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assignmentutor-lab™ 为您的留学生涯保驾护航 在代写组合学Combinatorics方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写组合学Combinatorics代写方面经验极为丰富，各种代写组合学Combinatorics相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 英国补考|组合学代写Combinatorics代考|False Alarms

False alarms, or clutter, are measurements that correspond to no object; they are “insertions” into the measurement set. They arise in many different applications. As mentioned in Sect. 1.3, they are often statistical artifacts of the sensor signal processor caused by low SNR. In some applications, such as electronic sensing devices, they are a residual (physical) background process known as “dark current.” No matter how it arises, the false alarm process is assumed to be independent of object and object-measurement processes.

Suppose the number of false alarms is Poisson distributed with mean number $\Lambda_{C}$, so its $\mathrm{GF}$ is $G_{C}(w)=\exp \left(-\Lambda_{C}+\Lambda_{C} w\right)$. The indeterminate variable $w$ is used because false alarms are added to the total measurement count. False alarms change the definition of the number of measurements- $M$ is now the sum of the number of measurements generated by objects and the number that are false alarms. With this new definition of $M$, the GF of the sum of independent processes is the product of their GFs (see Appendix A). With a Poisson distributed object process, (1.47) becomes
\begin{aligned} G_{N M}(z, w) &=G_{C}(w) G_{N M}(z, w) \ &=\exp \left(-\Lambda_{C}+\Lambda_{C} w-\chi \Lambda_{o}+\chi \Lambda_{O}(1-\rho) z+\chi \Lambda_{o} \rho z w\right) \end{aligned}
The probabilities $\operatorname{Pr}{N=n, M=m}$ are given by the coefficients of the bivariate power series of (1.48) expanded about the origin.

## 英国补考|组合学代写Combinatorics代考|Organization of the Book

The rest of the book adds meat to the bare bones outlined in this chapter to show that $\mathrm{AC}$ is well suited to model diverse problems in multiple object tracking. Much of the discussion will be novel to readers unfamiliar with $\mathrm{AC}$ and $\mathrm{GFs}$, so a relaxed writing style is used throughout the book. The emphasis on constructive mathematical methods and algorithms means that unnecessary abstractions and details are relegated to the references. The goal is to build the intuition and insight needed by practitioners for independent study. The book is largely self-contained.

The book proceeds in stages. Chapter 2 is all about the classic Bayes-Markov filter and the well-known family of PDA (probabilistic data association) and IPDA (integrated PDA) filters. These are single-object filters, and the focus is on how to formulate and derive them using generating functions and the $\mathrm{AC}$ method. Chapter 3 extends these methods to the JPDA (joint PDA) and JIPDA (joint IPDA) filters for tracking multiple objects, assuming that the number of objects is known. These chapters are best read as a “bridge” between very different combinatorial styles – the standard enumerative method and the methods of AC.

Chapter 4 is devoted to a family of superpositional, or intensity, filters called CPHD (cardinalized probability hypothesis density) filters. They are based on cluster point processses. The connection to the traditional JPDA filtêr is clearly and convincingly revealed in two simple steps. The first step applies superposition to the JPDA filter. This step has many lively implications that are discussed carefully. The superposed JPDA filter is called the JPDAS filter. The second step assumes the number of objects in JPDAS is a random integer with a known GF. The result is the CPHD filter. It is called the PHD filter if the number of objects is Poisson distributed. The mathematical forms of the generating functions of the CPHD and the JPDAS filters show clearly the similarity of these filters and, at the same time, sharply delineate the differences between them.

# 组合学代考

## 英国补考|组合学代写Combinatorics代考|False Alarms

$$G_{N M}(z, w)=G_{C}(w) G_{N M}(z, w) \quad=\exp \left(-\Lambda_{C}+\Lambda_{C} w-\chi \Lambda_{o}+\chi \Lambda_{o}(1-\rho) z+\chi \Lambda_{o} \rho z w\right)$$

## 有限元方法代写

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## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

assignmentutor™您的专属作业导师
assignmentutor™您的专属作业导师