Expected Attitude Angle Design for Spacecraft Multi-target Tracking

Real-time high-accuracy localization has a wide range of applications in scenarios, such as pedestrian navigation, emergency rescue, and vehicle networks. In these conditions, the measurement models are often nonlinear, and traditional Kalman and particle filters cannot provide long-time high-precision location-based services. To this end, we propose a Gaussian condensation filter (GCF) algorithm that can achieve high-accuracy localization in a harsh environment. However, aiming at the degradation of sampling points in target tracking based on the GCF, this article proposes a GCF algorithm based on particle flow which transfers the sample points satisfying the prior distribution of the target state to the posterior distribution, thereby improving the practical accuracy of the target-tracking algorithm. Further, to enhance the information fusion in the cooperative network, we propose a multitarget cooperative tracking algorithm to accomplish spatially constrained timing filtering of state information for improving the error correction of the target nodes on timing estimation. Numerical simulations are conducted to determine the effectiveness of our proposed algorithms. Compared with the GCF, its positioning accuracy is improved to 44.6%. Compared with the Gaussian condensation algorithm based on particle flow (PF), the practical accuracy of the GCF algorithm based on cooperative constrained PF in multitarget tracking is improved to 58.1%.

Soccer player video target tracking is a very challenging task, which has good practical and commercial value. Traditional soccer game target tracking relies on athletes to carry a recording chip to achieve target tracking, but the cost is very high. With the rapid development of photography technology and deep learning technology, athletes’ target tracking is realized through soccer game video. Deep learning technology is applied to computer vision detection and tracking. How to realize soccer players’ video target tracking under deep learning is a challenging lesson. To solve this problem, this paper takes the video target tracking of football players as the research object, collects the game images of the stadium through multiple cameras, realizes the long-term accurate tracking of multiple players, and establishes a multicamera multitarget tracking system. The KCF algorithm and the improved KCF algorithm formed by replacing the hog feature of the KCF algorithm with the depth convolution neural network are used to compare and analyze the impact of different target tracking ranges and target numbers on the target tracking accuracy of the system, so as to accurately obtain the motion trajectory of football players. The results show that the image data of football matches are collected independently by multiple cameras, and the data of multiple cameras are collected to generate each target motion datum. The KCF algorithm of multicamera multitarget tracking has good robustness and real-time for long-term accurate tracking of football players; the KCF algorithm and the improved KCF algorithm have high accuracy in target tracking. With the increase of tracking frame range, the accuracy of target tracking of the two algorithms is improved. At the same time, multitarget tracking helps to improve the antiocclusion ability of the system. The research results have important practical significance and good application prospects for the analysis technology of video content of football matches.

Target tracking is a hot topic in the field of computer vision. It uses the context information of video or image sequence to model the appearance and motion information of the target, so as to predict the motion state of the target and calibrate the position of the target. From the perspective of building models, target tracking algorithms can be divided into generative models and discriminant models; From the number of tracking targets, it can be divided into single target tracking and multi-target tracking. Target tracking integrates the theories and algorithms of image processing, machine learning, optimization and other fields. It is the premise and foundation to complete higher-level image understanding.The basic task of target tracking is to give the initial position of the target in a video sequence and continuously track and locate the target in each subsequent frame. In this process, a priori conditions such as the color, shape and size of the target will not be provided, that is, the tracking algorithm can track only by learning the target in the first frame. The traditional solution is to calculate the project similarity offline and save it in the system for algorithm call, but it can not make full use of the latest scoring data to reflect the change of user interest. To solve the above problems, a target tracking item similarity incremental update mechanism suitable for online applications is proposed, so that the recommendation system can update the similarity data between the corresponding item and other items in real time after the current user submits the item score, so that the recommendation system can recommend based on the latest item similarity data, To adapt to changes in user interests.

The point detections obtained from radars or sonars in surveillance environments include clutter measurements, as well as target measurements. Target tracking with these data requires data association, which distinguishes the detections from targets and clutter. Various algorithms have been proposed for clutter measurement density estimation to achieve accurate and robust target tracking with the point detections. Among them, the spatial clutter measurement density estimator (SCMDE) computes the sparsity of clutter measurement, which is the reciprocal of the clutter measurement density. The SCMDE considers all adjacent measurements only as clutter, so the estimated clutter measurement density is biased for multi-target tracking applications, which may result in degraded target tracking performance. Through the study in this paper, a major source of tracking performance degradation with the existing SCMDE for multi-target tracking is analyzed, and the use of the clutter measurement probability is proposed as a remedy. It is also found that the expansion of the volume of the hyper-sphere for each sparsity order reduces the bias of clutter measurement density estimates. Based on the analysis, we propose a new adaptive clutter measurement density estimation method called SCMDE for multi-target tracking (MTT-SCMDE). The proposed method is applied to multi-target tracking, and the improvement of multi-target tracking performance is shown by a series of Monte Carlo simulation runs and a real radar data test. The clutter measurement density estimation performance and target tracking performance are also analyzed for various sparsity orders.

