Vehicle recognition and tracking based on simulated annealing chaotic particle swarm optimization-Gauss particle filter algorithm

Target recognition and tracking is an important research filed in the surveillance industry.Traditional target recognition and tracking is to track moving objects, however, for the detected moving objects the specific content can not be determined.In this paper, a multi-target vehicle recognition and tracking algorithm based on YOLO v5 network architecture is proposed.The specific content of moving objects are identified by the network architecture, furthermore, the simulated annealing chaotic mechanism is embedded in particle swarm optimization-Gauss particle filter algorithm.The proposed simulated annealing chaotic particle swarm optimization-Gauss particle filter algorithm(SA-CPSO-GPF) is used to track moving objects.The experiment shows that the algorithm has a good tracking effect for the vehicle in the monitoring range.The root mean square error(RMSE), running time and accuracy of the proposed method are superior to traditional methods.The proposed algorithm has very good application value.

Immune adaptive Gaussian mixture particle filter for state estimation

The particle filter （PF） is a flexible and powerful sequen- tial Monte Carlo （SMC） technique capable of modeling nonlinear, non-Gaussian, and nonstationary dynamical systems. However, the generic PF suffers from particle degeneracy and sample im- poverishment, which greatly affects its performance for nonlinear, non-Gaussian tracking problems. To deal with those issues, an improved PF is proposed. The algorithm consists of a PF that uses an immune adaptive Gaussian mixture model （IAGM） based immune algorithm to re-approximate the posterior density. At the same time, three immune antibody operators are embed in the new filter. Instead of using a resample strategy, the newest obser- vation and conditional likelihood are integrated into those immune antibody operators to update the particles, which can further im- prove the diversity of particles, and drive particles toward their close local maximum of the posterior probability. The improved PF algorithm can produce a closed-form expression for the posterior state distribution. Simulation results show the proposed algorithm can maintain the effectiveness and diversity of particles and avoid sample impoverishment, and its performance is superior to several PFs and Kalman filters.

Gaussian particle filter based pose and motion estimation

Determination of relative three-dimensional (3D) position, orientation, and relative motion between two reference frames is an important problem in robotic guidance, manipulation, and assembly as well as in other fields such as photogrammetry. A solution to pose and motion estimation problem that uses two-dimensional (2D) intensity images from a single camera is de- sirable for real-time applications. The difficulty in performing this measurement is that the process of projecting 3D object features to 2D images is a nonlinear transformation. In this paper, the 3D transformation is modeled as a nonlinear stochastic system with the state estimation providing six degrees-of-freedom motion and position values, using line features in image plane as measuring inputs and dual quaternion to represent both rotation and translation in a unified notation. A filtering method called the Gaussian particle filter (GPF) based on the particle filtering concept is presented for 3D pose and motion estimation of a moving target from monocular image sequences. The method has been implemented with simulated data, and simulation results are provided along with comparisons to the extended Kalman filter (EKF) and the unscented Kalman filter (UKF) to show the relative advantages of the GPF. Simulation results showed that GPF is a superior alternative to EKF and UKF.

Bayesian target tracking based on particle filter

For being able to deal with the nonlinear or non-Gaussian problems, particle filters have been studied by many researchers. Based on particle filter, the extended Kalman filter （EKF） proposal function is applied to Bayesian target tracking. Markov chain Monte Carlo （MCMC） method, the resampling step, ere novel techniques are also introduced into Bayesian target tracking. And the simulation results confirm the improved particle filter with these techniques outperforms the basic one.

Improved Particle Filter for Passive Target Tracking

As a new method for dealing with any nonlinear or non-Ganssian distributions, based on the Monte Carlo methods and Bayesian filtering, particle filters （PF） are favored by researchers and widely applied in many fields. Based on particle filtering, an improved extended Kalman filter （EKF） proposal distribution is presented. Evaluation of the weights is simplified and other improved techniques including the residual resampling step and Markov Chain Monte Carlo method are introduced for target tracking. Performances of the EKF, basic PF and the improved PF are compared in target tracking examples. The simulation results confirm that the improved particle filter outperforms the others.

