Evolution of Radar Target Tracking Algorithms: A Move Towards Knowledge Based Multi-Sensor Adaptive Processing

Target detection and tracking is a key technology in intelligent monitoring system. However, it is still difficult to design a robust, accurate and real-time target detection and tracking algorithm. Taking target Tracking as the research focus, this paper focuses on one of the current mainstream algorithms, namely, TLD (tracking-learning-detection) target Tracking algorithm. Based on the theory of TLD target tracking and corner detection, a new target tracking method was proposed to solve the existing problems. A classifier is built online to track and learn the target in real time, and a target contour detector is added in the system to complement the random forest detector to solve the problem of tracking failure. Experimental results show that the proposed algorithm can effectively overcome the influence of fast change or depth occlusion, and improve the tracking efficiency of moving targets.

The highly competitive and rapidly changing environment faced by businesses has greatly increased the need for strategic planning. The importance of formulating strategies to reach competitive advantages with implications in the performance is becoming increasingly evident in the seaport context. Thus, it is relevant and appropriate to apply strategic positioning tools to seaports given the role of competitive strategies in the growth and development of this industry. This research aims to analyse the strategic positioning of the leading Iberian Peninsula seaports (Portuguese and Spanish seaports) using the Boston Consulting Group (BCG) matrix as a strategic tool in an evolutionary perspective. The portfolio analysis developed subsequently focuses on the annual data of eight seaports in a selected period of 18 years (1992–2009) and on five categories of traffic: liquid bulk (LB), dry bulk (DB), containers (CO), ro-ro (roll-on/roll-off) and conventional cargo (CC). This research allows us to compare and analyse different levels of performance and identify which of the seaports have improved their strategic positioning during the considered period. The findings reveal a better positioning of Spanish seaports in relation to total traffic and that most of the seaports in the BCG matrix had changed from the first to the third period. The seaport of Valencia is the only one that has maintained its Star Performer position in all the 18 years analysed. Furthermore, considering container traffic, the results evidence the seaports of Algeciras, Valencia and Barcelona as having attained a remarkable position of leadership.

The presence of 4G technology encourages changes in customer behavior in the Indonesian market. This technology is believed to have fulfill the needs of the community for data access to social media, messenger, transportation services and ecommerce. On the other hand 4G technology has also replaced the 2G technology that has been primary income for operators in Indonesia. The rampant messenger technology used in the 4G era enables users to make video calls, send text messages or voice over IP networks and replace conventional calls (Circuit Switch) and Short Message Services (SMS). The sustainability of 2G technology enters a critical phase, where traffic and subscriber continues to degrade and the need for spectrum allocation for data services gets louder. For that, in this paper we analyze about the sustainability of this technology using Boston Consulting Group (BCG) Matrix. From BCG Matrix analysis, it shown that 2G have low market share and experience low growth rates. It means 2G focussed on voice services are on declining phase of product life cycle.

Vehicle detection and tracking from a UAV perspective often encounters omission and misdetection due to the small targets, complex scenes and target occlusion, which finally influences hugely on detection accuracy and target tracking stability. Additionally, the number of parameters of current model is large that makes it is hard to be deployed on mobile devices. Therefore, this paper proposes a YOLO-LMP and NGCTrack-based target detection and tracking algorithm to address these issues. Firstly, the performance of detecting small targets in occluded scenes is enhanced by adding a MODConv to the small-target detection head and increasing its size; In addition, excessive deletion of prediction boxes is prevented by utilizing LSKAttention mechanism to adaptively adjust the target sensing field at the downsampling stage and combining it with the Soft-NMS strategy. Furthermore, the C2f module is replaced by the FPW to reduce the pointless computation and memory utilization of the model. At the target tracking stage, the so-called NGCTrack in our algorithm replaces IOU with GIOU and employs a modified NSA Kalman filter to adjust the state-space aspect ratio for width prediction. Finally, the camera adjustment mechanism was introduced to improve the precision and consistency of tracking. The experimental results show that, compared to YOLOv8, the YOLO-LMP model improves map50 and map50:95 metrics by 10.3 and 12.2%, respectively and the number of parameters is decreased by 47.7%. After combined it with the improved NGCTrack, the number of IDSW reduced by 73.6% compared to the ByteTrack method, while the MOTA and IDF1 increase by 5.2 and 9.8%, respectively.

Product portfolio analysis is used for analyzing company products’ strategic market position, in order to decide which products should receive more or less investment. The Boston Consulting Group (BCG) growth-share matrix is the best known approach for product portfolio analysis. It is a strategic tool for identifying products’ strategic market positions and formulating resources allocation strategies. However, traditional BCG matrix is a static historic analysis, which different time frames are not considered. Thus, the main purpose of this study was to propose a new BCG method, which is a dynamic trend analysis method and different time frames are considered for product portfolio analysis. A numerical case study was conducted to demonstrate that the proposed BCG method has achieved the objectives of the study for company products’ portfolio analysis. First, the rule-based BCG method has contributed a new vague set based data collection method. Second, the rule-based BCG method has contributed a new application for identifying company products’ strategic market positions and resources allocation strategy formulation.

