Multi-sensor Target Research Articles

Multi-sensor target tracking algorithm combining node energy planning and distributed collaboration

Multi-sensor target tracking algorithm combining node energy planning and distributed collaboration

Distributed Adaptive Bernoulli Filtering for Multi-Sensor Target Tracking Under Uncertainty

Distributed Adaptive Bernoulli Filtering for Multi-Sensor Target Tracking Under Uncertainty

Magnetic-encoded fluorescent nanospheres-based competitive immunoassay for near-simultaneous detection of four mycotoxins in wheat

Simultaneous detection of mycotoxins is important for food safety. In this study, a magnetic-encoded fluorescent nanosphere-based competitive immunosensor (cFMEIS) with 2×2 array was first developed for simultaneous detection of aflatoxin B1 (AFB1), ochratoxin (OTA), deoxynivalenol (DON), and zearalenone (ZEN) in wheat. Specifically, magnetic nanoparticles with strong and weak responses were conjugated with mycotoxin antigens as capture probes. Fluorescent nanoparticles doped with europium ion (Eu3+) and terbium ion (Tb3+) with red and green emission were coupled with mycotoxin antibodies as signal probes. Using a magnetic field, immune complexes were sequentially separated in a complex system and fluorescently detected. The detection limits of AFB1, DON, OTA, and ZEN were 0.032, 0.141, 0.097, and 0.376 μg/kg, respectively. The recoveries in the certified reference material of wheat flour ranged from 81.6 to 120.0 %. Owing to its high accuracy, selectivity, and sensitivity, the cFMEIS shows great promise as an efficient and sensitive multitarget sensor for mycotoxins.

Optimal Geometry and Motion Coordination for Multisensor Target Tracking with Bearings-Only Measurements.

This paper focuses on the optimal geometry and motion coordination problem of mobile bearings-only sensors for improving target tracking performance. A general optimal sensor-target geometry is derived with uniform sensor-target distance using D-optimality for arbitrary n (n≥2) bearings-only sensors. The optimal geometry is characterized by the partition cases dividing n into the sum of integers no less than two. Then, a motion coordination method is developed to steer the sensors to reach the circular radius orbit (CRO) around the target with a minimum sensor-target distance and move with a circular formation. The sensors are first driven to approach the target directly when outside the CRO. When the sensor reaches the CRO, they are then allocated to different subsets according to the partition cases through matching the optimal geometry. The sensor motion is optimized under constraints to achieve the matched optimal geometry by minimizing the sum of the distance traveled by the sensors. Finally, two illustrative examples are used to demonstrate the effectiveness of the proposed approach.

Hölder Divergence-Based Reward Function for Poisson RFSs and Application to Multitarget Sensor Management

In this study, we propose a novel information theoretic reward function based on the statistical Hölder divergence. The Hölder divergence is the generalization of Cauchy-Schwarz divergence. We extend the Hölder divergence to the FISST densities, thus making it possible to use in the multi-object applications based on the RFS theory. We derive the analytic expressions for the extended Hölder divergence (EHD) for the case when the multi-target densities have the form of Poisson RFSs and apply it to the PHD filter in a sequential Monte Carlo implementation. We evaluated the performance of the proposed reward function in a multi-target sensor management problem where the next position of a moving observer is decided according to the value of the EHD-based reward function. The performance of the algorithm is compared against, in terms of OSPA metric, and it is shown that the proposed reward function is superior to other similar reward functions in multi-target sensor management literature. We also show that it is possible to adapt the management algorithm to different situations, such as static and dynamic environments.

Multi-sensor target data detection method based on improved deep learning

Aiming at the problems of low detection accuracy and long detection time in the traditional multi-sensor target data detection method, a multi-sensor target data detection method based on improved deep learning is proposed. A regional nomination network model is built to locate all rectangular areas where multi-sensor targets may exist. A sample acquisition model is established by fusing multi-sensor target data in this area in combination with the storage structure of multi-sensor target data to collect multi-sensor target data and filter the collected target data. The histogram feature method is used to extract the processed multi-sensor target data features and the extracted multi-sensor target data features are input into the neural network training samples. The square error function is used to calculate the sample deviation and the deviation is corrected by the deviation gradient sinking method. Finally, the multi-sensor target data detection results are output. Simulation results show that the proposed method has high accuracy, low false detection rate and short detection time along with applicability.

Research on the Development Model of Rural Tourism Based on Multiobjective Planning and Intelligent Optimization Algorithm

In order to improve the effect of rural tourism development, this study combines multiobjective planning and intelligent optimization algorithms to analyze the development mode of rural tourism and further deals with the problem of multisensor target tracking under unknown input interference conditions by designing a tourism network consensus algorithm. The algorithm adopts a distributed multisensor fusion structure combined with consensus estimation, and each sensor first performs two-level information filtering estimation and unknown input parameter estimation locally. The experimental research results show that the rural tourism development model based on multiobjective planning and intelligent optimization algorithm proposed in this study can play an important role in the development of rural tourism.