An evolutionary particle filter based EM algorithm and its application

In this paper, an evolutionary recursive Bayesian estimation algorithm is presented, which incorporates the latest observation with a new proposal distribution, and the posterior state density is represented by a Gaussian mixture model that is recovered from the weighted particle set of the measurement update step by means of a weighted expectation-maximization algorithm. This step replaces the resampling stage needed by most particle filters and relieves the effect caused by sample impoverishment. A nonlinear tracking problem shows that this new approach outperforms other related particle filters.

State Estimation for Sound Environment System with Nonlinear Observation Characteristics by Introducing Wide-Sense Particle Filter

In this study, a modified particle filter considering non-Gaussian properties of noises is proposed in a form applicable to real situation in sound environment system where the observation data are contaminated by the external noise (i.e., background noise) of arbitrary probability distribution and measured in decibel scale. More specifically, a nonlinear observation model in decibel scale with a quantized level is first paid considered by introducing the additive property of energy variables (i.e., sound intensity) in sound environment system. Next, a wide-sense particle filter of an expansion expression type is derived in a form suitable for the nonlinear observation characteristics and the signal processing considering higher-order correlation information between the specific signal and observation. Furthermore, the effectiveness of the proposed theory is confirmed by applying it to the observed data measured in real sound environment.

Improved Particle Filter for Non-Gaussian Forecasting-aided State Estimation

Gaussian assumptions of non-Gaussian noises hinder the improvement of state estimation accuracy.In this paper,an asymmetric generalized Gaussian distribution(AGGD),as a unified representation of various unimodal distributions,is applied to formulate the non-Gaussian forecasting-aided state estimation problem.To address the problem,an improved particle filter is proposed,which integrates a near-optimal AGGD proposal function and an AGGD sampling method into the typical particle filter.The AGGD proposal function can approximate the target distribution of state variables to greatly alleviate particle degeneracy and promote precise estimation,through considering both state transitions and latest measurements.For rapid particle generation from the AGGD proposal function,an efficient inverse cumulative distribution function(CDF)sampling method is employed based on the derived approximation of inverse CDF of AGGD.Numerical simulations are carried out on a modified balanced IEEE 123-bus test system.The results validate that the proposed method outperforms other popular state estimation methods in terms of accuracy and robustness,whether in Gaussian,non-Gaussian,or abnormal measurement errors.

一种改进的UGPF算法及其在导航问题中的应用

通过对高斯粒子滤波(GPF)算法的分析与总结,提出了一种基于无味卡尔曼滤波(UKF)方法的改进GPF算法(改进UGPF算法)。该方法主要利用UKF获取更优的重要性抽样函数,同时优化GPF滤波的算法流程结构。最后通过二维目标跟踪过程中位置导航参数估计问题,对该算法进行了仿真分析,所得结果验证了该算法的有效性。

基于改进粒子滤波的锂离子电池剩余寿命预测

针对锂离子电池剩余寿命预测精度低、泛化能力差等问题,提出基于改进粒子滤波的预测方案。首先,提出双高斯模型作为退化经验模型,拟合锂离子电池的容量退化过程。然后,通过先验知识设置退化模型的初始参数,并利用粒子滤波方法进行参数更新。针对预测过程中出现的粒子退化问题,提出高斯混合方法进行粒子重采样,拟合重采样过程中粒子复杂的非线性分布和长尾分布,保证预测结果的概率密度分布状况均匀且集中。最后在不同的数据集上进行了实验验证,结果表明所提出的改进粒子滤波方案具有较高的精度和较强的鲁棒性。