In the classical kernel correlated target tracking algorithms, most of them initialize the target detector by importing the manually marked targets. Moreover, the anti-occlusion ability of the correlated filtering algorithm is poor, which leads to the model drift in complex scenes. This work combined the image matching technology with the target tracking technology. In the case of a certain similarity between the input image and the target, the improved SIFT algorithm SIFT-BRISK is used to complete the initialization of the target detector, which can realize the target tracking in more common cases. And then APCE confidence level is introduced to judge whether the target tracking performance is well, adapt the update strategy of keeping track in the high confidence, dynamically adjusting the tracking model in the low confidence, and improve the details of the kernel correlated target tracking algorithm. Thus, the drift of the training model is suppressed to a certain extent. Finally, the algorithm is tested under the OTB-2015 dataset and the result proves that the improved algorithm has better accuracy and higher speed than traditional target tracking algorithms.

Target positioning and tracking in wireless sensor networks (WSNs) are necessary for real ap-plications. This paper uses an artificial fish swarm algorithm (AFSA) with receive signal strength indicator (RSSI) for indoor target positioning and tracking. The performance of random and fixed artificial fish deployment topologies in AFSA for target positioning and tracking is investigated when the number of artificial fish is 12, 24, 52, 72, and 100, respectively. The simulation results show that the difference in average error between the random and fixed deployments is 5%, and the average positioning time is 2% for target position. The target tracking performance of fixed deployment is better than that of random one when the number of artificial fish is small.

Wireless Sensor Network (WSN) is a new technology integrated with communications, embedded technology, and network functions; having capability of real-time collecting, communicating, and sensing, for which dynamic target locating and tracking is its critical task, as well as a basic element in its commercial and military application such as battlefield information accessing and traffic monitoring. Sensor target tracking is the technology of target location estimate based on measurements from corresponding sensors. An inaccurate estimated location may very easily lead to target tracking failure which reduces the reliability and stability of the target tracking [1]. Experts and scholars have done a lot of researches to solve problems exited in wireless sensor target tracking, and many new tracking algorithms have been proposed, among which tracking algorithms such as target contrail-fitting, linear prediction, Kalman filter, unscented Kalman filter(UKF) and particle filter[2] are most common applied. Sikdar, with his partners, has proposed target location tracking algorithm based on linear sensor which is proved better than conventional contrail fitting algorithm in its effects. Zhang Xiaoping , with his partners, has proposed WSN target tracking algorithm through modeling for quadratic polynomial motions which has improved target tracking accuracy[3]. However, there is a problem in common: all the algorithms above assume the tracking target contrail-fittings being in linear motion, while the actual contrail fitting is of uncertainty and variability, and the estimated accuracy can hardly be improved after being to a certain level. In this condition, the tracking error of the above algorithms can be greater [4]. Kalman filter algorithm can be applied for weakly nonlinear models, but for strongly nonlinear models, Unscented Kalman Filter algorithm can be better. Kalman filter work well in cases based on model-linear and Gaussian noise, thus its estimated accuracy is not that ideal [5, 6]. Particle filter algorithm is based on optimal regression Bayesian filtering algorithm in Monte Carlo simulation, which can accurately estimate the any distribution state with only a small amount of mobile anchor nodes. PF algorithm has distinguished advantages in processing non-linear, non-Gaussian cases [7]. However, in PF application process after several iterations, diversified degradation of the particles will occur, which is likely to lead a target tracking loss, thus further affect WSN target tracking accuracy [8]. In order to solve the diversified degradation of the particles in Particle Filter algorithm, this paper Research on the Target Tracking Algorithm for Wireless Sensor Network Based on Improved Particle Filter Liang Peng,Xinhan Huang, Li He

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.

This paper aims to increase the Unmanned Aerial Vehicle's (UAV) capacity for target tracking. First, a control model based on fuzzy logic is created, which modifies the UAV's flight attitude in response to the target's motion status and changes in the surrounding environment. Then, an edge computing-based target tracking framework is created. By deploying edge devices around the UAV, the calculation of target recognition and position prediction is transferred from the central processing unit to the edge nodes. Finally, the latest Vision Transformer model is adopted for target recognition, the image is divided into uniform blocks, and then the attention mechanism is used to capture the relationship between different blocks to realize real-time image analysis. To anticipate the position, the particle filter algorithm is used with historical data and sensor inputs to produce a high-precision estimate of the target position. The experimental results in different scenes show that the average target capture time of the algorithm based on fuzzy logic control is shortened by 20% compared with the traditional proportional-integral-derivative (PID) method, from 5.2 s of the traditional PID to 4.2 s. The average tracking error is reduced by 15%, from 0.8 m of traditional PID to 0.68 m. Meanwhile, in the case of environmental change and target motion change, this algorithm shows better robustness, and the fluctuation range of tracking error is only half of that of traditional PID. This shows that the fuzzy logic control theory is successfully applied to the UAV target tracking field, which proves the effectiveness of this method in improving the target tracking performance.