Multi-sensor target allocation model based on NC-PSO

Traditional multi-sensor target allocation often has problems such as slow algorithm iteration and low solution quality. In response to this problem, this paper proposes the NC-PSO algorithm, so that the particles can jump out of the local optimum and increase the convergence speed. At the same time, on the basis of the improved algorithm, the constraint of the target threat degree is added, the interval analytic hierarchy process is used to estimate the target threat, and the allocation principle is optimized, so that the use of sensor resources is better and the allocation is more reasonable. The simulation results prove that NC-PSO can allocate targets to multiple sensors faster and better.

Multi-Sensor Target Tracking Using Ensemble Learning

Multi-Sensor Target Tracking Using Ensemble Learning

Exponential Mixture Density Based Approximation to Posterior Cramér-Rao Lower Bound for Distributed Target Tracking

The posterior Cramr-Rao lower bound (PCRLB) and a number of its extensions including the unconditional PCRLB (U-PCRLB) and the conditional PCRLB (C-PCRLB) have been widely studied in multi-sensor target tracking (MSTT). Previous MSTT works assume that the measurements are conditionally independent, which is often not the case in practice. When the correlations of the measurements are unknown, the standard Bayes update and PCRLB computations cannot be performed. Inspired by geometric average (GA) fusion that implements data fusion with unknown data correlations by employing exponential mixture density (EMD) to compute the global posterior, EMD-based approximations to U-PCRLB and C-PCRLB are derived in this paper for two classic distributed fusion architectures, namely the hierarchical and consensus architectures. The PCRLB can be decomposed into two parts, one coming from the prior information and the other from the information contained in the data. The data information part of PCRLB is approximated via EMD-based posterior, leading to the approximation of the PCRLB. We present a sequential Monte Carlo solution to recursively compute the proposed approximation to the PCRLB for nonlinear non-Gaussian estimation problems. Numerical simulations are provided to show that the proposed approximation to the bound on the estimation mean square error (MSE) is tighter compared to the existing bounds based on likelihood fusion obtained under the measurement independence assumption.

Motion Modeling and State Estimation in Range-Squared Coordinate

In some target tracking applications, angle measurements are absent due to sensor characteristics, which makes Cartesian states unobservable for a single stationary sensor. When multi-sensor target tracking techniques with only range or Doppler measurements are applied to produce Cartesian state estimates, their performance and efficiency can be benefited from accurate range and Doppler estimates obtained from single sensors. This paper investigates the range and Doppler estimation problem based on motion modeling in range-squared coordinate considering range-squared as a generic coordinate. A pseudostate vector consisting of range squared and its derivatives is defined to completely summarize the dynamic of the Cartesian nearly constant velocity (NCV) motion and it is found time evolution of the pseudostate is linear based on the methodology of state space representation. To estimate the pseudostate from range-Doppler or range-only measurements, a recursive filter is developed using the decorrelated unbiased converted measurement (DUCM) method. Finally, the pseudostate estimates in the range-squared coordinate are converted into the range coordinate using the scaled unscented transformation (SUT) method to produce range and Doppler estimates. Although the conversion is nonlinear, it is performed outside the filtering recursions without propagating nonlinearity approximation errors in the filtering recursions. Numerical experiments with various conditions of measurements demonstrate the superior performance benefits from the proposed accurate linear state equation and estimation method.

Message Passing and Hierarchical Models for Simultaneous Tracking and Registration

Sensor registration is an important problem that must be considered when attempting to perform any kind of data fusion in multimodal, multisensor target tracking. In this multiple target tracking (MTT) application, any inaccuracies in the registration can lead to false tracks being created, and tracks of true targets being stopped prematurely. This article introduces a method for simultaneously tracking multiple targets in a surveillance region and estimating appropriate sensor registration parameters so that sensor fusion can be performed accurately. The proposed method is based around particle belief propagation (BP), a recent but highly efficient framework for tracking multiple targets. The proposed method also uses a hierarchical model which allows for multiple processes to be linked and interact with one another. We present a comprehensive set of simulations and results using differing, asynchronous sensor setups, and compare with a random finite set (RFS) approach, namely the sequential Monte Carlo (SMC)-probability hypothesis density (PHD) filter. The results show the proposed method is 17% more accurate than the RFS approach on average.