震后特殊环境下压埋人员精确定位算法

针对目前对震后压埋人员定位精度较低、探测设备成本高且易受环境影响等不足,提出适用于压埋环境特性的压埋人员手机WiFi定位方法,通过衰减因子模型对WiFi探针获取的RSSI数据进行距离解算,结合简化压埋环境内部信号传输方式,采用高斯-卡尔曼滤波对获取的RSSI数据进行处理,通过模型测定的距离,利用改进附有参数的加权最小二乘平差方法,结合粒子群优化算法,最终得到压埋人员手机平面坐标位置。研究结果表明,该方法具有较高精度,在10 m×10 m范围内其平面坐标定位误差在0.3 m左右,可为震后压埋人员应急救援提供辅助决策。

条件线性高斯模型的Gauss Hermite filter-Kalman filter算法

针对条件线性高斯状态空间模型,提出了高斯厄密特滤波-卡尔曼滤波(Gauss Hermite filter-Kalmanfilter,GHF-KF)滤波算法。算法将模型中的条件线性状态方程代入观测方程,并融合线性状态的过程噪声和观测噪声,由GHF获得非线性状态的估计;再将非线性状态的估计均值代入线性状态方程与观测方程,由KF获得线性状态的估计;获得的非线性状态估计方差还用于修正由KF估计的线性状态,以提高精度。将GHF-KF算法应用于目标跟踪的仿真结果表明,与现有Rao-Blackwellized粒子滤波器RBPF相比,新方法在保证估计精度的同时,明显提高了实时性,计算时间仅约为RBPF的5%。

润滑油灰分对 GPF 过滤效率影响的试验研究

基于润滑油掺烧快速老化方法,在发动机台架上研究灰分对GPF背压和发动机动力性的影响;基于世界统一的轻型车测试循(WLTC)整车试验和实际道路测试(RDE)研究了灰分对GPF过滤效率的影响。结果表明:灰分沉积会提高发动机的排气背压,降低发动机的动力性,60 g灰分量时背压最大升高8.8 kPa,扭矩下降3.7 N·m;WLTC工况第一阶段PN排放贡献率大于90%,且WLTC和RDE工况少量灰分即可显著提高GPF对PN的过滤效率,3 g灰分量下过滤效率可达96.6%;过滤效率随着灰分量的增加而增大,60 g灰分样件的PN过滤效率达到99.6%。

机动再入目标的FGPF-BLUE跟踪

机动再入目标的运动具有明显的非线性,其观测又往往在传感器坐标系下进行,构成强非线性的跟踪问题。为了克服扩展卡尔曼滤波和粒子滤波在跟踪精度和实时性方面的缺点,提出了一种新型的非线性跟踪算法。新型的FGPF-BLUE滤波将快速高斯粒子滤波的预测步骤与最优线性无偏估计的更新步骤相结合,是一种半蒙特卡罗滤波方法。建立了机动再入目标的动态模型,并分别应用扩展卡尔曼滤波、粒子滤波和FGPF-BLUE滤波实现了对该目标的跟踪。通过对各种滤波方法精度和消耗时间的对比,可知新方法的稳态性能优于其他两种算法,实时性优于粒子滤波。

后验Monte Carlo Gaussian采样粒子滤波WSN定位

环境因素导致无线传感器网络定位存在噪声影响,实质上是非平滑的非线性问题。针对传统粒子滤波算法在处理该问题时精度不高的缺点,提出一种基于后验泊松分布的Monte Carlo Gaussian重采样粒子滤波算法的无线传感器网络定位算法。基于粒子滤波算法,借鉴扩展卡尔曼滤波算法采用近似后验高斯分布思想,设计了后验泊松分布Monte Carlo Gaussian重采样粒子滤波器。采用该滤波器设计实现了无线传感器网络定位算法,解决了非平滑非线性的噪声干扰定位问题。分别对滤波器和定位算法的性能进行了对比仿真实验,结果验证了所提算法的有效性。

