WSN移动目标的LSSVR回归建模定位理论与算法
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摘要
目标定位是无线传感器网络(Wireless Sensor Networks,WSN)的重要应用之一,实现准确可靠的目标定位在国防军事、环境监测、智能交通、安全监控等方面具有重要的应用价值。单目标定位是多目标定位的重要基础,以低能耗下综合提高实际环境中WSN单个移动目标定位准确度、快速性和可靠性为目标,论文研究包括局部回归建模、节点预测唤醒、快速建模定位方法的基于支持向量回归建模(Least Square Support Vector Regression, LSSVR)定位理论,这对促进制造信息化技术、网络化测控技术的发展与应用,加强制造工程、仪器仪表、信息学科的交叉,具有重要的学术价值和实际意义。研究工作得到教育部新世纪优秀人才支持计划项目(No.NCET -08-0211)、广东省自然科学基金项目(No.9151052101000013)资助。
论文从WSN目标定位基本环节入手,分析各个定位环节与定位性能指标的综合影响关系,从WSN目标定位方法、WSN目标预测方法、WSN节点唤醒与能耗等三方面综述国内外研究进展,确定论文的研究内容。论文的主要工作包括:
㈠讨论LSSVR数学模型及求解方法、特点,指出LSSVR适合复杂多元非线性系统建模问题,应用于资源受限的嵌入式计算系统具有明显优势;研究LSSVR回归建模WSN目标定位的基础理论与方法,指出距离向量与目标坐标存在非线性映射关系,满足应用LSSVR进行回归建模的数学条件;研究特征向量测量节点位置数量条件、向量空间映射条件;创造性提出应用LSSVR回归建模的WSN目标定位方法,该方法在小样本情况下具有较好推广性能,利用其LSSVR的抗噪能力可以减小测量噪声对定位结果的影响;建立LSSVR建模定位误差结构模型,模型影响误差和噪声影响误差分别反映LSSVR回归模型推广性能及抗噪能力大小;研究LSSVR目标定位误差空间分布特性,合理回归建模策略能调节模型定位误差、噪声定位误差分布函数,改善整体定位效果;讨论核函数对LSSVR建模定位影响机理,LSSVR建模选择的核函数应具有建模预测效果好、形式简单、核函数参数少等特点。
㈡理论分析基于目标发射功率的特征提取条件,提出信号强度差特征提取方法,该方法所构造的特征向量与信道参数P ( d 0)无关,发射功率不稳定时依然满足LSSVR建模定位条件,能减小目标发射功率变化对定位结果的影响;引入局部学习建模思想,研究LSSVR局部建模包含的训练样本点分布、采样点分布、建模区域的确定等一些规则,使LSSVR局部建模定位方法更具有实用性;研究建模参数变化对LSSVR局部建模定位特性的影响,指出不同建模参数与LSSVR定位误差相互之间关系;提出利用粒子群算法来优化LSSVR建模参数,LSSVR局部建模定位方法定位准确度明显提高。
㈢推导测量节点数N d、唤醒节点数N w、失跟率pm数学表达式,指出减少目标预测误差d p,能明显降低失跟率pm ;提出基于运动学原理的预测方法,在缺少目标运动先验信息下的机动性目标定位预测中有较好优势;研究基于粒子滤波的预测方法,对目标运动规律性较强情况下能取得较好的预测效果;研究预测时间动态调节方法,能够获得比固定预测时间间隔预测方法更高预测准确度,对机动性目标的适应性明显增强;提出基于动态预测的节点唤醒机制,相比基于线性预测的节点唤醒机制,减小不同运动特点目标预测误差,降低失跟率,实现较多测量节点数上的节点唤醒;建立节点唤醒机制的能耗估算公式,探索基于MATALB、OPNET的能耗仿真方法。
㈣研究目标移动下自适应LSSVR建模定位规律,指出可根据已有LSSVR模型建模节点、当前测量节点包含关系决定是否建模,减少建模次数;采用Gauss-Jordan列主元消元法对LSSVR矩阵方程进行同步求解,提高建模计算效率;研究数据汇集的分时通信机制、节点唤醒的广播式通信机制,减少节点通信时间;提出基于自适应LSSVR同步建模WSN目标快速定位方法。从建模定位计算、节点通信两方面减少定位时间,完成快速目标定位。该方法相比MLE方法定位准确度有所提高,定位时间明显减少,满足小于目标探测时间间隔条件,体现出良好的实时性能。
㈤把目标定位、目标预测、节点唤醒等定位环节进行综合,更加系统地去检验LSSVR定位方法的性能,可以达到良好的整体定位效果;基于研发的LSSVR室内快速定位系统,实验证明了LSSVR定位方法在实际应用环境中的实用性、有效性;分析LSSVR快速定位系统在制造流程、消防训练、船舶制造中应用的初步方案。
CC2430目标定位实验表明,通过LSSVR局部建模改善不同类型目标定位效果;采用基于动态预测的节点唤醒机制减小了定位能耗和失跟率;借助于自适应LSSVR同步建模快速定位方法提高了定位的实时性能;综合LSSVR定位方法可以达到良好的整体定位效果。选取距离值、信号强度差为特征量时,定位准确度分别减小19%-41%、51%-58%;在建模情况下定位时间分别为0.91s-1.01s、1.02s-1.12s,非建模情况下定位时间约为0.71s-0.81s;经过10次定位实验失跟次数均为0,但定位能耗与LP-MLE方法非常接近;室内定位实验证明了LSSVR定位方法在实际应用环境中的实用性、有效性,取得了良好的定位效果。如果在节点性能、网络可控性、远程接入等方面进行改进,可以推广应用到制造流程、消防训练、船舶制造等领域中。
Target localization is one of the important applications of Wireless Sensor Networks and achieving reliable and accurate target localization has an important application value for the national defense and military, environmental monitoring, intelligent transportation, security surveillance and so on. Single target localization is important basis of multiple targets localization. Aiming at achieving an integrated improvements of single target localization accuracy, rapidity and reliability