Acoustic target tracking has shown great advantages for device-free human-machine interaction over vision/RF-based mechanisms. However, existing approaches for portable devices solely track a single target, incapable of the ubiquitous and highly challenging multi-target situations such as double-hand multimedia controlling and multi-player gaming. In this paper, we propose <i>AMT</i><inline-formula><tex-math notation="LaTeX">$^+$</tex-math></inline-formula>, a pioneering smartphone MIMO system to achieve centimeter-level multi-target tracking. The challenge of multi-target occlusion is effectively addressed by employing multiple speaker-microphone pairs. However, the unique challenge raised by MIMO is the superposition of multi-source signals due to the cross-correlation among speakers. Initially, we tackle this challenge by designing a weak cross-correlation signal to reduce interference passively. In <i>AMT</i><inline-formula><tex-math notation="LaTeX">$^+$</tex-math></inline-formula>, we’ve further integrated self-interference cancellation for active minimize interference. The most distinguishing advantage of <i>AMT</i><inline-formula><tex-math notation="LaTeX">$^+$</tex-math></inline-formula> lies in the elimination of the raised multipath effect, which is commonly ignored in previous work by hastily assuming targets as particles. <i>AMT</i><inline-formula><tex-math notation="LaTeX">$^+$</tex-math></inline-formula> employs Doppler filtering over delay subtraction for echo suppression. Further, by non-particle target reflections modeling results, we introduce a distance-projection-based method for continuous target identification and tracking. Implemented on commercial smartphones, <i>AMT</i><inline-formula><tex-math notation="LaTeX">$^+$</tex-math></inline-formula> achieves on average 0.54 cm, 1.37 cm, and 2.13 cm errors for single, double, and triple target tracking respectively, and on average 97.0% classification accuracy for 14 controlling gestures.

To solve the low accuracy and poor stability in traditional object tracking methods for martial arts competition videos, a Kalman filtering algorithm based on feature matching and multi object tracking is proposed for object detection in martial arts competition videos. Firstly, feature matching in multi target tracking is studied. Then, based on target feature matching, the Kalman filtering algorithm is fused to construct a target detection model in martial arts videos. Finally, simulation experiments are conducted to verify the performance and application effectiveness of the model. The results showed that the average tracking errors of the model on the X and Y axes were 3.86% and 3.38%, respectively. At the same time, the average accuracy and recall rate in the video target tracking process were 93.64% and 95.48%, respectively. After 100 iterations, the results gradually stabilized. This indicated that the constructed model could accurately detect targets in martial arts competition videos. It had high tracking accuracy and robustness. Compared with traditional object detection methods, this algorithm has better performance and effectiveness. The Kalman filter algorithm based on feature matching and multi target tracking has broad application prospects and research value in target detection in martial arts competition videos.

Joint tracking sequence and dwell time allocation for multi-target tracking with phased array radar

Autonomous rendezvous and proximity operations between a chaser spacecraft and a target space object, consist of autonomous controlled approach and controlled docking with or controlled capture of the target by the chaser. Guidance, navigation and control problems during autonomous rendezvous and proximity maneuvers between spacecraft are challenging, particularly when the target spacecraft or space object is not cooperating with the chaser spacecraft. In this work, we present a guidance scheme and a control scheme for a chaser spacecraft that is tasked to synchronize its motion with a target space object during close proximity maneuvers. The guidance scheme generates a desired state trajectory based on remote measurements of the motion states of the target from the chaser and motion prediction of the target. The tracking control scheme for the chaser results in asymptotic tracking of the desired state trajectory with an almost global domain of convergence on the state space. These schemes are applied to the situation where the chaser spacecraft synchronizes its attitude motion with the target, while maintaining a constant relative position with respect to the target at the end of the maneuver. Numerical simulation results are presented to show the performance of these guidance and tracking control schemes.

Considering the power load in the system has the characteristics of random variation, and the prediction of the power load is the key to ensure the safe operation of the power. At present, there are many methods of power load analysis, based on the computer video capture equipment and target tracking technology, the power load analysis has a good performance in the application. Therefore, in this paper, the algorithm of target tracking under complex scenes of power system operation was researched, its specific application in power load analysis was discussed, and then, the multi-target tracking, irregular target tracking and other technical problems of the target tracking algorithm in the application process in the real world were solved. According to taking the power load analysis of a park grid system as the practical application object, it proved that the research of the target tracking algorithm had a good application effect in the real power load analysis.

