Ion Batteries." JPS 156.2 (2006): 620-628. Rahimian, S.K., et al. “State of Charge and Loss of Active Material Estimation of a Lithium Ion Cell under Low Earth Orbit Condition Using Kalman Filtering Approaches.” JES (2014): A860-A872. Figure 1

Target Detection Method Before Tracking based on Particle Filter

infrared dim target detection and tracking technology is one of the core technologies in infrared imaging system, and also a hot research topic. When the SNR is low, the tracking technique has a unique advantage. This paper studies the algorithm of track before detect based on particle filter, gives the basic theory of particle filter and its improved algorithm, and through the simulation of the particle filter technology examples, results show the superior performance in nonlinear and non particle filter under the background of Gauss.

A Novel Spectrum Sensing for Cognitive Radio Networks with Noise Uncertainty

This correspondence investigates a joint spectrum sensing scheme in cognitive radio (CR) networks with unknown and dynamic noise variance. A novel Bayesian solution is proposed to recover the dynamic noise variance and detect the occupancy of primary frequency band simultaneously. The states of primary users are detected based on particle filtering technology, and then the noise parameters are tracked by using finite-dimensional statistics for each particle based on marginalized adaptive particle filtering. Simulation results are provided to validate that the proposed method can improve the sensing performance significantly and target the dynamic noise variance accurately.

Spectrum Sensing for Self-Organizing Network in the Presence of Time-Variant Multipath Flat Fading Channels and Unknown Noise Variance

Cognitive radio, as an important means of implementing spectrum-awareness and dynamic sharing, is of great significance to the future deployment of self-organizing networks. Given its practical application, cognitive radio technology may operate in various adverse self-organizing networks environments. For example, in wireless mobile communication, the multipath propagation with time-varying fading coefficients and unknown noise variance becomes inevitable. Unfortunately, most existing spectrum sensing methods could hardly acquire good performance in this adverse situation. To overcome this difficulty, in this paper we present a novel spectrum sensing algorithm in realistic cognitive radio applications. Firstly, we establish a dynamic state-space model which gives full consideration to the evolution characteristics of primary user's state and time-variant multipath flat fading channel, while the received signal processed by matched filtering is viewed as the observation. On this basis, spectrum sensing is realized by estimating the primary user's state, multipath channel impulse response and noise variance jointly and iteratively. This formulated problem is solved based on maximum a posteriori probability criterion and marginal particle filtering technology. Simulations demonstrate that the sensing performance achieved by proposed algorithm is satisfactory and at the same time, the channel response and noise variance are estimated accurately.

Using Pseudo-Two-Dimensional (P2D) Reformulation Model with a Particle Filter to Estimate State of Charge, State of Health, and Remaining Useful Life in Li-Ion Battery Management System (BMS)

Discovering the precise state of charge (SOC), state of health (SOH), and remaining useful life (RUL) while monitoring depth of discharge (DOD) for Lithium ion (Li-ion) batteries can be computational enormous. Electrical engineers using equivalent circuit models (ECM) or enhanced equivalent circuit models (EECM) have achieved an acceptable level of accuracy modelling the current, voltage, and temperature behaviors to get SOC, SOH, RUL, and DOD. ECMs and EECMs are computationally fast but do not fully account for the electrochemical material that makes up the battery in their mathematical equations or isothermal characteristics that continuously change during charging and discharging. Chemical engineers have achieved greater levels of accuracy using physical-based models over ECMs and EECMs. Unfortunately, the number of factors and variables needed to compute the system of differential algebraic equations for the nonlinear physical-based models to achieve this level of precise data is computationally too expensive for real-time systems. In today’s environment, the use of filters has provided a bridge between empirical and physical based models. This paper examines an ECM and a porous electrode pseudo two-dimensional (P2D) model using particle filter (PF) technology for a series, parallel, and series/parallel combination battery switching microcontroller based battery management system (BMS). Mathematical and Physical Models BMSs are developed to manage the charging and discharging profile, and basic knowledge of all the states that the battery topology goes through is paramount in order to design a safe and more sustainable battery system. ECMs are used in BMSs because they can model battery characteristics for a desired state using resistors and capacitors to emulate the internal battery cell resistance and capacitance behaviors. Because ECMs are linearly based mathematical equations, simulation of these models can be performed quickly. Additionally, Kalman filtering techniques (KFT) have been used extensively with ECMs to assist in the estimation of the SOC and SOH for BMSs. However, the effectiveness of ECMs and KFTs are not optimal for the non-linear behavior of batteries. Physical based models have the advantage of performing comprehensive analysis on the effects of both the solid phase and the liquid phase, but they can be computationally complex. Modelling, porous electrode theory coupled with transport phenomena, and electrochemical reactions represented by coupled nonlinear partial differential equations in one or two dimensions give physical based models an advantage over ECMs. Santhanagopalan [1] and Rahimian [2] have used the single particle model (SPM) implementing Kalman filtering methods to estimate SOC of Li-ion cells. Efforts by Subramanian et al. [3] focused on a physical based P2D reformulated model characterized by solid-electrolyte interface layer growth, constant current–constant voltage, and capacity fade as it related to battery degradation to determine the manner in which extreme temperature and high charging rates affect the battery “calendar life.” This paper will simulate a reformulated P2D SOC and SOH convergence based model using the governing equations of a SPM with lithium active particles as in Northrop [4]: Eq (1), Eq (2), Eq (3) Particle Filter Methodology The PF is an algorithm based on a recursive Monte Carlo technique of estimating the posterior density of the state variable given a set of observation variables. The sequential importance sampling (SIS) algorithm implements the recursive Bayesian filter by associating weights to random samples representing the required posterior density. The sequential importance resampling (SIR) filter adapts the SIS features and adds a resampling phase. The posterior PDF approximation can be defined as in [5]: Eq (4), Eq (5) Results Figure 1(a) shows the simulated discharge (at 1C rate) of a 1.2 Volt Li-ion battery at 100% SOC using an empirical ECM in MatLab Simulink. Figure 1(b) includes simulated measured (noisy), SIS, and SIR filtered battery discharge data. Figure 1 Battery Voltage Profile Future work will include a reformulated physical based P2D PF battery model to simulate SOC, SOH, and RUL of a BMS. References Santhanagopalan, Shriram, et al. "Review of Models for Predicting the Cycling Performance of Lithium Ion Batteries." JPS 156.2 (2006): 620-8. Print. Rahimian, S.K., S. Rayman, and R.E. White. “State of Charge and Loss of Active Material Estimation of a Lithium Ion Cell under Low Earth Orbit Condition Using Kalman Filtering Approaches.” ECS (2014): A860-A872. Subramanian, Venkat R., et al. "Mathematical Model Reformulation for Lithium-Ion Battery Simulations: Galvanostatic Boundary Conditions." ECS 156.4 (2009): A260-71. Print.Northrop, Paul W. C., et al. "Efficient Simulation and Reformulation of Lithium-Ion Battery Models for Enabling Electric Transportation." ECS 161.8 (2014): E3149-57. Print.Arulampalam, M. S., et al. "A Tutorial on Particle Filters for Online nonlinear/non-Gaussian Bayesian Tracking." Signal Processing, IEEE Transactions on 50.2 (2002): 174-88. Print. Figure 1