in physical environment with low energy consumption, the research includes local regression modeling, target prediction, nodes wake-up, localization theory of rapid modeling and localization based on Least Square Support Vector Regression (LSSVR),which has an important academic value and practical significance for the promotion of development and application of the manufacturing information technology and networked measurement control technology and strengthening cross of manufacturing engineering, instrumentation, and information subjects. The research work is funded by the Ministry of Education Support Program for New Century Excellent Talent (No.NCET -08-0211) and Guangdong Province Natural Science Foundation project (No.9151052101000013).
Beginning with the basic steps of target localization in WSN, the influence relationship between various localization steps and localization performance indexes is analyzed in the paper. The research progress at home and abroad of WSN target localization, target prediction, nodes wake-up and energy consumption are reviewed generally to decide the research goals of this paper. Main research works in this paper are as follows:
(1) LSSVR mathematical model and its solution method, characteristics are discussed and it is pointed out that LSSVR is suitable for complex multivariate nonlinear system modeling problem. It has clear advantage to applying LSSVR in resource-constrained embedded computing system. The basic theory and methods of LSSVR regression modeling in WSN target localization is researched and points out that there is a non-linear mapping between distance-vector and target coordinates, meeting the mathematical conditions of adopting LSSVR regression modeling. Feature vector detection nodes in terms of location-number conditions, vector space mapping conditions are researched. The target localization method based on LSSVR regression model is proposed creatively, the method has a good generalization performance in small sample, making use of LSSVR anti-noise ability can reduce the impact of localization resulted from the measurement noise. LSSVR modeling localization errors structure model is established, the model affected error and noise error reflect LSSVR regression model promotion performance and anti-noise ability respectively. Target localization error spatial distribution characteristics based on LSSVR is researched, using a reasonable regression modeling strategy can adjust the distribution function of model localization error and noise error to improve the overall results of the localization. The mechanism of effects of the kernel function for target localization based on LSSVR is discussed, the kernel function for LSSVR modeling should has a good effect with model prediction, simple form, less parameters and so on.
