WO2018098926A1 - 一种适用于闪烁噪声的多目标跟踪方法及系统 - Google Patents
一种适用于闪烁噪声的多目标跟踪方法及系统 Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/66—Radar-tracking systems; Analogous systems
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- the invention belongs to the field of multi-sensor information fusion technology, and in particular relates to a multi-target tracking method and system suitable for flicker noise.
- the edge-distributed Bayesian filter is an effective method for multi-target tracking under clutter and noise, but the filter is only suitable for the case where the noise is Gaussian noise, and not for the case where the noise is flicker noise. How to solve the multi-target tracking problem under flicker noise is a key technical problem that needs to be explored and solved.
- the technical problem to be solved by the present invention is to provide a multi-target tracking method and tracking system suitable for flicker noise, aiming at solving the tracking problem of nonlinear moving targets under flicker noise.
- the present invention is implemented in such a manner that a multi-target tracking method suitable for scintillation noise includes:
- the heuristic method is used to generate the shape parameters and scale parameters of the gamma distribution, and then the predicted contract distribution and prediction of each target at the current moment are obtained. Probability of existence;
- the variation data Bayesian method is used to sequentially process the current time measurement data, and the update contract distribution and the update existence probability of each target at the current time are obtained;
- the target having a probability greater than the second threshold, the contract distribution of the extracted target is used as the output of the current time, and the average of the output contract distribution is used as the state estimate of the current time target.
- the invention also provides a multi-target tracking system suitable for scintillation noise, comprising:
- the prediction module is configured to generate a shape parameter and a scale parameter of the gamma distribution according to the contract distribution and the existence probability of each target at the previous moment and the time difference between the current moment and the previous moment, thereby obtaining the target of each moment at the current moment.
- Forecast contract distribution and forecast existence probability
- An update module configured to sequentially process the current time measurement data according to the predicted contract distribution and the predicted existence probability of each target at the current time, and obtain the update contract distribution and update of each target at the current time by using the variational Bayesian method. Probability of existence;
- a generating module configured to generate a contract distribution of the new target by using the current time measurement data, and specify an existence probability for the new target, and distribute the contract distribution and the existence probability of the new target to the update contract distribution of the current time respectively Update the existence probability to merge, and obtain the contract distribution and existence probability of each target at the current time;
- An extracting module configured to cut off a target whose probability of existence is less than a first threshold from each target in the current time, and use the contract distribution and the existence probability of the remaining target as a next recursive input of the filter, after the clipping
- the remaining targets extract the target whose probability of existence is greater than the second threshold, and the contract distribution of the extracted target is used as the output of the current time, and the average of the output contract distribution is used as the state estimation of the current time target.
- the present invention has the beneficial effects that: in the embodiment of the present invention, the flicker noise is modeled by the t distribution, the closed expression is obtained by the variational Bayesian method, and the multivariate is approximated by the product of the edge distribution of each subvariate.
- the contract distribution makes multivariate contract estimation into iterative estimation of the edge distribution of each sub-variable, which effectively solves the tracking problem of nonlinear moving targets under flicker noise and improves the tracking accuracy of multi-target.
- FIG. 1 is a flowchart of a multi-target tracking method suitable for flicker noise according to an embodiment of the present invention
- 2 is a measurement data of 70 scan cycles of a sensor according to an embodiment of the present invention
- FIG. 3 is a result of processing by a multi-target tracking method according to an embodiment of the present invention under flicker noise;
- Figure 4 is a result of processing according to the multi-target tracking method of the UK-PHD filter under flicker noise
- FIG. 5 is a schematic diagram showing an average OFAC distance obtained by 100 experiments in a multi-target tracking method and a UK-PHD filtering method according to an embodiment of the present invention
- FIG. 6 is a schematic structural diagram of a multi-target tracking system suitable for flicker noise according to an embodiment of the present invention.
- the embodiment of the present invention predicts the contract distribution and the existence probability of each target of the new measurement data received at the current time according to the contract distribution and the existence probability of each target of the measurement data received at the previous moment; and utilizes the predicted contract distribution and the existence probability according to the predicted contract distribution and the existence probability
- the variational Bayesian method sequentially processes each measurement at the current time to obtain the updated contract distribution and existence probability of each target; respectively, the updated contract distribution and existence probability are combined with the contract distribution and existence probability of the new target to generate each current moment.
- the contract distribution and the probability of existence of the target so that the embodiment of the invention can well solve the tracking problem of the nonlinear moving target under the flicker noise.
