WO2018157699A1 - 一种全局最优粒子滤波方法及全局最优粒子滤波器 - Google Patents

一种全局最优粒子滤波方法及全局最优粒子滤波器 Download PDF

Info

Publication number
WO2018157699A1
WO2018157699A1 PCT/CN2018/075151 CN2018075151W WO2018157699A1 WO 2018157699 A1 WO2018157699 A1 WO 2018157699A1 CN 2018075151 W CN2018075151 W CN 2018075151W WO 2018157699 A1 WO2018157699 A1 WO 2018157699A1
Authority
WO
WIPO (PCT)
Prior art keywords
particle
particles
population
weight
floating point
Prior art date
Application number
PCT/CN2018/075151
Other languages
English (en)
French (fr)
Inventor
李琳
李耘
Original Assignee
东莞理工学院
李琳
李耘
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 东莞理工学院, 李琳, 李耘 filed Critical 东莞理工学院
Priority to US16/335,675 priority Critical patent/US20200034716A1/en
Publication of WO2018157699A1 publication Critical patent/WO2018157699A1/zh

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/12—Computing arrangements based on biological models using genetic models
    • G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computing arrangements based on specific mathematical models
    • G06N7/01—Probabilistic graphical models, e.g. probabilistic networks
    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03H—IMPEDANCE NETWORKS, e.g. RESONANT CIRCUITS; RESONATORS
    • H03H17/00—Networks using digital techniques
    • H03H17/02—Frequency selective networks
    • H03H17/0248—Filters characterised by a particular frequency response or filtering method
    • H03H17/0255—Filters based on statistics
    • H03H17/0257—KALMAN filters