(2)Based on the target’s transmit power feature extraction conditions is analyzed theoretically. Furthermore, signal strength difference feature extraction method is proposed. The feature vectors constructed by the method has nothing to do with channel parameters P ( d 0), meeting the conditions of LSSVR modeling and localization when the target’s transmit power is unstable all the same. It is able to reduce the effect of localization resulted from the target’s transmit power changing. Local learning modeling idea is introduced, the distribution of the training sample points and the sampling points, the modeling region and some other rules based on the LSSVR local modeling are researched, making the method of LSSVR local modeling and localization more practically. Changes of modeling parameters effect on LSSVR local modeling and localization is researched. Moreover, the relationship between the different modeling parameters and LSSVR localization error is pointed out and compromise the parameters value is essential. Using particle swarm optimization to optimize LSSVR model parameters was proposed, the localization accuracy of LSSVR local modeling and localization is improved obviously.
(3) The mathematical expression of the number of measuring nodes N d and awaken–up nodes N wtogether with the rate of loss p m are deduced. It is pointed out that reducing target prediction error d p can cut down the loss rate pm significantly. A mobility target prediction method based on kinematics theory is proposed, which has a good advantage in the absence of prior information of target motion. Prediction method based on particle filter is also studied, which obtains a better prediction results under a strong regularity of the target motion. The method of adjusting prediction time dynamically is researched, which achieves a higher prediction accuracy compared with the method of fixed prediction time interval. It enhances the adaptability of mobile targets markedly. Nodes wake-up mechanism based on dynamic prediction is proposed, which reduces target prediction error owing to characteristics of different movement and cuts down the rate of loss compared with linear prediction method. It can realizes nodes awaken with more measuring nodes. The energy consumption estimation formula for nodes wake-up mechanism is established. Moreover, simulation method of energy consumption based on MATLAB and OPNET is discussed.
(4) The law of mobile target localization based on adaptive LSSVR regression modeling is researched. It is pointed out that whether to modeling can be decided by the containing relationship between LSSVR modeling nodes and current measuring nodes so as to reduce the number of modeling. Using Gauss-Jordan elimination method with maximal column pivoting to solve LSSVR matrix equation synchronously improves the computational efficiency of modeling. Data-centric in hourly communication mechanism and radio-style communication mechanism of nodes wake-up are studied to reduce the communication time between nodes. The rapid target localization method based on adaptive LSSVR modeling synchronously is proposed. It reduces the localization time and achieves rapid target localization in two aspects of modeling localization calculation and nodes communication. Compared to MLE, the method has higher accuracy and much litter localization time which is less than the target detection time interval and reflects a good real-time performance.
(5) By synthesizing target localization, target prediction, nodes wake-up and so on, the performance of LSSVR localization method is evaluated more systematically. It is proved that it can obtain good overall localization results. Indoor positioning experiments based on the developed LSSVR rapid indoor positioning system proves the practicality and effectiveness of LSSVR localization method in the practical application environment. Moreover, the application schemes of LSSVR rapid positioning system in the manufacturing process, fire training, and shipbuilding are analyzed.
Experiments of target localization based on CC2430 shows that LSSVR local modeling can improve the localization effect of different types of targets. The wake-up mechanism based on dynamic prediction can also reduce the energy consumption and loss rate. The rapid localization method of adaptive synchronous LSSVR modeling enhances real-time performance of localization. Integrated LSSVR location methods can achieve good overall localization performance. When selecting the distance value and signal strength difference as feature value respectively, the localization errors are reduced by 19%-41%,51%-58% and the localization time are 0.91-1.01s,1.02s-1.12s in modeling case, while in non-modeling case it is about 0.71-0.81s. The loss time is zero through ten experiments, while the energy consumption is close to the method of LP-MLE. Indoor positioning experiment proves the practicality and effect of LSSVR localization method in the practical application environment and achieves a good localization result. LSSVR indoor positioning method can be further applied to the manufacturing process, fire training, and shipbuilding and other areas if the nodes performance, network controllability, remote access etc are improved.