- an embodiment of the present invention provides a multi-target tracking method suitable for flicker noise as shown in FIG. 1, which includes:
- S103 Generate a contract distribution of the new target by using the current time measurement data, and specify an existence probability for the new target, and update the contract distribution and the existence probability of the new target with the current time update contract distribution and update existence probability respectively. Consolidate to obtain the contract distribution and existence probability of each target at the current time;
- step S101 to k-1 represents the previous time, k represents the current time, the time T k-1 represents a previous time, t k represents a measuring time of the current time, the current time is subject to noise ⁇
- the t distribution of the dimension, the probability density function measured by the current time is represented by S(z k ; H k x k , R k , r k ), where H k x k represents the mean of the t distribution, and R k represents the precision matrix, r k represents the degree of freedom of the t distribution, and
- the multivariate contract distribution of target i at the previous moment is The existence probability of the target i is ⁇ i,k-1 , where N represents a Gaussian distribution, g represents a gamma distribution, and x i,k-1 represents the state vector of the i-th contract distribution at the previous moment, m i,k- 1 represents the mean of the Gaussian distribution in the i-th contract distribution at the previous
- ⁇ i,k-1 represent the shape parameters of the gamma distribution in the i-th contract distribution at the previous moment
- ⁇ i,k-1 represent the scale parameter of the gamma distribution in the i-th contract distribution at the previous moment
- the predicted existence probability of each target at the current moment is ⁇ i,k
- k-1 P s,k (t k -t k-1 ) ⁇ i,k-1 ;
- Sigma point x i,0 m i,k-1
- Sigma point weight l 1,..., ⁇
- Survival probability for the target ⁇ i,k
- k-1 ⁇ ⁇ ⁇ i,k-1 is the shape parameter of the gamma distribution in the i-th contract distribution at the current time
- the contract distribution of each target when updating with the jth measurement is among them, Represents the gamma function, tr represents the trace of the matrix, Means the mean vector, Representing a covariance matrix, Represents the filter gain; Sigma point
- the shape parameter of the gamma distribution is
- the scale parameter of the gamma distribution is H k is the observation matrix, R k is the observed noise variance matrix, P D,k is the detection probability of the target, ⁇ c,k is the clutter density, I is the unit matrix, y j,k is the jth received at the current time Measurement data, the superscript T is represented as a transpose of a matrix or a vector, and ⁇ is the total dimension of the state vector;
- step S103 it is set that the update contract distribution of each target at the current time is
- the target of nonlinear motion in a two-dimensional space [-1000 m, 1000 m] ⁇ [-1000 m, 1000 m] is considered.
- the observation equation of the radar is:
- the simulated observation data of one experiment is shown in Fig. 2.
- the simulation data of FIG. 2 is processed by the UK-PHD filter under the existing flicker noise in the embodiment of the present invention, and the average OFAP (Optimal Subpattern Assignment) distance is obtained by 100 Monte Carlo experiments. Shown.
- the existing multi-target tracking method based on flicker noise can more accurately track the nonlinear moving target with uncertain correlation and detection uncertainty under scintillation noise.
- reliable target state estimation its OSPA distance is smaller than the existing SPAC distance obtained by this method.
- the present invention also provides an embodiment as shown in FIG. 6, a multi-target tracking system, comprising:
- the prediction module 601 is configured to generate a shape parameter and a scale parameter of the gamma distribution according to the contract distribution and the existence probability of each target at the previous moment and the time difference between the current time and the previous moment, thereby obtaining each target at the current moment. Predicted contract distribution and predicted existence probability;
- the update module 602 is configured to sequentially process the measurement data of the current time according to the predicted contract distribution and the predicted existence probability of each target at the current time, and obtain the update contract distribution and update existence of each target at the current time by using the variational Bayesian method. Probability
- the generating module 603 is configured to generate a contract distribution of the new target by using the measurement data of the current time, and specify an existence probability for the new target, and distribute the contract distribution and the existence probability of the new target to the updated contract distribution of the current time respectively. And updating the existence probability to merge, and obtaining the contract distribution and the existence probability of each target at the current time;
- the extracting module 604 is configured to: cut off the target whose probability of existence is less than the first threshold from each target in the current time, and use the contract distribution and the existence probability of the remaining target as the input of the filter next recursion, from the cut.
- the remaining target is extracted from the target whose probability of existence is greater than the second threshold, and the contract distribution of the extracted target is used as the output of the current time, and the average value of the output contract distribution is used as the state estimation of the current time target.