Mechanical Seal End Faces Friction Condition Monitoring Based on Particle Filter

The non-contact mechanical seal end faces opening friction condition detection and the measurement of film thickness when the end faces is just-lift-off, which are always key problems for scholars engaged in sealing industry for many years, but there is no effective solution. Acoustic emission (AE) signal generated in the running process of mechanical seal end faces has a plenty of information about the faces contact state. According to this, the thickness measurement of mechanical seal and opening condition monitoring technology by using particle filter are put forward based on the acoustic emission signal. Acoustic emission sensor is installed in the stationary ring seat, used for the indirect outer detection of the dynamic and stationary rings opening condition. The acoustic emission signals are processed by particle filter technology, and then the signal features are extracted in time domain, frequency domain and wavelet packet energy. A BP neural network model is established, the features of signal characteristic used as input of the model. It is finally realized that the mechanical seal end faces opening condition was recognized and the film thickness was measured. Eddy current sensor is installed inside the stationary ring and used for direct measurement of film thickness and verify the results got from the BP neural network. Through the experiment, this method is practical and effective, and which can be used in the monitoring of mechanical seal end faces working condition in industrial field.

Blind spectrum sensing for cognitive radio over time-variant multipath flat-fading channels

Abstract Cognitive radio has more extensive application in recent years, and it may operate in complex wireless environmental condition such as communication systems with time-variant multipath flat-fading channel. As an essential technology for cognitive radio, most existing spectrum sensing methods are designed for time-invariant propagation channel; thus, it could be extremely difficult to achieve acceptable sensing performance when we apply them to deal with time-variant multipath fading channel. In order to overcome this obstacle, we design a novel spectrum sensing method in this investigation. Firstly, a dynamic state-space model is proposed in which two different hidden Markov models are employed to abstract the evolution of primary user state and time-variant multipath flat-fading channel gain. Based on the dynamic state-space model, the spectrum sensing problem is formulated as blind estimation problem. Relying on maximum a posteriori probability criterion and particle filtering technology, a joint estimation algorithm of the time-variant channel gain and primary user state is presented. Experimental simulations demonstrate the superior performance of our presented sensing scheme, which could be used potentially in realistic cognitive radio systems.

Manufacturing and wetting low-cost microfluidic cell separation devices

Deterministic lateral displacement (DLD) is a microfluidic size-based particle separation or filter technology with applications in cell separation and enrichment. Currently, there are no cost-effective manufacturing methods for this promising microfluidic technology. In this fabrication paper, however, we develop a simple, yet robust protocol for thermoplastic DLD devices using regulatory-approved materials and biocompatible methods. The final standalone device allowed for volumetric flow rates of 660 μl min(-1) while reducing the manufacturing time to <1 h. Optical profilometry and image analysis were employed to assess manufacturing accuracy and precision; the average replicated post height was 0.48% less than the average post height on the master mold and the average replicated array pitch was 1.1% less than the original design with replicated posts heights of 62.1 ± 5.1 μm (mean ± 6 standard deviations) and replicated array pitches of 35.6 ± 0.31 μm.

Visual Detection Technology of the Driver's State of Fatigue Based on Multi-Information Fusion

This paper attempts to combine the image features of shape, movement and color, taking the oval as the shape model of the target to immediately catch the edge of the target, using the feature light stream technology to obtain the movement information of the target. The color information can be obtained using the color histogram features of the target and mean shift algorithm. In the tracking of the color, SDA color spatial selection algorithm is introduced to improve the target and background differentiation. Then, the above information would be integrated through particle filtering technology. The self-adapting integration technology is used, and each image features weight would be decided according to its validity, thus, a quick and robust facial tracking algorithm is gained.

Federated Particle Filter Technology Based on JIDS/SINS/GPS Integrated Navigation System

In multi-sensor integrated navigation systems, when sub-systems are non-linear and with Gaussian noise, the federated Kalman filter commonly used generates large error or even failure when estimating the global fusion state. This paper, taking JIDS/SINS/GPS integrated navigation system as example, proposes a federated particle filter technology to solve problems above. This technology, combining the particle filter with the federated Kalman filter, can be applied to non-linear non-Gaussian integrated system. It is proved effective in information fusion algorithm by simulated application, where the navigation information gets well fused.