论文从WSN目标定位基本环节入手,分析各个定位环节与定位性能指标的综合影响关系,从WSN目标定位方法、WSN目标预测方法、WSN节点唤醒与能耗等三方面综述国内外研究进展,确定论文的研究内容。论文的主要工作包括:
㈠讨论LSSVR数学模型及求解方法、特点,指出LSSVR适合复杂多元非线性系统建模问题,应用于资源受限的嵌入式计算系统具有明显优势;研究LSSVR回归建模WSN目标定位的基础理论与方法,指出距离向量与目标坐标存在非线性映射关系,满足应用LSSVR进行回归建模的数学条件;研究特征向量测量节点位置数量条件、向量空间映射条件;创造性提出应用LSSVR回归建模的WSN目标定位方法,该方法在小样本情况下具有较好推广性能,利用其LSSVR的抗噪能力可以减小测量噪声对定位结果的影响;建立LSSVR建模定位误差结构模型,模型影响误差和噪声影响误差分别反映LSSVR回归模型推广性能及抗噪能力大小;研究LSSVR目标定位误差空间分布特性,合理回归建模策略能调节模型定位误差、噪声定位误差分布函数,改善整体定位效果;讨论核函数对LSSVR建模定位影响机理,LSSVR建模选择的核函数应具有建模预测效果好、形式简单、核函数参数少等特点。
㈡理论分析基于目标发射功率的特征提取条件,提出信号强度差特征提取方法,该方法所构造的特征向量与信道参数P ( d 0)无关,发射功率不稳定时依然满足LSSVR建模定位条件,能减小目标发射功率变化对定位结果的影响;引入局部学习建模思想,研究LSSVR局部建模包含的训练样本点分布、采样点分布、建模区域的确定等一些规则,使LSSVR局部建模定位方法更具有实用性;研究建模参数变化对LSSVR局部建模定位特性的影响,指出不同建模参数与LSSVR定位误差相互之间关系;提出利用粒子群算法来优化LSSVR建模参数,LSSVR局部建模定位方法定位准确度明显提高。
㈢推导测量节点数N d、唤醒节点数N w、失跟率pm数学表达式,指出减少目标预测误差d p,能明显降低失跟率pm ;提出基于运动学原理的预测方法,在缺少目标运动先验信息下的机动性目标定位预测中有较好优势;研究基于粒子滤波的预测方法,对目标运动规律性较强情况下能取得较好的预测效果;研究预测时间动态调节方法,能够获得比固定预测时间间隔预测方法更高预测准确度,对机动性目标的适应性明显增强;提出基于动态预测的节点唤醒机制,相比基于线性预测的节点唤醒机制,减小不同运动特点目标预测误差,降低失跟率,实现较多测量节点数上的节点唤醒;建立节点唤醒机制的能耗估算公式,探索基于MATALB、OPNET的能耗仿真方法。
㈣研究目标移动下自适应LSSVR建模定位规律,指出可根据已有LSSVR模型建模节点、当前测量节点包含关系决定是否建模,减少建模次数;采用Gauss-Jordan列主元消元法对LSSVR矩阵方程进行同步求解,提高建模计算效率;研究数据汇集的分时通信机制、节点唤醒的广播式通信机制,减少节点通信时间;提出基于自适应LSSVR同步建模WSN目标快速定位方法。从建模定位计算、节点通信两方面减少定位时间,完成快速目标定位。该方法相比MLE方法定位准确度有所提高,定位时间明显减少,满足小于目标探测时间间隔条件,体现出良好的实时性能。
㈤把目标定位、目标预测、节点唤醒等定位环节进行综合,更加系统地去检验LSSVR定位方法的性能,可以达到良好的整体定位效果;基于研发的LSSVR室内快速定位系统,实验证明了LSSVR定位方法在实际应用环境中的实用性、有效性;分析LSSVR快速定位系统在制造流程、消防训练、船舶制造中应用的初步方案。
CC2430目标定位实验表明,通过LSSVR局部建模改善不同类型目标定位效果;采用基于动态预测的节点唤醒机制减小了定位能耗和失跟率;借助于自适应LSSVR同步建模快速定位方法提高了定位的实时性能;综合LSSVR定位方法可以达到良好的整体定位效果。选取距离值、信号强度差为特征量时,定位准确度分别减小19%-41%、51%-58%;在建模情况下定位时间分别为0.91s-1.01s、1.02s-1.12s,非建模情况下定位时间约为0.71s-0.81s;经过10次定位实验失跟次数均为0,但定位能耗与LP-MLE方法非常接近;室内定位实验证明了LSSVR定位方法在实际应用环境中的实用性、有效性,取得了良好的定位效果。如果在节点性能、网络可控性、远程接入等方面进行改进,可以推广应用到制造流程、消防训练、船舶制造等领域中。
Target localization is one of the important applications of Wireless Sensor Networks and achieving reliable and accurate target localization has an important application value for the national defense and military, environmental monitoring, intelligent transportation, security surveillance and so on. Single target localization is important basis of multiple targets localization. Aiming at achieving an integrated improvements of single target localization accuracy, rapidity and reliability in physical environment with low energy consumption, the research includes local regression modeling, target prediction, nodes wake-up, localization theory of rapid modeling and localization based on Least Square Support Vector