Definitions

  • the invention relates to a global optimal particle filtering method and a global optimal particle filter, and belongs to the field of signal processing.
  • the state estimation problem of dynamic systems involves many fields, especially in the fields of signal processing, artificial intelligence and image processing, and it also has important application value in the fields of navigation and guidance, information fusion, automatic control, financial analysis, intelligent monitoring and so on.
  • Traditional Kalman filtering is only applicable to linear Gaussian systems, and extended Kalman filtering can only deal with the weak nonlinearity of the system. Therefore, particle filtering, which is not limited by system model characteristics and noise distribution, has attracted much attention in the filtering problem of nonlinear and non-Gaussian dynamic systems.
  • Particle filtering is a filtering method based on Monte Carlo simulation and recursive Bayesian estimation.
  • the basic principle is to obtain a state minimum variance estimation process by finding a set of random samples propagating in the state space, namely "particles", approximating the posterior probability density function, and replacing the integral operation with the sample mean.
  • Common particle filtering algorithms include elementary particle filtering (PF), auxiliary particle filtering (APF), and regularized particle filtering (RPF).
  • Particle degradation means that as the number of iterations increases, the weight of the remaining particles is negligible except for a few particles with large weights.
  • Particle depletion means that after resampling, large weight particles are assigned multiple times and the diversity of particle sets is lost.
  • the key technique to solve these two problems is to propose the selection of the distribution and improve the resampling algorithm.
  • the present invention uses the Unscented Kalman Filter (UKF) algorithm as the importance density function, and thus constructs a globally optimal particle filter using the Lamarckian genetic natural law.
  • ULF Unscented Kalman Filter
  • the object of the present invention is to solve the problem that the existing particle filter algorithm causes a large deviation between the sampled sample and the true posterior probability density sample, and the control ability of the particle diversity and the optimization process is insufficient, which increases the complexity of the particle filter.
  • a global optimal particle filtering method and global optimal particle filter are constructed.
  • a global optimal particle filtering method comprising the following steps:
  • Step 1 Generate an initial particle set
  • Step 2 using the insensitive Kalman filter to sample the importance of the initial particle set to obtain sampled particles
  • Step 3 performing floating-point number coding on each of the sampled particles to obtain a coded particle set
  • Step 4 setting an initial population according to the encoded particle set
  • Step 5 The initial population is used as the original experimental population, and the Lamarck rewriting operation, the real decoding operation, and the elite retention operation are sequentially performed; the Lamarck rewriting operation refers to the ratio of the fitness of the candidate particles according to the two parents.
  • the real encoding operation is to convert the particle set obtained by the Lamarck rewriting operation into a set of particles in a real form
  • the elite reservation operation is to maximize the weight of the candidate particles in each iteration The particles are compared with the weights of the previous generation's largest particles, and the particles with larger weights are selected, and the particles with the smallest weight and their floating point numbers are replaced by the particles with the largest weight and their floating point format. , generating a new generation of populations and using the new generation of populations as the original experimental population;
  • Step 6 Repeat step 5 until the iteration termination condition is reached; when the termination, the particle set in the form of the optimal real number is obtained;
  • Step 7 The particle of the optimal real form is used as the prediction sample of the next moment, and the process proceeds to step 2 until the system termination condition is reached, and the state estimation value of the system is obtained.
  • the invention also provides a global optimal particle filtering method and a global optimal particle filter, comprising:
  • An initial particle set generation module for generating an initial particle set
  • a sampling module configured to perform importance sampling on the initial particle set by using an insensitive Kalman filter to obtain sampled particles
  • a floating point number encoding module which performs floating point number encoding on each of the sampled particles to obtain a coded particle set
  • An initial population setting module configured to set an initial population according to the encoded particle set
  • a Lamarck rewriting module for using the initial population as an original experimental population, performing a Lamarck rewriting operation, a real decoding operation, and an elite retention operation in sequence;
  • the Lamarck rewriting operation refers to two parent candidate particles.
  • the ratio of fitness is passed directly to the more adaptive parent code to the children of the less-adapted parent, replacing the corresponding bits of the floating-point number, and retaining the more adaptive parent particle as its child.
  • the real decoding operation converts the particle set obtained by the Lamarck rewriting operation into a particle set in a real form
  • the elite retention operation is a candidate for each iteration
  • the particle with the largest weight in the particle concentration is compared with the weight of the particle with the largest weight of the previous generation, and the particle with larger weight is selected, and the particle with the smallest weight and its floating point number format are replaced with the particle with the largest weight. And its floating point format, resulting in a new generation of populations, and using the new generation of populations as the original experimental population;
  • An iterative control module for causing the Lamarck rewrite module to iteratively perform iteratively until an iteration termination condition is reached; at the end, the particle set in the form of an optimal real number is obtained;
  • the state estimation value determining module is configured to use the particle of the optimal real form as the prediction sample of the next moment, and send the sampling signal to the sampling module until the system termination condition is reached, and the state estimation value of the system is obtained.
  • the re-sampling method based on Lamarckian genetics is designed to replace the traditional resampling method.
  • the particle set and increasing the particle diversity the particle depletion problem in the traditional particle filtering algorithm is avoided, and the purpose of improving the filtering estimation accuracy is achieved.
  • the method makes maximum use of the particle's own information, improves the particle utilization rate, reduces the number of particles used and the running time of the algorithm, and optimizes the sampling process with simple structure, less control parameters and lower computational complexity.
  • the present invention can be more widely applied to various fields, including 1) nonlinear filtering, cluster analysis, pattern recognition, image processing, etc. required for complex systems, and can organize complicated things with disordered surfaces. 2)
  • the nonlinear filtering, self-adaptation, self-learning and self-organizing intelligent behavior required by the automatic control system can adapt to environmental changes, reduce fluctuations, ensure high precision, and ensure real-time and rapid execution;
  • the design of the hardware and program does not need to tell the computer exactly how to do it, but it is automatically completed by the computer; 4)
  • the system and the artificial neural network are combined to solve problems such as noisy machine learning and machine reading.
  • the invention has high accuracy and stability in various tests of the nonlinear target tracking model.
  • FIG. 1 is a flow chart of a global optimal particle filtering method according to the present invention.
  • FIG. 2 is a schematic diagram of an embodiment of floating point number encoding of the present invention
  • FIG. 3 is a flow chart of an embodiment of step 5 of the present invention.
  • step 5 of the present invention is a schematic diagram of a rewriting operation in step 5 of the present invention.
  • 5 is a schematic diagram showing a comparison curve of the root mean square error mean value of the method of the present invention and other particle filtering algorithms when the number of particles is 10;
  • Figure 6 is a block schematic diagram of a global optimal particle filter of the present invention.
  • the global optimal particle filtering method provided by the invention uses the particle to describe the state space of the dynamic system, and the state space model of the nonlinear dynamic system is:
  • x k ⁇ R n is the n-dimensional system state vector at time k
  • z k ⁇ R m is the m-dimensional measurement vector at time k
  • the system state transition map and the measurement map are f k-1 ( ⁇ ): R n ⁇ R n ⁇ R n and h k ( ⁇ ): R m ⁇ R m ⁇ R m
  • the process noise and measurement noise of the system are u k-1 ⁇ R n and v k ⁇ R m , respectively .
  • the insensitive Kalman filter algorithm is used to generate the importance function, and it is sampled to obtain the sampled particles.
  • the Lamarck genetic resampling method is used to replace the traditional resampling process, and the Lamarck genetic rewriting operation and elite retention operation are performed.
  • the sampled particles are optimized and propagated; finally, the optimal particle point set is obtained, and the target estimation result is given.
  • the method of the present invention performs floating point number encoding on particles, and treats the particle set in the floating point number format as a population, and the particles in each floating point format are encoded as a chromosome of the population, and each floating point value of the particle is used as a decimal of the chromosome. gene.
  • the specific recursive process includes the following operations:
  • the non-linear dynamic system signal processing system of this embodiment includes the following steps:
  • Step 1 Generate an initial set of particles. Specifically, the initial particle set is generated according to the initial suggested distribution p(x 0 ). Where N is the total number of particles and i is the number of the particles.
  • Step 2 Using the insensitive Kalman filter to sample the importance of the initial particle set to obtain sampled particles Where k denotes that the sampled particle is a sample at time k.
  • Step 3 Perform floating-point number coding on each of the sampled particles to obtain a coded particle set.
  • the encoding method of this floating point number can use the encoding method in references [1]-[3].
  • Step 4 Set an initial population according to the encoded particle set.
  • Step 5 The initial population is used as the original experimental population, and the Lamarck rewriting operation, the real decoding operation, and the elite retention operation are sequentially performed; the Lamarck rewriting operation refers to the ratio of the fitness of the candidate particles according to the two parents.
  • the real decoding operation is to convert a set of floating point format particles obtained by a Lamarck rewriting operation into a set of particles in a real form
  • the elite reservation operation is a weight concentration of particles selected in each iteration The largest particle, compared with the weight of the previous generation's largest particle, selects the particle with larger weight, and replaces the particle with the smallest weight and its floating point format with the particle with the largest weight and its floating point number.
  • the format produces a new generation of populations and uses the new generation of populations as the original experimental population.
  • Step 6 Step 5 is repeated until the iteration termination condition is reached; when terminated, the particle set in the form of the best real number is obtained. Specifically, the new generation floating point format particle set is used as the original experimental population of the rewriting operation, and step 5 is iteratively repeated until the termination condition is reached. Finally, the optimal real form particle set is obtained.
  • Step 7 The particle of the optimal real form is used as the prediction sample of the next moment, and the process proceeds to step 2 until the system termination condition is reached, and the state estimation value of the system is obtained.
  • Embodiment 2 This embodiment differs from Embodiment 1 in that:
  • the initial particle set in step 1 is The step 2 is specifically as follows:
  • Step 2.1 Calculate the initial particle set Mean And variance Get the recommended distribution of UKF Particle Satisfy
  • Step 2.2 Calculating the sampled particles Weight And normalized to get the normalized weight which is
  • Step 2.3 According to the particle And its weight Get sampled particles
  • Embodiment 3 This embodiment differs from the specific embodiment by one or two:
  • Step 3 is specifically: using a floating point number format with a fixed effective number of bits l to particle Expressed as Get the encoded particle set as among them A value indicating the 1st significant digit of the Nth particle.
  • Step 4 is specifically as follows:
  • the floating point number format particle set will be encoded after k As the first generation of the initial population of the entire optimization operation; the population size of the initial population N P is equal to the number of particles N; the floating point number format of each particle As a chromosome, each digit of the floating point value represents a decimal gene, each particle weight Represents the fitness function value for each chromosome.
  • Embodiment 5 This embodiment differs from one of the specific embodiments 1 to 4 in that:
  • Step 5 includes: Step 5A: Lamarck Rewrite Operation; Step 5B: Real Encoding Operation; and Step 5C: Elite Retention Operation.
  • Step 5A specifically includes:
  • Step 5A.2 Calculate the ratio of delivered genes p t , p t to satisfy
  • Step 5A.3 Select the right if they are rewritten weight particles not all zeros, then the weights with smaller weights floating-point format rewritable larger particles of the i 1 i 2 particles of the floating-point format Where the rewritten position is random and the number of rewrite positions is n t .
  • Step 5A.4 Repeat steps 5A.1 through 5A.3 until a predetermined number of times N P is reached to obtain a particle set in the final rewritten floating point format:
  • Step 5B specifically includes:
  • the real-number decoding is performed on the particle set of the floating point number format obtained in step 5A, and the rewritten real-formed particle set is obtained. among them:
  • Step 5C specifically includes:
  • Embodiment 8 This embodiment differs from one of the specific embodiments 1 to 7 in that:
  • step 5C the g-generation population is recorded as s i (g) g-th generation represents the best individual in the population, 1 ⁇ i ⁇ N s, N s is the size of the groups; new generation of the population
  • the optimal individual is s j (g+1)
  • the worst individual is s m (g+1), 1 ⁇ j ⁇ N s , 1 ⁇ m ⁇ N s :
  • the optimal individual s i (g) of the g- th generation is added to the new-generation population S(g+1) as a new-generation population S ( The Nth s +1 individuals of g+1), the new generation population S(g+1) removes the individuals with the least fitness, and the new generation population S(g+1) at this time is expressed as:
  • the optimal real particle set with equal weight is finally passed in step 7.
  • the system status is estimated as follows:
  • the population corresponds to the particle set, and the individuals in the population correspond to the particles in the particle group.
  • Embodiment 9 This embodiment provides a global optimal particle filter, as shown in FIG. 6, including:
  • a sampling module 12 configured to perform importance sampling on the initial particle set by using an insensitive Kalman filter to obtain sampled particles
  • the floating point number encoding module 13 performs floating point number encoding on each of the sampled particles to obtain a coded particle set.
  • An initial population setting module 14 configured to set an initial population according to the encoded particle set
  • the Lamarck rewriting module 15 is used to use the initial population as the original experimental population, and successively performs the Lamarck rewriting operation, the real decoding operation, and the elite retention operation; the Lamarck rewriting operation refers to the fitness of the candidate particles according to the two parents.
  • the ratio of the more adaptive parent code is directly passed to the children of the less-adapted parent, replacing the corresponding bits of the floating-point number, and retaining the more adaptive parent particle as its child particle, and finally
  • the rewritten particle set is obtained;
  • the real number decoding operation is to convert the particle set obtained by the Lamarck rewriting operation into a real set of particle sets;
  • the elite retention operation is to maximize the weight of the candidate particles in each iteration.
  • the iterative control module 16 is configured to cause the Lamarck rewriting module 15 to iteratively perform iteratively until an iterative termination condition is reached; when terminated, the particle set in the form of an optimal real number is obtained;
  • the state estimation value determining module 17 is configured to use the particle of the optimal real form as the prediction sample of the next time, and send the sampling signal to the sampling module 12 until the system termination condition is reached, and the state estimation value of the system is obtained.
  • This embodiment compares the present invention with several other particle filtering algorithms by state estimation of a nonlinear dynamic system.
  • the state space model of the system is as follows:
  • the process noise is v k to Gamma (3, 2)
  • the observed noise is n k to N (0, 0.00001).
  • Set the observation time to 70, the number of runs to 200, and the number of particles N to be 10, 100, and 200, respectively.
  • the mean square root error (RMSE) of the algorithm proposed by the present invention and other particle filtering algorithms is shown in FIG. 5.
  • the present invention has high accuracy and stability in the nonlinear target tracking model.
  • Tan K C Li Y. Performance-based control system design automation via evolutionary computing [J]. Engineering Applications of Artificial Intelligence, 2001, 14 (4): 473-486.