Marine target recognition and tracking are of great significance for achieving intelligent perception of the marine environment, avoiding ship collisions, and maintaining ship navigation safety. Propose a sea multi-target tracking model based on visual, AIS, and radar data fusion to achieve real-time monitoring of sea navigation targets. Firstly, an imagebased maritime target recognition model is established based on the YOLOv3 algorithm to achieve automatic recognition of maritime navigation targets around ships; Secondly, based on the SORT framework, a maritime navigation target tracking model was established to achieve real-time tracking of multiple targets at sea. The actual ship test results show that the average accuracy, average detection time, average tracking accuracy, and tracking precision of multi-target detection results at sea under different weather conditions can provide effective technical support for multi-target tracking tasks at sea.

Multitarget tracking in the interference environments suffers from the nonuniform, unknown, and time-varying clutter, resulting in dramatic performance deterioration. We address this challenge by proposing a robust multitarget tracking algorithm, which estimates the states of clutter and targets simultaneously by the message-passing (MP) approach. We define the nonhomogeneous clutter with a finite mixture model containing a uniform component and multiple nonuniform components. The measured signal strength is utilized to estimate the mean signal-to-noise ratio of targets and the mean clutter-to-noise ratio of clutter, which are then used as additional feature information of targets and clutter to improve the performance of discrimination of targets from clutter. We also present a hybrid data association, which can reason over correspondence between targets, clutter, and measurements. Then, a unified MP algorithm is used to infer the marginal posterior probability distributions of targets, clutter, and data association by splitting the joint probability distribution into a mean-field approximate part and a belief propagation part. As a result, a closed-loop iterative optimization of the posterior probability distribution can be obtained, which can effectively deal with the coupling between target tracking, clutter estimation, and data association. Simulation results demonstrate the performance superiority and robustness of the proposed multitarget tracking algorithm compared with the probability hypothesis density (PHD) filter and the cardinalized PHD (CPHD) filter.

Under space-based observation conditions, targets are subject to a large number of stars, clutter, false alarms, and other interferences, which can significantly impact the traditional Gaussian mixture probability hypothesis density (GM-PHD) filtering method, leading to tracking biases. To enhance the capability of the traditional GM-PHD method for multi-target tracking in space-based platform observation scenarios, in this article, we propose a GM-PHD algorithm based on spatio-temporal pipeline filtering and enhance the conventional spatio-temporal pipeline filtering method. The proposed algorithm incorporates two key enhancements: firstly, by adaptively adjusting the pipeline’s central position through target state prediction, it ensures continuous target tracking while eliminating noise; secondly, by computing trajectory similarity to distinguish stars from targets, it effectively mitigates stellar interference in target tracking. The proposed algorithm realizes a more accurate estimation of the target by constructing a target state pipeline using the time series and correlating multiple frames of data to achieve a smaller optimal sub-pattern assignment (OSPA) distance and a higher tracking accuracy compared with the traditional algorithm. Through simulations and real-world data validation, the algorithm showcased its capability for multi-target tracking in a space-based context, outperforming traditional methods and effectively addressing the challenge of stellar interference in space-based multi-target tracking.

This paper describes the performance of the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter for multiple human tracking in an intelligent vision system. Human movement trajectories were observed with a camera and tracked by the GM-PHD filter. The filter multi-target tracking ability was validated by two random motion trajectories in the paper. To evaluate the filter performance in relation to the target movement, the motion velocity and angular velocity as key evaluation factors were proposed. A circular motion model was implemented for simplified analysis of the filter tracking performance. The results indicate that the mean absolute error defined as the difference between the filter prediction and the ground truth is proportional to the motion speed and angular velocity of the target. The error is only slightly affected by the tracking targets' number.

The underwater sensor network is currently a hot research field in academia and industry with many underwater applications, such as ocean monitoring, seismic monitoring, environment monitoring, and seabed exploration. Underwater target tracking is a critical component of ocean development. This paper studies the underwater target tracking problem of the wireless sensor network. The core technology of the target tracking algorithm is the filtering algorithm, which identifies the accuracy of the target tracking system. Nonlinear filtering is a hot issue in target tracking because feasible projects are mostly non-linear systems. The linearization method used in traditional Kalman filtering has serious shortcomings. Therefore, this paper presents the improved cubature Kalman filtering (ICKF) algorithm for underwater target tracking. There is uncertainty in the target movement, an adaptive forgetting factor is given into the cubature Kalman filtering algorithm to directly modify the error covariance to reduce the impact of uncertainties. Then, interactive multi-model technology is introduced to establish the IMMICKF algorithm with multiple states. Compared with other filtering algorithms, the new algorithm can effectively deal with non-linear target tracking problems and obtain better estimation accuracy. The numerical simulation is given to demonstrate the effectiveness of the IMMICKF algorithm.

Robust cardinalized probability hypothesis density filter based underwater multi-target direction-of-arrival tracking with uncertain measurement noise