Regression (LSSVR),which has an important academic value and practical significance for the promotion of development and application of the manufacturing information technology and networked measurement control technology and strengthening cross of manufacturing engineering, instrumentation, and information subjects. The research work is funded by the Ministry of Education Support Program for New Century Excellent Talent (No.NCET -08-0211) and Guangdong Province Natural Science Foundation project (No.9151052101000013).
Beginning with the basic steps of target localization in WSN, the influence relationship between various localization steps and localization performance indexes is analyzed in the paper. The research progress at home and abroad of WSN target localization, target prediction, nodes wake-up and energy consumption are reviewed generally to decide the research goals of this paper. Main research works in this paper are as follows:
(1) LSSVR mathematical model and its solution method, characteristics are discussed and it is pointed out that LSSVR is suitable for complex multivariate nonlinear system modeling problem. It has clear advantage to applying LSSVR in resource-constrained embedded computing system. The basic theory and methods of LSSVR regression modeling in WSN target localization is researched and points out that there is a non-linear mapping between distance-vector and target coordinates, meeting the mathematical conditions of adopting LSSVR regression modeling. Feature vector detection nodes in terms of location-number conditions, vector space mapping conditions are researched. The target localization method based on LSSVR regression model is proposed creatively, the method has a good generalization performance in small sample, making use of LSSVR anti-noise ability can reduce the impact of localization resulted from the measurement noise. LSSVR modeling localization errors structure model is established, the model affected error and noise error reflect LSSVR regression model promotion performance and anti-noise ability respectively. Target localization error spatial distribution characteristics based on LSSVR is researched, using a reasonable regression modeling strategy can adjust the distribution function of model localization error and noise error to improve the overall results of the localization. The mechanism of effects of the kernel function for target localization based on LSSVR is discussed, the kernel function for LSSVR modeling should has a good effect with model prediction, simple form, less parameters and so on.