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Biophysics (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • Artificial Intelligence (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Computational Linguistics (AREA)
  • Biomedical Technology (AREA)
  • Molecular Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • General Health & Medical Sciences (AREA)
  • Evolutionary Biology (AREA)
  • Physiology (AREA)
  • Genetics & Genomics (AREA)
  • Probability & Statistics with Applications (AREA)
  • Computer Hardware Design (AREA)
  • Algebra (AREA)
  • Computational Mathematics (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Pure & Applied Mathematics (AREA)
  • Complex Calculations (AREA)
  • Feedback Control In General (AREA)

Abstract

一种全局最优粒子滤波方法及全局最优粒子滤波器，属于信号处理领域，克服了现有的粒子滤波器会造成采样样本与真实后验概率密度样本存在较大偏差的缺点，有效地解决了粒子滤波处理非线性和非高斯信号的问题。主要技术手段是利用拉马克遗传自然法则，构造了一种全局最优的粒子滤波器，包括：产生初始粒子集；使用不敏卡尔曼滤波对初始粒子集进行重要性采样，得到采样粒子；对每一个采样粒子进行浮点数编码，得到编码后的粒子集；设置初始种群；将初始种群作为原始试验种群，依次进行拉马克重写操作、实数解码操作以及精英保留操作；将实数形式的最优候选粒子作为下一时刻的预测样本，得到系统的状态估计值。适用于机器学习。