- prediction module 601 is specifically configured to:
- the current time is represented by k-1, k represents the current time, t k-1 represents the time of the previous time, t k represents the time of the current time, and the measurement noise of the current time obeys the t distribution of the dimension, with S ( z k ; H k x k , R k , r k ) represent the probability density function measured at the current time, where H k x k represents the mean of the t distribution, R k represents the precision matrix, and r k represents the degree of freedom of the t distribution
- the multivariate contract distribution of target i at the previous moment is The existence probability of the target i is ⁇ i,k-1 , where N represents a Gaussian distribution, g represents a gamma distribution, and x i,k-1 represents the state vector of the i-th contract distribution at the previous moment, m i,k- 1 represents the mean of the Gaussian distribution in the i-th contract distribution at the previous moment, and P
- ⁇ i,k-1 represent the shape parameters of the gamma distribution in the i-th contract distribution at the previous moment
- ⁇ i,k-1 represent the scale parameter of the gamma distribution in the i-th contract distribution at the previous moment
- the predicted existence probability of each target at the current moment is ⁇ i,k
- k-1 P s,k (t k -t k-1 ) ⁇ i,k-1 ;
- Sigma point x i,0 m i,k-1
- Sigma point weight l 1,..., ⁇
- Survival probability for the target ⁇ i,k
- k-1 ⁇ ⁇ ⁇ i,k-1 is the shape parameter of the gamma distribution in the i-th contract distribution at the current time
- update module 602 is specifically configured to:
- the first to the Mth measurement data are sequentially processed sequentially by using the variational Bayesian method
- the contract distribution of each target when updating with the jth measurement is among them, Represents the gamma function, tr represents the trace of the matrix, Means the mean vector, Representing a covariance matrix, Represents the filter gain; Sigma point
- the shape parameter of the gamma distribution is