(2)Based on the target’s transmit power feature extraction conditions is analyzed theoretically. Furthermore, signal strength difference feature extraction method is proposed. The feature vectors constructed by the method has nothing to do with channel parameters P ( d 0), meeting the conditions of LSSVR modeling and localization when the target’s transmit power is unstable all the same. It is able to reduce the effect of localization resulted from the target’s transmit power changing. Local learning modeling idea is introduced, the distribution of the training sample points and the sampling points, the modeling region and some other rules based on the LSSVR local modeling are researched, making the method of LSSVR local modeling and localization more practically. Changes of modeling parameters effect on LSSVR local modeling and localization is researched. Moreover, the relationship between the different modeling parameters and LSSVR localization error is pointed out and compromise the parameters value is essential. Using particle swarm optimization to optimize LSSVR model parameters was proposed, the localization accuracy of LSSVR local modeling and localization is improved obviously.
(3) The mathematical expression of the number of measuring nodes N d and awaken–up nodes N wtogether with the rate of loss p m are deduced. It is pointed out that reducing target prediction error d p can cut down the loss rate pm significantly. A mobility target prediction method based on kinematics theory is proposed, which has a good advantage in the absence of prior information of target motion. Prediction method based on particle filter is also studied, which obtains a better prediction results under a strong regularity of the target motion. The method of adjusting prediction time dynamically is researched, which achieves a higher prediction accuracy compared with the method of fixed prediction time interval. It enhances the adaptability of mobile targets markedly. Nodes wake-up mechanism based on dynamic prediction is proposed, which reduces target prediction error owing to characteristics of different movement and cuts down the rate of loss compared with linear prediction method. It can realizes nodes awaken with more measuring nodes. The energy consumption estimation formula for nodes wake-up mechanism is established. Moreover, simulation method of energy consumption based on MATLAB and OPNET is discussed.
(4) The law of mobile target localization based on adaptive LSSVR regression modeling is researched. It is pointed out that whether to modeling can be decided by the containing relationship between LSSVR modeling nodes and current measuring nodes so as to reduce the number of modeling. Using Gauss-Jordan elimination method with maximal column pivoting to solve LSSVR matrix equation synchronously improves the computational efficiency of modeling. Data-centric in hourly communication mechanism and radio-style communication mechanism of nodes wake-up are studied to reduce the communication time between nodes. The rapid target localization method based on adaptive LSSVR modeling synchronously is proposed. It reduces the localization time and achieves rapid target localization in two aspects of modeling localization calculation and nodes communication. Compared to MLE, the method has higher accuracy and much litter localization time which is less than the target detection time interval and reflects a good real-time performance.
(5) By synthesizing target localization, target prediction, nodes wake-up and so on, the performance of LSSVR localization method is evaluated more systematically. It is proved that it can obtain good overall localization results. Indoor positioning experiments based on the developed LSSVR rapid indoor positioning system proves the practicality and effectiveness of LSSVR localization method in the practical application environment. Moreover, the application schemes of LSSVR rapid positioning system in the manufacturing process, fire training, and shipbuilding are analyzed.
Experiments of target localization based on CC2430 shows that LSSVR local modeling can improve the localization effect of different types of targets. The wake-up mechanism based on dynamic prediction can also reduce the energy consumption and loss rate. The rapid localization method of adaptive synchronous LSSVR modeling enhances real-time performance of localization. Integrated LSSVR location methods can achieve good overall localization performance. When selecting the distance value and signal strength difference as feature value respectively, the localization errors are reduced by 19%-41%,51%-58% and the localization time are 0.91-1.01s,1.02s-1.12s in modeling case, while in non-modeling case it is about 0.71-0.81s. The loss time is zero through ten experiments, while the energy consumption is close to the method of LP-MLE. Indoor positioning experiment proves the practicality and effect of LSSVR localization method in the practical application environment and achieves a good localization result. LSSVR indoor positioning method can be further applied to the manufacturing process, fire training, and shipbuilding and other areas if the nodes performance, network controllability, remote access etc are improved.
引文
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