Description

一种全局最优粒子滤波方法及全局最优粒子滤波器 技术领域
本发明涉及一种全局最优粒子滤波方法及全局最优粒子滤波器，属于信号处理领域。
背景技术
动态系统的状态估计问题涉及很多领域，尤其是信号处理、人工智能和图像处理领域，且其在导航与制导、信息融合、自动控制、金融分析、智能监控等领域也具有重要应用价值。传统的卡尔曼滤波只适用于线性高斯系统，而扩展卡尔曼滤波也只能应对系统的弱非线性。因此，不受系统模型特性和噪声分布限制的粒子滤波，在非线性、非高斯动态系统的滤波问题中备受关注。
粒子滤波是一种基于蒙特卡洛模拟和递推贝叶斯估计的滤波方法。其基本原理就是通过寻找一组在状态空间中传播的随机样本，即“粒子”，对后验概率密度函数进行近似，以样本均值代替积分运算，从而获得状态最小方差估计的过程。常见的粒子滤波算法包括基本粒子滤波(PF)、辅助粒子滤波(APF)、正则化粒子滤波(RPF)。
粒子滤波算法的性能受自身粒子退化和粒子贫化两大问题限制。粒子退化是指随着迭代次数增加，粒子集中除了少数粒子具有较大权值以外，其余粒子的权值均可以忽略不计。粒子贫化是指经过重采样后，大权值粒子被多次赋值，粒子集的多样性丧失。解决这两个问题的关键技术就是建议分布的选取和改进重采样算法。
近些年来，研究者尝试采用智能优化算法，如遗传算法、粒子群优化算法、蚁群算法和人工鱼群算法等，通过优化搜索并保留能够反映系统概率密度函数的粒子，以达到改善粒子分布，提高粒子滤波性能的目的。目前对智能优化粒子滤波的研究还存在一些不足之处。一方面，现有的研究方法没有考虑到系统状态的最新观测值，造成采样样本与真实后验概率密度样本存在较大偏差。另一方面，目前提出的智能优化算法在控制粒子的多样性，以及寻优过程的全局引导能力上，尚有不足，且都增加了粒子滤波的复杂度以及计算量，影响了优化速度。围绕上述两个问题，本发明采用了不敏卡尔曼滤波(Unscented Kalman Filter,UKF)算法作为重要性密度函数，由此利用拉马克遗传自然法则，构造了一种全局最优的粒子滤波器。
发明内容
本发明的目的是为了解决现有的粒子滤波算法会造成采样样本与真实后验概率密度样本存在较大偏差，并且在控制粒子多样性以及寻优过程引导能力不足，会增加粒子滤波 的复杂度以及计算量，以及现有的粒子退化和粒子贫化造成无法有效处理非线性和非高斯信号的问题的缺点，而构造的一种全局最优粒子滤波方法及全局最优粒子滤波器。
一种全局最优粒子滤波方法，其特征在于，包括如下步骤：
步骤1：产生初始粒子集；
步骤2：使用不敏卡尔曼滤波对所述初始粒子集进行重要性采样，得到采样粒子；
步骤3：对所述的每一个采样粒子进行浮点数编码，得到编码后的粒子集；
步骤4：根据所述编码后的粒子集设置初始种群；
步骤5：将所述初始种群作为原始试验种群，依次进行拉马克重写操作、实数解码操作以及精英保留操作；所述拉马克重写操作是指根据两个父代候选粒子的适应度之比将适应度更大的父代编码直接传递给适应度较小父代的子代，对其浮点数相应位进行替换，并且保留适应度更大的父代粒子作为其子代粒子，最终得到重写后的粒子集；所述实数编码操作是将拉马克重写操作得到的粒子集编码转换为实数形式的粒子集；所述精英保留操作是将每次迭代选出的候选粒子集中权值最大的粒子，与上一代权值最大的粒子进行权值比较，选出权值更大的粒子，并把权值最小的粒子及其浮点数格式均替换成权值最大的粒子及其浮点数格式，产生新一代的种群，并将所述新一代的种群作为原始试验种群；
步骤6：反复执行步骤五，直至达到迭代终止条件；终止时得到最优实数形式的粒子集；
步骤7：将所述最优实数形式的粒子作为下一时刻的预测样本，转至步骤二，直至达到系统终止条件，得到系统的状态估计值。
本发明还提供一种全局最优粒子滤波方法及全局最优粒子滤波器，包括：
初始粒子集产生模块，用于产生初始粒子集；
采样模块，用于使用不敏卡尔曼滤波对所述初始粒子集进行重要性采样，得到采样粒子；
浮点数编码模块，对所述的每一个采样粒子进行浮点数编码，得到编码后的粒子集；
初始种群设置模块，用于根据所述编码后的粒子集设置初始种群；
拉马克重写模块，用于将所述初始种群作为原始试验种群，依次进行拉马克重写操作、实数解码操作以及精英保留操作；所述拉马克重写操作是指根据两个父代候选粒子的适应度之比将适应度更大的父代编码直接传递给适应度较小父代的子代，对其浮点数相应位进行替换，并且保留适应度更大的父代粒子作为其子代粒子，最终得到重写后的粒子集；所述实数解码操作是将拉马克重写操作得到的粒子集编码转换为实数形式的粒子集；所述精 英保留操作是将每次迭代选出的候选粒子集中权值最大的粒子，与上一代权值最大的粒子进行权值比较，选出权值更大的粒子，并把权值最小的粒子及其浮点数格式均替换成权值最大的粒子及其浮点数格式，产生新一代的种群，并将所述新一代的种群作为原始试验种群；
迭代控制模块，用于使拉马克重写模块反复迭代执行，直至达到迭代终止条件；终止时得到最优实数形式的粒子集；
状态估计值确定模块，用于将所述最优实数形式的粒子作为下一时刻的预测样本，向采样模块发送采样信号，直至达到系统终止条件，得到系统的状态估计值。
本发明的技术效果为：
1、设计了基于拉马克遗传的重采样方法来代替了传统重采样方法，通过优化粒子集，增加粒子多样性，避免了传统粒子滤波算法中的粒子贫化问题，达到提高滤波估计精度的目的。同时该方法最大限度地利用了粒子自身的信息，提高粒子利用率，减少了采用粒子数目和算法运行时间，且优化采样过程结构简单，控制参数少，计算复杂度较低。
2、采用不敏卡尔曼滤波产生重要性密度函数，融入更多的最新观测信息，提高了所产生预测粒子的精度和稳定性，从而有效避免了粒子退化问题，在观测噪声较大的环境下具有更好的状态估计精度。
3、融合不敏卡尔曼滤波，利用拉马克遗传自然法则，提高了寻优过程的全局引导能力上，减少了粒子滤波的复杂度和计算量，构造了一种快速全局最优的粒子滤波器。
4、本发明还能更广泛地应用于各种不同的领域，包括1)复杂系统所需的非线性滤波、聚类分析、模式识别、图像处理等工作，可将表面杂乱无章的复杂事物条理化；2)自动控制系统所需的非线性滤波、自适应性、自学习和自组织的智能行为，能够适应环境变化，减少波动，保证高的精度，保证执行的实时性和快速性；3)硬件和程序的设计，不必精确地告诉计算机具体怎样去做，而由计算机自动完成；4)综合应用，和其他技术相结合，各自发挥特长，综合解决问题。例如，将本系统和人工神经网络相结合，解决有噪音的机器学习、机器阅读等问题。
4、在非线性目标跟踪模型的各项测试中，本发明具有较高的准确性和稳定性。
附图说明
图1为本发明的一种全局最优粒子滤波方法的流程图；
图2为本发明的浮点数编码的一个实施例的示意图；
图3为本发明的步骤5的一个实施例的流程图；
图4为本发明的步骤5中重写操作的示意图；
图5为本发明的方法与其他粒子滤波算法在粒子数为10时的均方根误差均值对比曲线示意图；
图6为本发明的一种全局最优粒子滤波器的方框示意图。
具体实施方式
具体实施方式一：
本发明提供的全局最优粒子滤波方法，采用粒子描述动态系统的状态空间，设该非线性动态系统的状态空间模型为：
x k＝f k-1(x k-1,u k-1)
z k＝h k(x k,v k)
其中，x k∈R n是k时刻的n维系统状态向量，z k∈R m是k时刻的m维量测向量；系统状态转移映射和量测映射分别是f k-1(×):R n×R n→R n和h k(·):R m×R m→R m；系统的过程噪声和量测噪声分别是u k-1∈R n和v k∈R m。
需要说明的是，非线性系统的状态空间模型的表示形式与上述公式是对等的，即本领域的技术人员能够想到非线性系统的公式表示是如上式所示的。