- the scale parameter of the gamma distribution is H k is the observation matrix, R k is the observed noise variance matrix, P D,k is the detection probability of the target, ⁇ c,k is the clutter density, I is the unit matrix, y j,k is the jth received at the current time Measurement data, the superscript T is represented as a transpose of a matrix or a vector, and ⁇ is the total dimension of the state vector;
- the contract distribution and the existence probability of each target after processing the Mth measurement data are respectively used as the update contract distribution of each target at the current time, thereby obtaining the update contract distribution of each target at the current time.
- the generating module 603 is specifically configured to:
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Abstract
一种适用于闪烁噪声的多目标跟踪方法与系统,多目标跟踪方法包括:预测步骤、更新步骤、生成步骤以及提取步骤。在保证数据处理实时性的同时,有效地解决了闪烁噪声下非线性运动目标的跟踪问题。
Description
本发明属于多传感器信息融合技术领域,尤其涉及一种适用于闪烁噪声的多目标跟踪方法及系统。
边缘分布贝叶斯滤波器是适用于杂波和噪声下多目标跟踪的有效方法,但滤波器仅适用于噪声为高斯噪声的情况,不适用于噪声为闪烁噪声的情况。如何解决闪烁噪声下多目标跟踪问题是需要探索和解决的关键技术问题。
发明内容
本发明所要解决的技术问题在于提供一种适用于闪烁噪声的多目标跟踪方法与跟踪系统,旨在解决闪烁噪声下非线性运动目标的跟踪问题。
本发明是这样实现的,一种适用于闪烁噪声的多目标跟踪方法,包括:
根据前一时刻各个目标的合同分布和存在概率以及当前时刻与前一时刻的时间差,采用启发式的方法产生伽马分布的形状参数和尺度参数,进而得到当前时刻各个目标的预测合同分布和预测存在概率;
根据所述当前时刻各个目标的预测合同分布和预测存在概率,利用变分贝叶斯方法序贯处理当前时刻的测量数据,得到当前时刻各个目标的更新合同分布和更新存在概率;
利用当前时刻的测量数据生成新生目标的合同分布,并为所述新生目标指定存在概率,将所述新生目标的合同分布及存在概率分别与所述当前时刻的更新合同分布及更新存在概率进行合并,得到当前时刻各个目标的合同分布和存在概率;
从所述当前时刻各个目标中裁减掉存在概率小于第一阈值的目标,并将裁减后余下目标的合同分布和存在概率作为滤波器下一次递归的输入,从所述裁减后余下的目标中提取存在概率大于第二阈值的目标,所提取出的目标的合同分布作为所述当前时刻的输出,所输出的合同分布的均值作为当前时刻目标的状态估计。
本发明还提供了一种适用于闪烁噪声的多目标跟踪系统,包括:
预测模块,用于根据前一时刻各个目标的合同分布和存在概率以及当前时刻与前一时刻的时间差,采用启发式的方法产生伽马分布的形状参数和尺度参数,进而得到当前时刻各个目标的预测合同分布和预测存在概率;
更新模块,用于根据所述当前时刻各个目标的预测合同分布和预测存在概率,利用变分贝叶斯方法序贯处理当前时刻的测量数据,得到当前时刻各个目标的更新合同分布和更新
存在概率;
生成模块,用于利用当前时刻的测量数据生成新生目标的合同分布,并为所述新生目标指定存在概率,将所述新生目标的合同分布及存在概率分别与所述当前时刻的更新合同分布及更新存在概率进行合并,得到当前时刻各个目标的合同分布和存在概率;
提取模块,用于从所述当前时刻各个目标中裁减掉存在概率小于第一阈值的目标,并将裁减后余下目标的合同分布和存在概率作为滤波器下一次递归的输入,从所述裁减后余下的目标中提取存在概率大于第二阈值的目标,所提取出的目标的合同分布作为所述当前时刻的输出,所输出的合同分布的均值作为当前时刻目标的状态估计。
本发明与现有技术相比,有益效果在于:本发明实施例通过用t分布建模闪烁噪声,利用变分贝叶斯方法获得闭合表达式,用各个分变量边缘分布的乘积来逼近多变量的合同分布,从而将多变量的合同估计化为对各个分变量边缘分布的迭代估计,有效解决了闪烁噪声下非线性运动目标的跟踪问题,提高多目标的跟踪精度。
图1是本发明实施例提供的一种适用于闪烁噪声的多目标跟踪方法的流程图;
图2是本发明实施例提供的传感器70个扫描周期的测量数据;
图3是在闪烁噪声下按照本发明实施例提供的多目标跟踪方法处理得到的结果;
图4是在闪烁噪声下根据UK-PHD滤波器的多目标跟踪方法处理得到的结果;
图5是按照本发明实施例提供多目标跟踪方法与UK-PHD滤波方法在经过100次实验得到的平均OSPA距离示意图;
图6是本发明实施例提供一种适用于闪烁噪声的多目标跟踪系统的结构示意图。