A hybrid Hausdorff distance track correlation algorithm based on time sliding window

In multi-sensor target tracking, track correlation is the key to the unification of global situation. Hausdorff distance has been applied to power fault elimination, point cloud data, medical measurement, image segmentation, vehicle trajectory recognition and other directions. To solve the problem of track correlation, a hybrid Hausdorff distance track correlation algorithm based on time sliding window is proposed. The hybrid Hausdorff distance based on position, speed and azimuth is defined, and the time sliding window is added on this basis. Simulation results show that the proposed algorithm can maintain a high correct correlation rate under the conditions of target maneuver, time asynchronism, and inconsistent radar sampling frequency, the algorithm is superior to traditional track correlation algorithm.

The KF-SVM-based fusion method for multi sensor uncertain system with correlated noise

For multi-sensor target tracking system, the accurate state estimation is obtained under the condition that the system model is unbiased and the noise disturbance satisfies the characteristics of independent Gaussian white noise. However, in engineering practice, it is almost impossible to get the accurate system model and also the system noise is non-independent Gaussian white noise. So the traditional state estimation methods are not suitable for uncertainty system with non Gaussian white noise. In this paper, the Kalman Filter-Support Vector Machine (KF-SVM) based data fusion algorithm is proposed for system with model uncertainty and correlated noise. Firstly, the state pre-estimates are calculated by the proposed improved Kalman Filter for single sensor system. Then, the state estimation is obtained via proposed KF-SVM data fusion algorithm for multi-sensor system. Finally, compared with the traditional algorithms, the simulation results show that the proposed fusion algorithm based on KF-SVM does not need to calculate the sensor cross-covariance matrix and has better estimation accuracy.

Intelligent Transportation Application and Analysis for Multi-Sensor Information Fusion of Internet of Things

During the driving of the smart car, due to the complexity of the environment, a single sensor or multiple homogeneous sensors cannot fully perceive the traffic environment around the smart car. Therefore, it is necessary to study the information fusion scheme of heterogeneous sensors, use the advantages of heterogeneous sensors to make up for the shortcomings of a single sensor, to realize the function of cooperation and mutual compensation among multiple heterogeneous sensors. Therefore, this paper proposes a data fusion method based on discrete factor multi-sensor target recognition. The output data from multiple sensors acquired over too many periods and multiple regions give the discrete factor of the sensor corresponding to the target characteristic. The current weight of multi-sensor target recognition is given according to the discrete factor, and the relative consistency and the relativeness of multi-sensor target recognition is established. Weighted consistency and other functions; combined with the current weights of multi-sensor target recognition and related consistency functions, a data fusion support calculation model for multi-sensor target recognition is constructed. The test results show that the scheme is more reliable and has a certain anti-interference ability, which can make up for the shortcomings of a single sensor and improve the target recognition rate.

Multisensor distributed out-of-sequence-tracks fusion with track origin uncertainty

The increased trend toward multisensor target tracking system is driving an interest for distributed tracks fusion algorithm. In such a system, local tracks formed by each sensor may arrive in the fusion center with temporal out-of-sequence because of different sensor data processing time and random communication delay, moreover, the priori information on the origins of tracks to be fused is usually unknown due to clutter disturbance and presence of multitargets. This paper considers the problem of fusing out-of-sequence tracks (OOSTs) with track origin uncertainty in a distributed fusion setup, and proposes a novel all neighbor fusion-integrated forward prediction fusion and decorrelation (ANF-IFPFD) method. The proposed ANF-IFPFD enumerates and probabilistically evaluates all feasible track-to-track association events, and fuses the central tracks with extracted information purely contributed by the local OOSTs through an information decorrelation process. Furthermore, it enables to operate the false track discrimination (FTD) in the fusion center by using the fused probability of target existence as a track quality measure. Additionally, two implementation configurations of the proposed ANF-IFPFD method are also designed to provide a trade-off between the tracking performance and the system communication bandwidth consumption. Monte Carlo simulations are carried out to validate the superiority of the proposed ANF-IFPFD over the enhanced state-of-art methods, in terms of both tracking performance and also computation-storage requirement. Compared to the configuration without any information feedback, the partial information feedback configuration of the ANF-IFPFD method is verified to deliver intensive improvements for both local tracking and tracks fusing.

Research on Active Intelligent Perception Technology of Vessel Situation Based on Multisensor Fusion

In this paper, we focus on the safety supervision of inland vessels. This paper especially aims at studying the vessel target detection and dynamic tracking algorithm based on computer vision and the target fusion algorithm based on multisensor. For the vessel video target detection and tracking, this paper analyzes the current widely used methods and theories. Additionally, facing the application scenarios and characteristics of inland vessels, a comprehensive vessel video target detection algorithm is proposed in this paper. It is combined with a three-frame difference method based on Canny edge detection and a background subtraction method based on mixed Gaussian background modeling. Besides, for the multisensor target fusion, the processing method of laser point cloud data and automatic identification system (AIS) data is analyzed in this paper. Based on the idea of fuzzy mathematics, this paper proposes a method for calculating the fuzzy correlation matrix with normal membership function, which realizes the fusion of vessel track features of laser point cloud data and AIS data under dynamic video correction. Finally, through this method, a set of vessel situation active intelligent perception systems based on multisensor fusion was developed. Experiments show that this method has better environmental applicability and detection accuracy than traditional manual detection and any single monitoring method.