首先采用不敏卡尔曼滤波算法来产生重要性函数，并对其进行采样，得到采样粒子；然后，利用拉马克遗传重采样方法代替传统重采样过程，通过拉马克遗传重写操作和精英保留操作对采样粒子进行优化繁殖；最后求得最优的粒子点集，给出目标估计结果。本发明的方法对粒子进行了浮点数编码，把浮点数格式的粒子集看作一个种群，每个浮点数格式的粒子作为种群的一个染色体编码，粒子每一位浮点数值作为染色体的一个十进制基因。具体递推过程包括以下操作：
本实施方式的非线性动态系统信号处理系统，如图1所示，包括如下步骤：
步骤1：产生初始粒子集。具体可以为，依据初始建议分布p(x 0)，产生初始粒子集
Figure PCTCN2018075151-appb-000001
其中N为粒子的总数量，i为粒子的序号。
步骤2：使用不敏卡尔曼滤波对所述初始粒子集进行重要性采样，得到采样粒子
Figure PCTCN2018075151-appb-000002
其中k表示此采样粒子为k时刻下的采样。
步骤3：对所述的每一个采样粒子进行浮点数编码，得到编码后的粒子集。这个浮点 数的编码方式可以使用参考文献[1]-[3]中的编码方式。
步骤4：根据所述编码后的粒子集设置初始种群。
步骤5：将所述初始种群作为原始试验种群，依次进行拉马克重写操作、实数解码操作以及精英保留操作；所述拉马克重写操作是指根据两个父代候选粒子的适应度之比将适应度更大的父代编码直接传递给适应度较小父代的子代，对其浮点数相应位进行替换，并且保留适应度更大的父代粒子作为其子代粒子，最终得到重写后的粒子集；所述实数解码操作是将拉马克重写操作得到的浮点数格式粒子集转换为实数形式的粒子集；所述精英保留操作是将每次迭代选出的粒子集中权值最大的粒子，与上一代权值最大的粒子进行权值比较，选出权值更大的粒子，并把权值最小的粒子及其浮点数格式均替换成权值最大的粒子及其浮点数格式，产生新一代的种群，并将所述新一代的种群作为原始试验种群。
步骤6：反复执行步骤五，直至达到迭代终止条件；终止时得到最优实数形式的粒子集。具体可以为，把新一代浮点数格式粒子集作为重写操作的原始试验种群，反复迭代步骤5，直到达到终止条件。最终获得最优实数形式粒子集。
步骤7：将所述最优实数形式的粒子作为下一时刻的预测样本，转至步骤二，直至达到系统终止条件，得到系统的状态估计值。
具体实施方式二：本实施方式与具体实施方式一不同的是：
步骤1中的初始粒子集为
Figure PCTCN2018075151-appb-000003
其特征在于，所述步骤2具体为：
步骤2.1：计算所述初始粒子集
Figure PCTCN2018075151-appb-000004
的均值
Figure PCTCN2018075151-appb-000005
和方差
Figure PCTCN2018075151-appb-000006
获得UKF的建议分布
Figure PCTCN2018075151-appb-000007
其中粒子
Figure PCTCN2018075151-appb-000008
满足
Figure PCTCN2018075151-appb-000009
步骤2.2：计算所述采样粒子
Figure PCTCN2018075151-appb-000010
的权值
Figure PCTCN2018075151-appb-000011
并归一化得到归一化后的权值
Figure PCTCN2018075151-appb-000012
即
Figure PCTCN2018075151-appb-000013
步骤2.3：根据粒子
Figure PCTCN2018075151-appb-000014
及其权值
Figure PCTCN2018075151-appb-000015
得到采样粒子
Figure PCTCN2018075151-appb-000016
其它步骤及参数与具体实施方式一相同。
具体实施方式三：本实施方式与具体实施方式一或二不同的是：
步骤3具体为：使用固定有效位数l的浮点数格式将粒子
Figure PCTCN2018075151-appb-000017
表示为
Figure PCTCN2018075151-appb-000018
得到编码后的粒子集为
Figure PCTCN2018075151-appb-000019
其 中
Figure PCTCN2018075151-appb-000020
表示第N个粒子第l个有效位数的数值。
浮点数值的第一位
Figure PCTCN2018075151-appb-000021
代表符号位，“1”代表正数，“0”代表负数。固定有效位数l通过预先滤波范围来设定，这里需注意Matlab里精度为小数点后面4位，且如果位数不满l的，最高位补0。例如，k时刻第i个粒子状态值为15.6745，且l＝7，那么它的浮点数格式如图2所示。
其它步骤及参数与具体实施方式一或二相同。
具体实施方式四：本实施方式与具体实施方式一至三之一不同的是：
步骤4具体为：
将编码后k时刻的浮点数格式粒子集
Figure PCTCN2018075151-appb-000022
当作整个优化操作的第一代初始种群；初始种群的种群大小N P等于粒子数N；每个粒子的浮点数格式
Figure PCTCN2018075151-appb-000023
作为一个染色体，浮点数值的每一位表示一个十进制基因，每个粒子权值
Figure PCTCN2018075151-appb-000024
表示每个染色体的适应度函数值。
其它步骤及参数与具体实施方式一至三之一相同。
具体实施方式五：本实施方式与具体实施方式一至四之一不同的是：
步骤5包括：步骤5A：拉马克重写操作；步骤5B：实数编码操作；以及步骤5C：精英保留操作。其中步骤5A具体包括：
步骤5A.1：根据重写概率h选择重写粒子，h∈(0,1]；随机产生一个(0,1]之间的随机数r，如果r＜h，则选取重写粒子，否则进入下次循环；设第i 1个粒子及其浮点数格式
Figure PCTCN2018075151-appb-000025
和第i 2个粒子及其浮点数格式
Figure PCTCN2018075151-appb-000026
为被选取的重写粒子，且满足
Figure PCTCN2018075151-appb-000027
例如取l＝6，该轮循环进行选取步骤，且选择第i 1和第i 2个粒子的浮点数格式，即
Figure PCTCN2018075151-appb-000028
和
Figure PCTCN2018075151-appb-000029
权值关系为
Figure PCTCN2018075151-appb-000030
步骤5A.2：计算传递基因比例p t，p t满足
Figure PCTCN2018075151-appb-000031
并根据下式计算传递基因的数目n t：
n t＝l×p t＝4；
步骤5A.3：若被选取重写的粒子的权重不全为0，则用权值较大的第i 1个粒子的浮点数格式重写权值较小的第i 2个粒子的浮点数格式，其中重写的位置是随机的，重写位置的数量为n t。
例如，取l＝6时，第i 2个粒子的浮点数格式被第i 1个粒子的浮点数格式重写，这里重写基因的位置是随机的，选取第1位、第3位、第5位、第6位，本发明算法中一次重写操作举例如图4所示。则重写后种群中的第i 1个粒子的浮点数格式不变，第i 2个粒子的浮点数格式为
Figure PCTCN2018075151-appb-000032
这样经过重写操作，小权值的粒子就被大权值的粒子修正。如果被选择的两个粒子的权重都为0，则该过程不被执行。
步骤5A.4：重复执行步骤5A.1至步骤5A.3，直到达到预定的次数N P，得到最终重写后的浮点数格式的粒子集：
Figure PCTCN2018075151-appb-000033
其它步骤及参数与具体实施方式一至四之一相同。