为了使本发明的目的、技术方案及优点更加清楚明白,以下结合附图及实施例,对本发明进行进一步详细说明。应当理解,此处所描述的具体实施例仅仅用以解释本发明,并不用于限定本发明。
本发明实施例根据前一时刻接收的测量数据的各个目标的合同分布及存在概率预测当前时刻接收的新的测量数据的各个目标的合同分布及存在概率;根据预测的合同分布及存在概率,利用变分贝叶斯方法序贯处理当前时刻的各测量得到各个目标的更新合同分布及存在概率;分别将更新的合同分布及存在概率与新目标的合同分布及存在概率进行合并,生成当前时刻各个目标的合同分布及存在概率,这样使得本发明实施例能够很好的解决闪烁噪声下非线性运动目标的跟踪问题。
基于上述原理,本发明实施例提供了如图1所示的一种适用于闪烁噪声的多目标跟踪方法,包括:
S101,根据前一时刻各个目标的合同分布和存在概率以及当前时刻与前一时刻的时间
差,采用启发式的方法产生伽马分布的形状参数和尺度参数,进而得到当前时刻各个目标的预测合同分布和预测存在概率;
S102,根据所述当前时刻各个目标的预测合同分布和预测存在概率,利用变分贝叶斯方法序贯处理当前时刻的测量数据,得到当前时刻各个目标的更新合同分布和更新存在概率;
S103,利用当前时刻的测量数据生成新生目标的合同分布,并为所述新生目标指定存在概率,将所述新生目标的合同分布及存在概率分别与所述当前时刻的更新合同分布及更新存在概率进行合并,得到当前时刻各个目标的合同分布和存在概率;
S104,从所述当前时刻各个目标中裁减掉存在概率小于第一阈值的目标,并将裁减后余下目标的合同分布和存在概率作为滤波器下一次递归的输入,从所述裁减后余下的目标中提取存在概率大于第二阈值的目标,所提取出的目标的合同分布作为所述当前时刻的输出,所输出的合同分布的均值作为当前时刻目标的状态估计。
具体地,在步骤S101中,以k-1表示前一时刻,k表示当前时刻,tk-1表示前一时刻的时间,tk表示当前时刻的时间,所述当前时刻的测量噪声服从ξ维的t分布,以S(zk;Hkxk,Rk,rk)表示所述当前时刻测量的概率密度函数,其中Hkxk表示t分布的均值,Rk表示精度矩阵,rk表示t分布的自由度,且前一时刻目标i的多变量合同分布为目标i的存在概率为ρi,k-1,其中,N表示高斯分布,g表示伽玛分布,xi,k-1表示前一时刻第i个合同分布的状态向量,mi,k-1表示前一时刻第i个合同分布中高斯分布的均值,Pi,k-1表示前一时刻第i个合同分布中高斯分布的方差,表示Rk的对角线元素,和γi,k-1表示前一时刻第i个合同分布中伽玛分布的形状参数,和ηi,k-1表示前一时刻第i个合同分布中伽玛分布的尺度参数,ξ为状态向量的维数,i=1,2,…,Nk-1,Nk-1为前一时刻目标的总数;
根据前一时刻各个目标的合同分布和存在概率、当前时刻与前一时刻的时间差,得到当前时刻各个目标的预测合同分布当前时刻各个目标的预测存在概率为ρi,k|k-1=Ps,k(tk-tk-1)ρi,k-1;其中,i=1,2,…,Nk-1,为当前时刻第i个合同分布中高斯分布的均值,为当前时刻第i个合同分布中高斯分布的方差,Sigma点xi,0=mi,k-1,Sigma点的权重
l=1,…,ξ,为目标的幸存概率,γi,k|k-1=ργγi,k-1为当前时刻第i个合同分布中伽玛分布的形状参数,ηi,k|k-1=ρηηi,k-1为当前时刻第i个合同分布中伽玛分布的尺度参数,f为非线性函数,Qk-1为所述接收时刻的过程噪声方差矩阵,上标T表示矩阵或向量的转置,T为采样周期,δ为已知的常数,ρα,ρβ,ργ,ρη为传播因子,取值范围为(0,1],rk表示自由度,为已知常数,ξ为状态向量的维数,k为一尺度参数。
在步骤S102中,设当前时刻接收到的观测集为yk=(y1,k,…,yM,k),其中,M为当前时刻接收到测量数据总数,则所述根据所述当前时刻各个目标的预测合同分布和预测存在概率,利用变分贝叶斯方法序贯处理当前时刻的测量数据,得到当前时刻各个目标的更新合同分布和更新存在概率,包括:
S1022,利用变分贝叶斯方法对第1个至第M个测量数据依次进行序贯处理;
求得用第j个测量更新时各个目标的合同分布为其中,表示伽玛函数,tr表示矩阵的迹,表示均值向量,表示协方差矩阵,表示滤波器增益;其中
Sigma点
伽玛分布的形状参数为伽玛分布的尺度参数为
Hk为观测矩阵,Rk为观测噪声方差矩阵,PD,k为目标的检测概率,λc,k为杂波密度,I表示单位矩阵,yj,k为当前时刻接收到的第j个测量数据,上标T表示为矩阵或向量的转置,ξ为状态向量的总维数;
S1023,第M个测量数据处理后的各个目标的合同分布及存在概率分别为和其中i=1,2,…,Nk-1;将第M个测量数据处理后各个目标的合同分布及存在概率分别作为当前时刻各个目标的更新合同分布,由此得到当前时刻各个目标的更新合同分布为
及当前时刻各个目标的更新存在概率其中i=1,…,Nk-1,
在步骤S103中,设所述当前时刻各个目标的更新合同分布为各个目标的存在概率为ρi,k;其中i=1,2,…,Nk-1,利用所述当前时刻的M个测量数据生成当前时刻新生目标的合同分布为并指定当前时刻各新生目标的存在概率为其中j=1,2,…,M,为给定的第j个新生目标的合同分布中高斯分布的协方差,为第j个新生目标的合同分布中高斯分布的均值,由所述当前时刻第j个测量数据yj,k=[xj,k yj,k]T产生,并且和为所述当前时刻第j个新生目标的合同分布中伽玛分布的形状参数,和为所述当前时刻第j个新生目标的合同分布中伽玛分布的尺度参数。
