Research and Application of Multi-Target Tracking Based on GM-PHD Filter

In recent years, multi-target tracking technology based on Gaussian Mixture- Probability Hypothesis Density (GM-PHD) filtering has become a hot field of information fusion research. This article outlines the generation and development of multi-target tracking methods based on GM-PHD filtering, and the principle and implementation method of GM-PHD filtering are explained, and the application status based on GM-PHD filtering is summarized, and the key issues of the development of GM-PHD filtering technology are analyzed.

proximates PHD with the sum of multiple Gaussian terms [2]. It is assumed that the detection probability and survival probability are independent with the states, and the PHD of the target derivative set and the new set can be both expressed as Gaussian sum form, the analytical solution of PHD is obtained by updating the prediction of PHD, and the state information of each Gaussian item can be obtained, and the calculation amount due to the increase of Gaussian item is reduced by pruning and merging strategy. Target tracking has less computational complexity, and has attracted the attention of many scholars and engineering technicians. This paper reviews the development process of multi-target tracking method based on the Gaussian Mixture-Probability Hypothesis Density (GM-PHD) filtering, discusses the current development status of the technology, and discusses the future improvement and development trend of GM-PHD filtering technology.

Basic Principles of PHD Filters
The PHD filter algorithm is actually a first-order moment recursion process for the posterior multi-target state. Because the integration step of the probability density function is completed on the motion state of a single target, it has a small amount of calculation and compared with the Bayes filter algorithm, it is easier to handle. The process of PHD filtering is the same as the traditional Bayes filtering. It is also divided into a forecasting process and an update. The core thought process is as Figure 1.
In a multi-target tracking environment, the updated PHD function Taking the integer value closest to k N as the desired result of the target number at time k, estimated value of the target number could be expressed as Equation (2), and the peak number indicates the target number.

Principle of GM-PHD Filter
There are complex integration operations in the PHD filter update process, where ( ; , ) ⋅  m P represents a Gaussian distribution with mean density m and The second condition is to assume that the target survival probability and sensor detection probability are independent with the target state: , , The third condition is that the strength of the new target random set ( ) k γ x and a derivative target random set ; ,  The first step is the prediction process, assume that the posterior intensity at time 1 k − has the following mixed Gaussian form: Then the predicted PHD at time k can be shown as a Gaussian mixture: The second step is the update process, suppose the predicted PHD at time k could be described as a Gaussian mixture: Then the posterior PHD at time k is also Gaussian mixture:

GM-PHD Filter Simulation
GM-PHD could be widely utilized in the field of multi-targets. Figure 2 shows that the definition of GM-PHD in the case of two targets as follows: The simulation conditions are defined as follows: Initially, the A target coordinates are (250, 250), the B target coordinates are (−250, −250), and the A and

Application of PHD Filter in Target Tracking
In the past ten years, GM-PHD filtering has made a lot of research results under the continuous efforts of theoretical research scholars represented by Mahler and engineering technicians represented by Vo. At present, PHD filtering technology has been applied in many fields of research, extended multi-target tracking, image tracking, group target detection and tracking, multi-target tracking under unknown parameters and sensor management Numerous applications [5]. Next, we will summarize the outstanding achievements in various application fields.

Prospect of PHD Filter Technology
Throughout the above, PHD filtering has achieved many encouraging research results, focusing on the multi-target tracking research of PHD filtering, and now the existing problems in this field and the key aspects of future development are summarized as follows: • Research on PHD filter Implementation methods The current PHD filter implementation methods are mainly sequential Monte Carlo (particle filter) method and Gaussian mixture method, but the particle filter method requires higher selection of density function, and this type of function is difficult to confirm. A large number of sampling approximate calculations are required, which require high computing power and are difficult to implement in engineering. The Gaussian mixture me- the accuracy of target estimation has decreased. Therefore, it is necessary to carry out the algorithm research on the compromise between computational complexity and estimation accuracy.
• Target tracking in the case of multiple sensors Research Currently, Target tracking in the case of multiple sensors based on PHD filtering usually assumes that the observation data of each sensor is completely independent, adopts sequential processing, and has low computational complexity, but low tracking accuracy and poor stability. The other is the product multi-sensor PHD filtering method, which comprehensively considers the multi-sensor observation information, which has better tracking accuracy and stability, but the calculation complexity is higher and it is difficult to achieve [6].
Therefore, how to achieve multi-sensor multi-target tracking with higher accuracy, stronger stability and easy engineering realization is the direction of future development.
• Research on the tracking method of multi-target situation Currently, the tracking method of multi-target situation based on PHD filtering is mostly based on linear Gaussian models, which have certain limitations. It is more universal to carry out research on nonlinear non-Gaussian multi-extended target filtering methods [7]. The research on multi-expanded target tracking methods is mainly a two-dimensional model. If a three-dimensional extended model of an extended target can be developed, it will be able to describe the target more accurately and realistically, thereby obtaining more extended target information.
• Research on track generation In the field of multi-target tracking, the PHD filter tracking algorithm can be used to obtain the number and status of the targets at any time. There is no track association information between adjacent target states, and the relationship between targets at consecutive moments is not given. Estimate the trajectory of all targets. At present, the track generation method based on PHD filtering is mainly combined with the traditional data correlation technology, and the multi-target RFS estimated at each discrete time is regarded as an "observation set", and then the traditional data correlation technology is used to form the target track, but this method does not Considering the situation of missed detection and false estimation of the target, it will lead to the formation of a false track and a target will produce multiple tracks, which will be detrimental to the comprehensive situation understanding and analysis of the surveillance scene [8]. In the future, how to realize joint state estimation and track generation and extraction under the framework of PHD filtering is a direction worthy of study.
• Research on multi-target joint detection and classification method At present, most researches deal with the three problems of target detection, tracking and classification separately. However, these three problems can ac- existing There is a theoretical framework for effective theoretical approaches to the foundation of strict theoretical interpretation, but the emergence of RFS theory fills the gap in this area, which can realize the simultaneous detection, tracking and classification of targets, which will greatly improve the ability of modern surveillance systems to acquire multi-target information, Has greater theoretical and practical significance.