具体实施方式六：本实施方式与具体实施方式一至五之一不同的是：
步骤5B具体包括：
对步骤5A得到的浮点数格式的粒子集进行实数解码，得到重写后的实数形式的粒子集
Figure PCTCN2018075151-appb-000034
其中：
Figure PCTCN2018075151-appb-000035
并根据所述实数形式的粒子得到相应粒子的归一化权重
Figure PCTCN2018075151-appb-000036
Figure PCTCN2018075151-appb-000037
为重写后的粒子，
Figure PCTCN2018075151-appb-000038
为第i个粒子第l个有效位数经过重写后的数值。
其它步骤及参数与具体实施方式一至五之一相同。
具体实施方式七：本实施方式与具体实施方式一至六之一不同的是：
步骤5C具体包括：
选出本次迭代中实数形式的粒子集中权值最大的粒子，再与上一次迭代中权值最大的粒子进行权值比较，选出权值更大的粒子，并将权值最小的粒子及其浮点数格式都替换成权值最大的粒子及其浮点数格式，产生新一代的种群。
其它步骤及参数与具体实施方式一至六之一相同。
具体实施方式八：本实施方式与具体实施方式一至七之一不同的是：
步骤5C中，将第g代种群记为
Figure PCTCN2018075151-appb-000039
s i(g)表示第g代种群中的最优个体，1≤i≤N s，N s为群体的大小；新一代种群为
Figure PCTCN2018075151-appb-000040
其最优个体为s j(g+1)，最差个体为s m(g+1)，1≤j≤N s，1≤m≤N s：
若s i(g)优于s j(g+1)，则将第g代种群的最优个体s i(g)加入到新一代种群S(g+1)中，作为新一代种群S(g+1)的第N s+1个个体，新一代种群S(g+1)移除适应度最小的个体，此时的新一代种群S(g+1)表示为：
Figure PCTCN2018075151-appb-000041
若s i(g)不优于s j(g+1)，则新一代种群S(g+1)不变，得到新一代的粒子集
Figure PCTCN2018075151-appb-000042
和其浮点数格式粒子集为
Figure PCTCN2018075151-appb-000043
根据上述执行步骤，最终在步骤七中过相等权重的最优的实数粒子集
Figure PCTCN2018075151-appb-000044
系统状态估计如下：
Figure PCTCN2018075151-appb-000045
并把产生的最优粒子集作为下一时刻k+1的预测样本，转到步骤2。
需要说明的是，种群对应于粒子集，种群中的个体对应于粒子群中的粒子。
具体实施方式九：本实施方式提供一种全局最优粒子滤波器，如图6所示，包括：
初始粒子集产生模块11，用于产生初始粒子集；
采样模块12，用于使用不敏卡尔曼滤波对所述初始粒子集进行重要性采样，得到采样粒子；
浮点数编码模块13，对所述的每一个采样粒子进行浮点数编码，得到编码后的粒子集；
初始种群设置模块14，用于根据所述编码后的粒子集设置初始种群；
拉马克重写模块15，用于将初始种群作为原始试验种群，依次进行拉马克重写操作、实数解码操作以及精英保留操作；拉马克重写操作是指根据两个父代候选粒子的适应度之比将适应度更大的父代编码直接传递给适应度较小父代的子代，对其浮点数相应位进行替换，并且保留适应度更大的父代粒子作为其子代粒子，最终得到重写后的粒子集；实数解码操作是将拉马克重写操作得到的粒子集编码转换为实数形式的粒子集；精英保留操作是将每次迭代选出的候选粒子集中权值最大的粒子，与上一代权值最大的粒子进行权值比较，选出权值更大的粒子，并把权值最小的粒子及其浮点数格式均替换成权值最大的粒子及其浮点数格式，产生新一代的种群，并将新一代的种群作为原始试验种群；
迭代控制模块16，用于使拉马克重写模块15反复迭代执行，直至达到迭代终止条件；终止时得到最优实数形式的粒子集；
状态估计值确定模块17，用于将最优实数形式的粒子作为下一时刻的预测样本，向采样模块12发送采样信号，直至达到系统终止条件，得到系统的状态估计值。
本实施方式与具体实施方式一是完全对应的，对于原理此处不做详述。
<实施例1>
本实施例通过一个非线性动态系统的状态估计对本发明和其它几种粒子滤波算法进行比较。系统的状态空间模型如下：
x k+1＝1+sin(0.04πk)+0.5x k+v k
Figure PCTCN2018075151-appb-000046
其中，过程噪声v k～Gamma(3,2)，观测噪声n k～N(0,0.00001)。设定观测时间为70，运行次数为200，粒子数N分别取10、100和200。本发明的算法的参数设置为：G＝20，h＝0.9，l＝6。在粒子数N取10时，本发明提出的算法与其他粒子滤波算法所产生的均方根误差(RMSE)均值如图5所示。
从图5可以看出，本发明算法在相同粒子数目下的RMSE均值明显优于其它算法。
对实施例分别在粒子数目N＝10，N＝100，N＝500的情况下进行200次蒙特卡罗实验，统计RMSE均值、RMSE方差和平均运行时间，结果如表1、表2和表3所示：
表1：N为10的试验结果
Figure PCTCN2018075151-appb-000047
Figure PCTCN2018075151-appb-000048
表2：N为100的试验结果
Figure PCTCN2018075151-appb-000049
表3：N为200的试验结果
Figure PCTCN2018075151-appb-000050
由表1、表2和表3的统计数据可以看出：
1)在粒子数相同时，虽然本发明比其它粒子滤波方法运行时间长，但是跟踪精度比其它粒子滤波方法高很多；尤其粒子数为100和200时，本发明的跟踪均方根误差均值要远小于其它粒子滤波方法。
2)在粒子数为200时，其它三种粒子滤波方法的跟踪性能都不如本发明，也就是说本发明粒子利用率高于其它粒子滤波方法。
3)在用时相当的情况下本发明的跟踪精度远优于其它粒子滤波算法。
4)在不同粒子数时，本发明的跟踪均方根误差的方差都小于其它粒子滤波算法，说明本发明有更好的稳定性。
综上可以得出，本发明在非线性目标跟踪模型中具有较高的准确性和稳定性。
参考文献：
[1]Tan K C,Li Y.Evolutionary L∞identification and model reduction for robust control[J].Journal of Systems and Control Engineering,2000,214(3):231-238.
[2]Tan K C,Li Y.Performance-based control system design automation via evolutionary computing[J].Engineering Applications of Artificial Intelligence,2001,14(4):473-486.
[3]Li Y.,K.C.Tan and M.Gong.Model Reduction in Control Systems by Means of Global Structure Evolution and Local Parameter Learning.In Evolutionary Algorithms in Engineering Applications,Springer-Verlag,Berlin,Germany,1996.
本发明还可有其它多种实施例，在不背离本发明精神及其实质的情况下，本领域技术人员当可根据本发明作出各种相应的改变和变形，但这些相应的改变和变形都应属于本发明所附的权利要求的保护范围。