下面结合图2至图5对本实施例进行进一步地解释:
在本实施例中,考虑二维空间[-1000m,1000m]×[-1000m,1000m]中非线性运动的目标。目标的状态由位置、速度和转弯率构成,表示为x=[x &x y &y ω],其中x和y分别表示位置分量,&x和&y分别表示速度分量,ω表示转弯率,上标T表示向量的转置,状
态转移矩阵为过程噪声方差矩阵为Δtk=tk-tk-1为当前时刻与前一时刻的时间差,σv和σw为过程噪声标准差;雷达的观测方程为:
为了产生仿真数据,取幸存概率pS,k=1.0、检测概率pD,k=0.9、过程噪声的标准差σv,=1ms-2,σw,=0.1rads-2和观测噪声的标准差σr=2m,σθ=0.0003rad。一次实验的仿真观测数据如图2所示。为了处理仿真数据,将本发明实施例与闪烁噪声下的无迹卡尔曼高斯混合概率假设密度滤波器的相关参数设置为pS,k=1.0、pD,k=0.9、σv=1ms-2、σw=0.1rads-2,σr=2m,σθ=0.0003rad、第一阈值为10-3、第二阈值为0.5,传播因子ρα=ρβ=ργ=ρη=0.75,伽玛分布形状参数初始值伽玛分布尺度参数初始值
闪烁噪声下UK-PHD滤波器新生目标的权重wγ=0.1、本发明实施例的新生目标的存在概率ργ=0.1、新产生目标的协方差图3和图4为对比滤波器与本发明实施例提供的多目标跟踪方法产生的结果。将本发明实施例与现有的闪烁噪声下的UK-PHD滤波器对图2的仿真数据进行处理,100次Monte Carlo实验得到平均OSPA(Optimal Subpattern Assignment,最优亚模式分配)距离如图5所示。将现有的基于闪烁噪声的UK-PHD滤波器与本发明相比,本发明的多目标跟踪方法在闪烁噪声下对于关联不确定、检测不确定的非线性运动目标的跟踪可获得更为精确和可靠的目标状态估计、其OSPA距离比现有的这种方法得到的OSPA距离要小。
本发明还提供了如图6所示的实施例,一种多目标跟踪系统,包括:
预测模块601,用于根据前一时刻各个目标的合同分布和存在概率以及当前时刻与前一时刻的时间差,采用启发式的方法产生伽马分布的形状参数和尺度参数,进而得到当前时刻各个目标的预测合同分布和预测存在概率;
更新模块602,用于根据所述当前时刻各个目标的预测合同分布和预测存在概率,利用变分贝叶斯方法序贯处理当前时刻的测量数据,得到当前时刻各个目标的更新合同分布和更新存在概率;
生成模块603,用于利用当前时刻的测量数据生成新生目标的合同分布,并为所述新生目标指定存在概率,将所述新生目标的合同分布及存在概率分别与所述当前时刻的更新合同分布及更新存在概率进行合并,得到当前时刻各个目标的合同分布和存在概率;
提取模块604,用于从所述当前时刻各个目标中裁减掉存在概率小于第一阈值的目标,并将裁减后余下目标的合同分布和存在概率作为滤波器下一次递归的输入,从所述裁减后余下的目标中提取存在概率大于第二阈值的目标,所提取出的目标的合同分布作为所述当前时刻的输出,所输出的合同分布的均值作为当前时刻目标的状态估计。
进一步地,预测模块601具体用于:
以k-1表示前一时刻,k表示当前时刻,tk-1表示前一时刻的时间,tk表示当前时刻的时间,所述当前时刻的测量噪声服从ξ维的t分布,以S(zk;Hkxk,Rk,rk)表示所述当前时刻测量的概率密度函数,其中Hkxk表示t分布的均值,Rk表示精度矩阵,rk表示t分布的自由度,且前一时刻目标i的多变量合同分布为目标i的存在概率为ρi,k-1,其中,N表示高斯分布,g表示伽玛分布,xi,k-1表示前一时刻第i个合同分布的状态向量,mi,k-1表示前一时刻第i个合同分布中高斯分布的均值,Pi,k-1表示前一时刻第i个合同分布中高斯分布的方差,表示Rk的对角线元素,和γi,k-1表示前一时刻第i个合同分布中伽玛分布的形状参数,和ηi,k-1表示前一时刻第i个合同分布中伽玛分布的尺度参数,ξ为状态向量的维数,i=1,2,…,Nk-1,Nk-1为前一时刻目标的总数;
根据前一时刻各个目标的合同分布和存在概率、当前时刻与前一时刻的时间差,得到当前时刻各个目标的预测合同分布当前时刻各个目标的预测存在概率为ρi,k|k-1=Ps,k(tk-tk-1)ρi,k-1;其中,i=1,2,…,Nk-1,为当前时刻第i个合同分布中高斯分布的均值,为当前时刻第i个合同分布中高斯分布的方差,Sigma点xi,0=mi,k-1,
Sigma点的权重
l=1,…,ξ,为目标的幸存概率,γi,k|k-1=ργγi,k-1为当前时刻第i个合同分布中伽玛分布的形状参数,ηi,k|k-1=ρηηi,k-1为当前时刻第i个合同分布中伽玛分布的尺度参数,f为非线性函数,Qk-1为所述接收时刻的过程噪声方差矩阵,上标T表示矩阵或向量的转置,T为采样周期,δ为已知的常数,ρα,ρβ,ργ,ρη为传播因子,取值范围为(0,1],rk表示自由度,为已知常数,ξ为状态向量的维数,k为一尺度参数。
进一步地,更新模块602具体用于:
利用变分贝叶斯方法对第1个至第M个测量数据依次进行序贯处理;
求得用第j个测量更新时各个目标的合同分布为
其中,表示伽玛函数,tr表示矩阵的迹,表示均值向量,表示协方差矩阵,表示滤波器增益;其中
Sigma点