Multi-sensor weighted fusion algorithm based on improved TOPSIS

In the process of track fusion of multi-sensor target tracking, multi-sensor weighted fusion is used to overcome the shortcoming of incomplete single information source. In order to change the weight of each sensor participating in fusion in real time according to the merits and demerits of sensor information adaptively, a method of sensor weight calculation based on improved TOPSIS is proposed. Firstly, the local estimation and global prediction residuals of each sensor are composed into residuals matrix, from which the ideal solution is found, and then the relative closeness degree is calculated according to the grey correlation distance between the local estimation residuals and the ideal solution, so as to screen the sensors with high weight to participate in the fusion. The simulation results show that the tracking error of the algorithm after fusion is significantly reduced compared with the local estimation error of each sensor, and the tracking error of multiple sensors for the target can be realized.

A Modified Bayesian Framework for Multi-Sensor Target Tracking with Out-of-Sequence-Measurements.

Target detection and tracking is important in military as well as in civilian applications. In order to detect and track high-speed incoming threats, modern surveillance systems are equipped with multiple sensors to overcome the limitations of single-sensor based tracking systems. This research proposes the use of information from RADAR and Infrared sensors (IR) for tracking and estimating target state dynamics. A new technique is developed for information fusion of the two sensors in a way that enhances performance of the data association algorithm. The measurement acquisition and processing time of these sensors is not the same; consequently the fusion center measurements arrive out of sequence. To ensure the practicality of system, proposed algorithm compensates the Out of Sequence Measurements (OOSMs) in cluttered environment. This is achieved by a novel algorithm which incorporates a retrodiction based approach to compensate the effects of OOSMs in a modified Bayesian technique. The proposed modification includes a new gating strategy to fuse and select measurements from two sensors which originate from the same target. The state estimation performance is evaluated in terms of Root Mean Squared Error (RMSE) for both position and velocity, whereas, track retention statistics are evaluated to gauge the performance of the proposed tracking algorithm. The results clearly show that the proposed technique improves track retention and and false track discrimination (FTD).

RETRACTED: The application of multi-sensor target tracking and fusion technology to the comprehensive early warning information extraction of landslide multi-point monitoring data

• Application of multi-sensor target tracking fusion technology. • Proposes a spatiotemporal registration method and Ensemble Kalman Filte. • The test simulation is carried out by selecting 30 monitoring data. Landslide disasters are the second largest geological disaster activity in addition to earthquake disasters. They cause economic losses of up to several billion yuan each year, resulting in thousands of deaths and injuries. Therefore, it is important to monitor and alert landslide disasters. Significance. In addition, after the occurrence of landslides, a large amount of deposits will be created, which will further breed mudslides. The ability to qualitatively and quantitatively extract landslide information at the first time is particularly important for disaster prevention, mitigation, and disaster management. Therefore, the extraction of landslide information is a significant measure. At the same time, due to the limitations of sensors, how to organically fuse data from multiple sensors to achieve tracking performance that cannot be achieved by a single sensor has become the focus of research and attention on multi-sensor data fusion technology in the field of target tracking. Based on the above background, the research content of this paper is the application of multi-sensor target tracking fusion technology to achieve comprehensive warning information extraction of landslide multi-point monitoring data. This paper proposes a spatiotemporal registration method and Ensemble Kalman Filter (EnKF) for target tracking Algorithm, and the prediction model is established through optimization problem and target tracking. Finally, the test simulation is carried out by selecting 30 monitoring data of a monitoring point of a new construction project as the detection value. The experimental results show that when the moving target model is unknown in actual work, the polynomial. The order of the fitting and the boundary conditions of the spline fitting are not easy to give, but you can optionally use the sensor measurement data in the middle part and then use the spline fitting algorithm to obtain a better time registration effect; In terms of spatial registration, in the Z-axis direction, the measurement error of sensor A and sensor B is large, and the maximum value exceeds 2000 m. The maximum value of sensor A error between the measured values and theoretical values of radar A and B after registration is about 1000 m, which is the Z axis direction, and the maximum error of sensor B is about 1000 m, which is also the Z axis direction. The error has been significantly reduced. EnKF is better than the UKF algorithm under non-Gaussian noise conditions. The PF algorithm itself is suitable for nonlinear and non-Gaussian systems and has the best tracking performance.