The purpose of this research itself is to determine the market position PT Samsung Elektronik Indonesia in the BCG matrix (Boston Consulting Group) and formulating strategies to maintain market share. This type of research used in this research is to use descriptive methods. While this research data collection technique uses secondary data based on written documents in the form of sales results and market share PT Samsung Elektronik Indonesia. The calculation result of BCG Matrix owned by PT Samsung Elektronik Indonesia shows that the company is in the position of Cash Cows where the position has a medium market growth with high market share. Thus the company can formulate strategies in the form of product development and diversification. Keywords: BCG (Boston Consulting Group Matrix), Implementation of Strategies, Market Share Defense

Energy efficiency is a critical issue for mobile target tracking in wireless sensor networks, which typically consist of small-sized battery-operated devices with limited processing capability. Clustering techniques are widely adopted for target tracking in large-scale sensor networks to reduce energy consumption and delay. In this paper, we propose an energy efficient target tracking algorithm, called triangular cluster-based target tracking (TCTT). However, clustering based target tracking suffers from the boundary problem caused by insufficient switch among clusters when the target moves close to the boundary. To overcome the boundary problem, we propose a cluster transforming mechanism to prevent loss of the target. To reduce the complexity of accurately locating the target position, we employ the gray model prediction algorithm with wavelet denoising. Through simulation results, we show that our proposed scheme achieves better energy efficiency compared with other typical target tracking algorithms.

With the continuous deepening of economic globalization and the continuous development of Internet big data, the spread of martial arts culture has attracted people’s attention. The advent of the Internet big data era has brought new opportunities and media for the dissemination of martial arts culture but also brought certain risks and challenges. This paper firstly uses the literature method, logical analysis method, and related theories of communication, with the help of the SWOT strategic analysis method, to analyze the internal advantages and disadvantages faced by martial arts culture communication itself, as well as the external opportunities in the context of the Internet big data era and challenges. In order to realize the target detection and tracking task of Martial athletes, this paper proposes a target detection and tracking algorithm for Martial athletes. First, the algorithm constructs and trains a fully CNN-based athlete target detection model, which can detect the first frame of athlete targets in martial arts competitions or martial arts training videos. Experiments show that the athlete positioning information output by the detection model and the video frame sequence are jointly input into the athlete target tracking module based on neighborhood similarity, so as to realize the target tracking of martial arts athletes.

The poor real-time performance and target occlusion occurred easily when the UAV was tracking the target. In this paper, a target tracking method based on the Back Propagation neural network fusion Kalman filter algorithm was developed to solve the position prediction problem of the UAV target tracking in real time. Firstly, the target tracking algorithm was used to acquire the center position coordinates of the target on the onboard computer, and then the coordinate difference matrix was constructed to train the BP neural network in real time. Secondly, when the target was occluded by the obstacles judged by the Bhattacharyya coefficient, the BP neural network fusion Kalman filter algorithm was used to accurately predict the center position coordinates of the occluded target. Then the flight speed of UAV was calculated by the deviation between the coordinates of the target and the image center. Finally, the velocity command was sent to the UAV by the onboard computer. The experimental results shown that the target position predicted by BP neural network fusion Kalman filter algorithm was more accurate and robust in predicting the center position coordinates of the target, and the UAV can track the moving target on the ground stably.

The target tracking by space-based surveillance systems is difficult due to the long distances, weak energies, fast speeds, high false alarm rates, and low algorithmic efficiencies involved in the process. To mitigate the impact of these difficulties, this article proposes a target tracking algorithm based on image processing and Transformer, which employs a two-dimensional Gaussian soft-thresholding method to reduce the image noise, and combines a Laplace operator-weighted fusion method to augment the image, so as to improve the overall quality of the image and increase the accuracy of target tracking. Based on the SiamCAR framework, the Transformer model in the field of natural language processing is introduced, which can be used to enhance the image features extracted from the backbone network by mining the rich temporal information between the initial and dynamic templates. In order to capture the information of the target’s appearance change in the temporal sequence, a template update branch is introduced at the input of the algorithm, which realizes the dynamic update of the templates by constructing a template memory pool, and selecting the best templates for the candidate templates in the memory pool using the cosine similarity-based selection, thus ensuring the robustness of the tracking algorithm. The experimental results that compared with the SiamCAR algorithm and the mainstream algorithms, the TrD-Siam algorithm proposed in this article effectively improves the tracking success rate and accuracy, addressing poor target tracking performance under space-based conditions, and has a good value of application in the field of optoelectronic detection.