Claims (9)

  1. 一种全局最优粒子滤波方法，其特征在于，包括如下步骤：
    步骤1：产生初始粒子集；
    步骤2：使用不敏卡尔曼滤波对所述初始粒子集进行重要性采样，得到采样粒子；
    步骤3：对所述的每一个采样粒子进行浮点数编码，得到编码后的粒子集；
    步骤4：根据所述编码后的粒子集设置初始种群；
    步骤5：将所述初始种群作为原始试验种群，依次进行拉马克重写操作、实数解码操作以及精英保留操作；所述拉马克重写操作是指根据两个父代候选粒子的适应度之比将适应度更大的父代编码直接传递给适应度较小父代的子代，对其浮点数相应位进行替换，并且保留适应度更大的父代粒子作为其子代粒子，最终得到重写后的粒子集；所述实数解码操作是将拉马克重写操作得到的粒子集编码转换为实数形式的粒子集；所述精英保留操作是将每次迭代选出的候选粒子集中权值最大的粒子，与上一代权值最大的粒子进行权值比较，选出权值更大的粒子，并把权值最小的粒子及其浮点数格式均替换成权值最大的粒子及其浮点数格式，产生新一代的种群，并将所述新一代的种群作为原始试验种群；
    步骤6：反复执行步骤五，直至达到迭代终止条件；终止时得到最优实数形式的粒子集；
    步骤7：将所述最优实数形式的粒子作为下一时刻的预测样本，转至步骤二，直至达到系统终止条件，得到系统的状态估计值。
  2. 根据权利要求1所述的方法，所述步骤1中的初始粒子集为
    Figure PCTCN2018075151-appb-100001
    其特征在于，所述步骤2具体为：
    步骤2.1：计算所述初始粒子集
    Figure PCTCN2018075151-appb-100002
    的均值
    Figure PCTCN2018075151-appb-100003
    和方差
    Figure PCTCN2018075151-appb-100004
    获得UKF的建议分布
    Figure PCTCN2018075151-appb-100005
    其中粒子
    Figure PCTCN2018075151-appb-100006
    满足
    Figure PCTCN2018075151-appb-100007
    步骤2.2：计算所述采样粒子
    Figure PCTCN2018075151-appb-100008
    的权值
    Figure PCTCN2018075151-appb-100009
    并归一化得到归一化后的权值
    Figure PCTCN2018075151-appb-100010
    即
    Figure PCTCN2018075151-appb-100011
    步骤2.3：根据粒子
    Figure PCTCN2018075151-appb-100012
    及其权值
    Figure PCTCN2018075151-appb-100013
    得到采样粒子
    Figure PCTCN2018075151-appb-100014
  3. 根据权利要求1或2所述的方法，其特征在于，所述步骤3具体为：所述使用固定有效位数l的浮点数格式将粒子
    Figure PCTCN2018075151-appb-100015
    表示为
    Figure PCTCN2018075151-appb-100016
    得到编码后的粒子集为
    Figure PCTCN2018075151-appb-100017
    其中
    Figure PCTCN2018075151-appb-100018
    表示第N个粒子第l个有效位数的数值。
  4. 根据权利要求3所述的方法，其特征在于，所述步骤4具体为：
    将编码后k时刻的浮点数格式粒子集
    Figure PCTCN2018075151-appb-100019
    当作整个优化操作的第一代初始种群；所述初始种群的种群大小N P等于粒子数N；每个粒子的浮点数格式
    Figure PCTCN2018075151-appb-100020
    作为一个个体，浮点数值的每一位表示一个十进制基因，每个粒子权值
    Figure PCTCN2018075151-appb-100021
    表示每个染色体的适应度函数值。
  5. 根据权利要求4所述的方法，步骤5包括：
    步骤5A：拉马克重写操作；
    步骤5B：实数解码操作；以及
    步骤5C：精英保留操作；
    其特征在于，所述步骤5A具体包括：
    步骤5A.1：对于每一个粒子，根据生成的重写概率h确定是否将该粒子选择为重写粒子，h∈(0,1]；随机产生一个(0,1]之间的随机数r，如果r＜h，则选取重写粒子，否则不选取；设第i 1个粒子及其浮点数格式
    Figure PCTCN2018075151-appb-100022
    和第i 2个粒子及其浮点数格式
    Figure PCTCN2018075151-appb-100023
    为被选取的重写粒子，且满足
    Figure PCTCN2018075151-appb-100024
    步骤5A.2：计算传递基因比例p t，p t满足
    Figure PCTCN2018075151-appb-100025
    并根据下式计算传递基因的数目n t：
    n t＝l×p t；
    步骤5A.3：若被选取重写的粒子的权重不全为0，则用权值较大的第i 1个粒子的浮点数格式重写权值较小的第i 2个粒子的浮点数格式，其中重写的位置是随机的，重写位置的数量为n t；
    步骤5A.4：重复执行步骤5A.1至步骤5A.3，直到达到预定的次数N P，得到最终重写后的浮点数格式的粒子集：
    Figure PCTCN2018075151-appb-100026
  6. 根据权利要求5所述的方法，其特征在于，所述步骤5B具体包括：
    对步骤5A得到的浮点数格式的粒子集进行实数解码，得到重写后的实数形式的粒子集
    Figure PCTCN2018075151-appb-100027
    其中：
    Figure PCTCN2018075151-appb-100028
    并根据所述实数形式的粒子得到相应粒子的归一化权重
    Figure PCTCN2018075151-appb-100029
  7. 根据权利要求6所述的方法，其特征在于，所述步骤5C具体包括：
    选出本次迭代中实数形式的粒子集中权值最大的粒子，再与上一次迭代中权值最大的粒子进行权值比较，选出权值更大的粒子，并将权值最小的粒子及其浮点数格式都替换成权值最大的粒子及其浮点数格式，产生新一代的候选粒子集种群。
  8. 根据权利要求7所述的方法，所述步骤5C中，将第g代种群记为
    Figure PCTCN2018075151-appb-100030
    s i(g)表示第g代种群中的最优个体，1≤i≤N s，N s为群体的大小；新一代种群为
    Figure PCTCN2018075151-appb-100031
    其最优个体为s j(g+1)，最差个体为s m(g+1)，1≤j≤N s，1≤m≤N s，其特征在于：
    若s i(g)优于s j(g+1)，则将第g代种群的最优个体s i(g)加入到新一代种群S(g+1)中，作为新一代种群S(g+1)的第N s+1个个体，新一代种群S(g+1)移除适应度最小的个体，此时的新一代种群S(g+1)表示为：
    Figure PCTCN2018075151-appb-100032
    若s i(g)不优于s j(g+1)，则新一代种群S(g+1)不变，得到新一代的粒子集
    Figure PCTCN2018075151-appb-100033
    和其浮点数格式粒子集为
    Figure PCTCN2018075151-appb-100034
  9. 一种全局最优粒子滤波器，其特征在于，包括：
    初始粒子集产生模块，用于产生初始粒子集；
    采样模块，用于使用不敏卡尔曼滤波对所述初始粒子集进行重要性采样，得到采样粒 子；
    浮点数编码模块，对所述的每一个采样粒子进行浮点数编码，得到编码后的粒子集；
    初始种群设置模块，用于根据所述编码后的粒子集设置初始种群；
    拉马克重写模块，用于将所述初始种群作为原始试验种群，依次进行拉马克重写操作、实数解码操作以及精英保留操作；所述拉马克重写操作是指根据两个父代候选粒子的适应度之比将适应度更大的父代编码直接传递给适应度较小父代的子代，对其浮点数相应位进行替换，并且保留适应度更大的父代粒子作为其子代粒子，最终得到重写后的粒子集；所述实数解码操作是将拉马克重写操作得到的粒子集编码转换为实数形式的粒子集；所述精英保留操作是将每次迭代选出的候选粒子集中权值最大的粒子，与上一代权值最大的粒子进行权值比较，选出权值更大的粒子，并把权值最小的粒子及其浮点数格式均替换成权值最大的粒子及其浮点数格式，产生新一代的种群，并将所述新一代的种群作为原始试验种群；
    迭代控制模块，用于使拉马克重写模块反复迭代执行，直至达到迭代终止条件；终止时得到最优实数形式的粒子集；
    状态估计值确定模块，用于将所述最优实数形式的粒子作为下一时刻的预测样本，向采样模块发送采样信号，直至达到系统终止条件，得到系统的状态估计值。
PCT/CN2018/075151 2017-02-28 2018-02-02 一种全局最优粒子滤波方法及全局最优粒子滤波器 WO2018157699A1 (zh)