A kind of fast Gaussian particle filter based on Artificial Fish School Algorithm

This paper proposes an improved Gaussian particle filter integratingthe Artificial Fish School Algorithm to optimise the measured values to improve the overall estimation accuracy of the system.Meanwhile,it also solves the problems of susceptibility to interference and insufficient estimation accuracy in nonlinear systems.Furthermore,since the calculation time of the fusion algorithm increases,in order to ensure the speed of state estimation,the linear transformation of standard particle swarm is used to replace the particle sampling link of Gaussian particle filter.Simulation results show that the calculation speed of a fast Gaussian Particle Filter based on the Artificial Fish School Algorithm is 21.7%faster than the Particle Filter based on the Artificial Fish School Algorithm.Compared with Particle Filter,Gaussian particle filter,and the Artificial Fish School Algorithm,the proposed algorithm has a higher accuracy.

计及噪声和模型参数不确定的发电机动态状态估计

针对发电机动态状态估计过程中通信噪声以及模型参数不确定时估计精度降低和鲁棒性差的缺陷,提出了一种具有鲁棒性的发电机动态状态估计方法——H∞无迹粒子滤波(HUPF)。首先,建立四阶发电机的状态空间模型,利用无迹变换法计算粒子滤波的重要密度分布,提高了滤波精度和计算效率,增加了算法的灵活性;其次,根据H∞滤波理论建立发电机模型不确定性的边界约束准则,并在此基础上结合无迹粒子滤波(UPF),设计了一种可以根据模型不确定性动态调整估计误差协方差的更新策略,进一步提升了发电机的估计精度和抗差性能。通过IEEE 39节点系统中的仿真算例验证了所提方法的有效性,测试结果表明:所提HUPF方法的均方根误差最低为0.006,最高为0.0458,相比于UKF、UPF和AUKF方法,HUPF方法的均方根误差最小,能够显著提高模型不确定情形下发电机的状态估计精度,并且具有更强的鲁棒性。

基于改进金枪鱼优化粒子滤波算法的谐波估计

新能源快速发展和电力电子设备不断应用,在推动整个电力系统快速发展的同时,也加重了影响电能质量的谐波污染问题。对谐波的准确估计是有效解决谐波污染的前提,针对目前电力系统的谐波估计精度不高、适用性低、抗干扰性较弱等问题,提出了改进金枪鱼群优化粒子滤波算法(TSO-PF)。首先引入Tent混沌映射初始化粒子种群,使其个体分布更均匀;随后将金枪鱼的螺旋觅食和抛物线觅食两种狩猎行为引入粒子滤波算法中,通过加入高斯扰动因子和水波动态自适应因子改进策略,提升金枪鱼优化算法的寻优能力,并指导粒子向高似然区域移动,提高粒子的多样性;最后对IEEE-14节点系统进行仿真,结果表明,所提的算法具有较好的鲁棒性、精度和抗干扰能力。

基于高斯混合概率假设滤波的水下目标跟踪算法

为了解决传统水下目标跟踪中目标数目估计不准确、状态估计误差增长过快的问题,提出了一种基于高斯混合概率假设滤波的水下目标跟踪算法。该算法基于双基地观测模型,采用高斯混合概率假设滤波算法处理方位和时延信息,利用粒子群算法处理多普勒频率获得矢量速度,进一步提升算法的跟踪精度。结果表明,该算法能完成在杂波环境下对目标的跟踪,相比传统的关联算法,能够有效地实现目标个数估计和抑制状态误差增长的目的。

基于高斯过程粒子滤波的WIFI信号定位追踪

提出基于高斯过程粒子滤波的模型,根据室内WIFI信号强度进行行程追踪。非参数估计的高斯过程可以量化不确定性,与参数模型相比有更高的灵活性。仿真结果表明,与粒子滤波的行程追踪模型相比,基于高斯过程粒子滤波的模型在迭代初期估计更准确,更稳定。