伽玛分布的形状参数为伽玛分布的尺度参数为
Hk为观测矩阵,Rk为观测噪声方差矩阵,PD,k为目标的检测概率,λc,k为杂波密度,I表示单位矩阵,yj,k为当前时刻接收到的第j个测量数据,上标T表示为矩阵或向量的转置,ξ为状态向量的总维数;
进一步地,生成模块603具体用于:
设所述当前时刻各个目标的更新合同分布为各个目标的存在概率为ρi,k;其中i=1,2,…,Nk-1,利用所述当前时刻的M个测量数据生成当前时刻新生目标的合同分布为并指定当前时刻各新生目标的存在概率为其中j=1,2,…,M,为给定的第j个新生目标的合同分布中高斯分布的协方差,为第j个新生目标的合同分布中高斯分布的均值,由所述当前时刻第j个测量数据yj,k=[xj,k yj,k]T产生,并且和为所述当前时刻第j个新生目标的合同分布中伽玛分布的形状参数,和为所述当前时刻第j个新生目标的合同分布中伽玛分布的尺度参数。
以上所述仅为本发明的较佳实施例而已,并不用以限制本发明,凡在本发明的精神和原则之内所作的任何修改、等同替换和改进等,均应包含在本发明的保护范围之内。
Claims (8)
- 一种适用于闪烁噪声的多目标跟踪方法,其特征在于,包括:根据前一时刻各个目标的合同分布和存在概率以及当前时刻与前一时刻的时间差,采用启发式的方法产生伽马分布的形状参数和尺度参数,进而得到当前时刻各个目标的预测合同分布和预测存在概率;根据所述当前时刻各个目标的预测合同分布和预测存在概率,利用变分贝叶斯方法序贯处理当前时刻的测量数据,得到当前时刻各个目标的更新合同分布和更新存在概率;利用当前时刻的测量数据生成新生目标的合同分布,并为所述新生目标指定存在概率,将所述新生目标的合同分布及存在概率分别与所述当前时刻的更新合同分布及更新存在概率进行合并,得到当前时刻各个目标的合同分布和存在概率;从所述当前时刻各个目标中裁减掉存在概率小于第一阈值的目标,并将裁减后余下目标的合同分布和存在概率作为滤波器下一次递归的输入,从所述裁减后余下的目标中提取存在概率大于第二阈值的目标,所提取出的目标的合同分布作为所述当前时刻的输出,所输出的合同分布的均值作为当前时刻目标的状态估计。
- 如权利要求1所述的多目标跟踪方法,其特征在于,所述根据前一时刻各个目标的合同分布和存在概率以及当前时刻与前一时刻的时间差,采用启发式的方法产生伽马分布的形状参数和尺度参数,进而得到当前时刻各个目标的预测合同分布和预测存在概率,包括:以k-1表示前一时刻,k表示当前时刻,tk-1表示前一时刻的时间,tk表示当前时刻的时间,所述当前时刻的测量噪声服从ξ维的t分布,以S(zk;Hkxk,Rk,rk)表示所述当前时刻测量的概率密度函数,其中Hkxk表示t分布的均值,Rk表示精度矩阵,rk表示t分布的自由度,且前一时刻目标i的多变量合同分布为目标i的存在概率为ρi,k-1,其中,N表示高斯分布,g表示伽玛分布,xi,k-1表示前一时刻第i个合同分布的状态向量,mi,k-1表示前一时刻第i个合同分布中高斯分布的均值,Pi,k-1表示前一时刻第i个合同分布中高斯分布的方差,表示Rk的对角线元素,和γi,k-1表示前一时刻第i个合同分布中伽玛分布的形状参数,和ηi,k-1表示前一时刻第i个合同分布中伽玛分布的尺度参数,ξ为状态向量的维数,i=1,2,...,Nk-1,Nk-1为前一时刻目标的总数;根据前一时刻各个目标的合同分布和存在概率、当前时刻与前一时刻的时间差,得到当前时刻各个目标的预测合同分布当前时刻各个目标的预测存在 概率为ρi,k|k-1=Ps,k(tk-tk-1)ρi,k-1;其中,i=1,2,...,Nk-1,为当前时刻第i个合同分布中高斯分布的均值,为当前时刻第i个合同分布中高斯分布的方差,Sigma点xi,0=mi,k.1,Sigma点的权重 l=1,...,ξ,为目标的幸存概率,γi,k|k-1=ργγi,k-1为当前时刻第i个合同分布中伽玛分布的形状参数,ηi,k|k-1=ρηηi,k-1为当前时刻第i个合同分布中伽玛分布的尺度参数,f为非线性函数,Qk-1为所述接收时刻的过程噪声方差矩阵,上标T表示矩阵或向量的转置,T为采样周期,δ为已知的常数,ρα,ρβ,ργ,ρη为传播因子,取值范围为(0,1],rk表示自由度,为已知常数,ξ为状态向量的维数,k为一尺度参数。
- 如权利要求2所述的多目标跟踪方法,其特征在于,设当前时刻接收到的观测集为yk=(y1,k,…,yM,k),其中,M为当前时刻接收到测量数据总数,则所述根据所述当前时刻各个目标的预测合同分布和预测存在概率,利用变分贝叶斯方法序贯处理当前时刻的测量数据,得到当前时刻各个目标的更新合同分布和更新存在概率,包括:利用变分贝叶斯方法对第1个至第M个测量数据依次进行序贯处理;求得用第j个测量更新时各个目标的合同分布为其中,表示伽玛函数,tr表示矩阵的迹,表示均值向量,表示协方差矩阵,表示滤波器增益;其中 Sigma点 伽玛分布的形状参数为伽玛分布的尺度参数为 Hk为观测矩阵,Rk为观测噪声方差矩阵,PD,k为目标的检测概率,λc,k为杂波密度,I表示单位矩阵,yj,k为当前时刻接收到的第j个测量数据,上标T表示为矩阵或向量的转置,ξ为状态向量的总维数;