Priority Applications (1)

Application Number Priority Date Filing Date Title
US16/335,675 US20200034716A1 (en) 2017-02-28 2018-02-02 Global optimal particle filtering method and global optimal particle filter

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201710114832.XA CN106921366A (zh) 2017-02-28 2017-02-28 一种全局最优粒子滤波方法及全局最优粒子滤波器
CN201710114832.X 2017-02-28

Publications (1)

Publication Number Publication Date
WO2018157699A1 true WO2018157699A1 (zh) 2018-09-07

Family

ID=59453958

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2018/075151 WO2018157699A1 (zh) 2017-02-28 2018-02-02 一种全局最优粒子滤波方法及全局最优粒子滤波器

Country Status (3)

Country Link
US (1) US20200034716A1 (zh)
CN (1) CN106921366A (zh)
WO (1) WO2018157699A1 (zh)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113452349A (zh) * 2021-06-28 2021-09-28 中山大学 一种基于贝叶斯序贯重要性积分的卡尔曼滤波方法
CN113625309A (zh) * 2021-07-23 2021-11-09 哈尔滨工程大学 一种基于无迹差分粒子滤波算法实现的多径参数估计方法

Families Citing this family (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107527152B (zh) * 2017-08-31 2020-11-13 广东工业大学 一种基于缺件管理的舰船管件排程的方法与装置
CN108961235B (zh) * 2018-06-29 2021-05-14 山东大学 一种基于YOLOv3网络和粒子滤波算法的缺陷绝缘子识别方法
CN111931415A (zh) * 2019-07-18 2020-11-13 东莞理工学院 一种锂离子电池的基于全局最优粒子滤波的寿命预测方法
CN111859773B (zh) * 2020-08-05 2023-07-18 哈尔滨工程大学 基于正则化粒子滤波的电动闸阀故障确定方法及系统
CN112039496B (zh) * 2020-09-10 2024-04-19 南京航空航天大学 一种基于人工鱼群优化的快速高斯粒子滤波数据融合方法
CN112613222B (zh) * 2021-01-04 2023-09-15 重庆邮电大学 基于改进粒子滤波的倾斜探测电离层muf短期预报方法
CN115086229B (zh) * 2022-04-29 2023-07-11 珠海高凌信息科技股份有限公司 一种基于进化算法的sdn网络多路径计算方法

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101710384A (zh) * 2009-11-25 2010-05-19 北京航空航天大学 基于小生境遗传算法的改进粒子滤波方法
CN101807900A (zh) * 2010-03-10 2010-08-18 北京航空航天大学 基于并行遗传重采样的粒子滤波技术
CN101826852A (zh) * 2010-03-11 2010-09-08 哈尔滨工程大学 基于粒子群优化算法的无迹粒子滤波方法

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101710384A (zh) * 2009-11-25 2010-05-19 北京航空航天大学 基于小生境遗传算法的改进粒子滤波方法
CN101807900A (zh) * 2010-03-10 2010-08-18 北京航空航天大学 基于并行遗传重采样的粒子滤波技术
CN101826852A (zh) * 2010-03-11 2010-09-08 哈尔滨工程大学 基于粒子群优化算法的无迹粒子滤波方法

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
LI, LIN ET AL.: "Particle Filter with Lamarckian Inheritance for Nonlinear Filtering", 2016 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC, 21 November 2016 (2016-11-21), pages 2852 - 2857, XP033007043 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113452349A (zh) * 2021-06-28 2021-09-28 中山大学 一种基于贝叶斯序贯重要性积分的卡尔曼滤波方法
CN113452349B (zh) * 2021-06-28 2022-09-02 中山大学 一种基于贝叶斯序贯重要性积分的卡尔曼滤波方法
CN113625309A (zh) * 2021-07-23 2021-11-09 哈尔滨工程大学 一种基于无迹差分粒子滤波算法实现的多径参数估计方法
CN113625309B (zh) * 2021-07-23 2023-09-29 哈尔滨工程大学 一种基于无迹差分粒子滤波算法实现的多径参数估计方法

Also Published As

Publication number Publication date
CN106921366A (zh) 2017-07-04
US20200034716A1 (en) 2020-01-30

Similar Documents

Publication Publication Date Title
WO2018157699A1 (zh) 一种全局最优粒子滤波方法及全局最优粒子滤波器
Zhang et al. Efficient evolutionary search of attention convolutional networks via sampled training and node inheritance
WO2018161468A1 (zh) 一种基于拉马克获得性遗传原理的全局优化、搜索和机器学习方法
Singh et al. PI-LSTM: Physics-infused long short-term memory network
CN110910004A (zh) 一种多重不确定性的水库调度规则提取方法及系统
WO2022227400A1 (zh) 神经网络训练方法和装置、设备，及计算机存储介质
WO2022033258A1 (zh) 基于自动储层神经网络的多步预测未来风速的方法和系统
CN111008224B (zh) 一种基于深度多任务表示学习的时间序列分类和检索方法
CN112699998B (zh) 一种时间序列预测方法、装置、电子设备及可读存储介质
CN113705793B (zh) 决策变量确定方法及装置、电子设备和介质
CN111027672A (zh) 一种基于交互式多尺度循环神经网络的时间序列预测方法
WO2020019102A1 (en) Methods, systems, articles of manufacture and apparatus to train a neural network
CN112733273A (zh) 一种基于遗传算法和最大似然估计确定贝叶斯网络参数的方法
CN110335160B (zh) 一种基于分组和注意力改进Bi-GRU的就医迁移行为预测方法及系统
Xie et al. Automated design of CNN architecture based on efficient evolutionary search
CN114708479A (zh) 一种基于图结构和特征的自适应防御方法
CN113642727A (zh) 神经网络模型的训练方法和多媒体信息的处理方法、装置
WO2022166850A1 (zh) 基于量子线路的风险值估算方法、装置、介质及电子装置
JP7290183B2 (ja) データ処理の方法、ニューラルネットワークの学習方法及びデバイス
CN112329833B (zh) 一种基于球面嵌入的图像度量学习方法
CN115115920A (zh) 一种数据训练方法及装置
CN111402042B (zh) 一种用于股市大盘形态分析的数据分析与显示方法
Shi Comparison of Real Estate Price Prediction Based on LSTM and LGBM
US20240144064A1 (en) Pruning quantum computational results
CN113420561B (zh) 一种命名实体识别方法、装置、设备及存储介质

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 18761113

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 18761113

Country of ref document: EP

Kind code of ref document: A1