- 如权利要求1所述的多目标跟踪方法,其特征在于,设所述当前时刻接收到的观测集为yk=(y1,k,…,yM,k),其中,M为所述当前时刻接收到所述新的测量数据总数,所述利用当前时刻的测量数据生成新生目标的合同分布,并为所述新生目标指定存在概率,将所述新生目标的合同分布及存在概率分别与所述当前时刻的更新合同分布及更新存在概率进行合并,得到当前时刻各个目标的合同分布和存在概率包括:设所述当前时刻各个目标的更新合同分布为各个目标的存在概率为ρi,k;其中i=1,2,...,Nk-1,利用所述当前时刻的M个测量数据生成当前时刻新生目标的合同分布为并指定当前时刻各新生目标的存在概率为其中j=1,2,...,M,为给定的第j个新生目标的合同分布中高斯分布的协方差,为第j个新生目标的合同分布中高斯分布的均值,由所述当前时刻第j个测量数据yj,k=[xj,k yj,k]T产生,并且和为所述当前时刻第j个新生目标的合同分布中伽玛分布的形状参数,和为所述当前时刻第j个新生目标的合同分布中伽玛分布的尺度参数;
- 一种适用于闪烁噪声的多目标跟踪系统,其特征在于,包括:预测模块,用于根据前一时刻各个目标的合同分布和存在概率以及当前时刻与前一时刻的时间差,采用启发式的方法产生伽马分布的形状参数和尺度参数,进而得到当前时刻各个目标的预测合同分布和预测存在概率;更新模块,用于根据所述当前时刻各个目标的预测合同分布和预测存在概率,利用变分贝叶斯方法序贯处理当前时刻的测量数据,得到当前时刻各个目标的更新合同分布和更新存在概率;生成模块,用于利用当前时刻的测量数据生成新生目标的合同分布,并为所述新生目标指定存在概率,将所述新生目标的合同分布及存在概率分别与所述当前时刻的更新合同分布及更新存在概率进行合并,得到当前时刻各个目标的合同分布和存在概率;提取模块,用于从所述当前时刻各个目标中裁减掉存在概率小于第一阈值的目标,并将裁减后余下目标的合同分布和存在概率作为滤波器下一次递归的输入,从所述裁减后余下的目标中提取存在概率大于第二阈值的目标,所提取出的目标的合同分布作为所述当前时刻的输出,所输出的合同分布的均值作为当前时刻目标的状态估计。
- 如权利要求5所述的多目标跟踪系统,其特征在于,所述预测模块具体用于:以k-1表示前一时刻,k表示当前时刻,tk-1表示前一时刻的时间,tk表示当前时刻的时间,所述当前时刻的测量噪声服从ξ维的t分布,以S(zk;Hkxk,Rk,rk)表示所述当前时刻测量的概率密度函数,其中Hkxk表示t分布的均值,Rk表示精度矩阵,rk表示t分布的自由度,且前一时刻目标i的多变量合同分布为目标i的存在概率为ρi,k-1,其中,N表示高斯分布,g表示伽玛分布,xi,k-1表示前一时刻第i个合同分布的状态向量,mi,k-1表示前一时刻第i个合同分布中高斯分布的均值,Pi,k-1表示前一时刻第i个合同分布中高斯分布的方差,表示Rk的对角线元素,和γi,k-1表示前一时刻第i个合同分布中伽玛分布的形状参数,和ηi,k-1表示前一时刻第i个合同分布中伽玛分布的尺度参数,ξ为状态向量的维数,i=1,2,...,Nk-1,Nk-1为前一时刻目标的总数;根据前一时刻各个目标的合同分布和存在概率、当前时刻与前一时刻的时间差,得到当前时刻各个目标的预测合同分布当前时刻各个目标的预测存在概率为ρi,k|k-1=Ps,k(tk-tk-1)ρi,k-1;其中,i=1,2,...,Nk-1,为当前时 刻第i个合同分布中高斯分布的均值,为当前时刻第i个合同分布中高斯分布的方差,Sigma点xi,0=mi,k-1,Sigma点的权重 l=1,...,ξ,为目标的幸存概率,γi,k|k-1=ργγi,k-1为当前时刻第i个合同分布中伽玛分布的形状参数,ηi,k|k-1=ρηηi,k-1为当前时刻第i个合同分布中伽玛分布的尺度参数,f为非线性函数,Qk-1为所述接收时刻的过程噪声方差矩阵,上标T表示矩阵或向量的转置,T为采样周期,δ为已知的常数,ρα,ρβ,ργ,ρη为传播因子,取值范围为(0,1],rk表示自由度,为已知常数,ξ为状态向量的维数,k为一尺度参数。
- 如权利要求6所述的多目标跟踪方法,其特征在于,所述更新模块具体用于:利用变分贝叶斯方法对第1个至第M个测量数据依次进行序贯处理;求得用第j个测量更新时各个目标的合同分布为其中,表示伽玛函数,tr表示矩阵的迹,表示均值向量,表示协方差矩阵,表示滤波器增益;其中 Sigma点 伽玛分布的形状参数为伽玛分布的尺度参数为 Hk为观测矩阵,Rk为观测噪声方差矩阵,PD,k为目标的检测概率,λc,k为杂波密度,I表示单位矩阵,yj,k为当前时刻接收到的第j个测量数据,上标T表示为矩阵或向量的转置,ξ为状态向量的总维数;
- 如权利要求5所述的多目标跟踪系统,其特征在于,所述更新模块还用于:设所述当前时刻各个目标的更新合同分布为各个目标的存在概率为ρi,k;其中i=1,2,...,Nk-1,利用所述当前时刻的M个测量数据生成当前时刻新生目标的合同分布为并指定当前时刻各新生目标的存在概率为其中j=1,2,...,M,为给定的第j个新生目标的合同分布中高斯分布的协方差,为第j个新生目标的合同分布中高斯分布的均值,由所述当前时刻第j个测量数据yj,k=[xj,k yj,k]T产生,并且和为所述当前时刻第j个新生目标的合同分布中伽玛分布的形状参数,和为所述当前时刻第j个新生目标的合同分布中伽玛分布的尺度参数;
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