CN115220002B - A method and device for multi-target data association tracking at a fixed single station - Google Patents

A method and device for multi-target data association tracking at a fixed single station Download PDF

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CN115220002B
CN115220002B CN202210622342.1A CN202210622342A CN115220002B CN 115220002 B CN115220002 B CN 115220002B CN 202210622342 A CN202210622342 A CN 202210622342A CN 115220002 B CN115220002 B CN 115220002B
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CN115220002A (en
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李良群
邓兵
陈柱杰
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Shenzhen University
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    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO 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
    • G01S7/00Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G01S7/418Theoretical aspects
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO 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
    • G01S13/00Systems 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
    • G01S13/66Radar-tracking systems; Analogous systems

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Abstract

本申请实施例公开一种固定单站的多目标数据关联跟踪方法,用于更准确地对多目标物体的观测值进行数据关联。方法包括:获取至少两个目标物体在目标时刻的第一实际观测值集合;根据目标时刻所处的目标时间段确定每个目标物体的关联中心值;基于联合极大似然估计模型,根据关联中心值从第一实际观测值集合中确定每个目标物体的第二实际观测值集合;基于单目标跟踪模型,根据第二实际观测值集合确定每个目标物体在目标时刻的运动状态信息,运动状态信息用于确定每个目标物体在目标时间段中的航迹。

The embodiment of the present application discloses a multi-target data association tracking method of a fixed single station, which is used to more accurately associate data with the observation values of multiple target objects. The method includes: obtaining a first set of actual observation values of at least two target objects at a target time; determining the association center value of each target object according to the target time period at the target time; based on a joint maximum likelihood estimation model, determining a second set of actual observation values of each target object from the first set of actual observation values according to the association center value; based on a single target tracking model, determining the motion state information of each target object at the target time according to the second set of actual observation values, and the motion state information is used to determine the track of each target object in the target time period.

Description

一种固定单站的多目标数据关联跟踪方法和相关装置A method and device for multi-target data association tracking at a fixed single station

技术领域Technical Field

本申请涉及雷达数据处理技术领域,尤其涉及一种固定单站的多目标数据关联跟踪方法和相关装置。The present application relates to the field of radar data processing technology, and in particular to a multi-target data association tracking method and related devices for a fixed single station.

背景技术Background technique

在无源定位系统中,由于存在环境噪声和随机扰动,一次检测可能得到多个观测值,而且在这些观测值中,不知道哪些来自被跟踪的目标,哪些是虚假的观测值;并且当需要观测多个目标物体时,有一些与目标物体运动状态的预测值较远的观测值也不确定是属于哪个目标物体的,这就需要将观测值与目标物体关联起来,称为数据关联。In a passive positioning system, due to the presence of environmental noise and random disturbances, multiple observations may be obtained in one detection, and among these observations, it is unknown which ones are from the tracked target and which ones are false observations. When multiple target objects need to be observed, some observations that are far from the predicted values of the target object's motion state are also uncertain to which target object they belong. This requires associating the observations with the target objects, which is called data association.

一种数据关联的方法是,根据前一时刻的观测值对下一时刻目标物体的运动状态进行预测,将得到的预测值与实际值进行比较,将差异较小的数据通过极大联合似然估计进行计算,确定实际值与目标物体对应的关联对数据。One method of data association is to predict the motion state of the target object at the next moment based on the observed value at the previous moment, compare the predicted value with the actual value, calculate the data with smaller differences through maximum joint likelihood estimation, and determine the associated pair data corresponding to the actual value and the target object.

但是,在航迹起始阶段定位误差过大,滤波还没能达到稳定的收敛效果,所以此时若使用航迹量测预测值和真实量测进行关联,就容易出现关联错误,严重的会导致最终整条航迹的错误关联。However, the positioning error is too large at the beginning of the track, and the filter has not yet reached a stable convergence effect. Therefore, if the track measurement prediction value and the actual measurement are used for correlation at this time, correlation errors are likely to occur, which may lead to incorrect correlation of the entire track in severe cases.

发明内容Summary of the invention

本申请实施例的主要目的在于提出一种固定单站的多目标数据关联跟踪方法和相关装置,旨在提高多目标跟踪中数据关联的准确度。The main purpose of the embodiments of the present application is to propose a fixed single-station multi-target data association tracking method and related devices, aiming to improve the accuracy of data association in multi-target tracking.

第一方面,本申请实施例提供了一种固定单站的多目标数据关联跟踪方法,所述方法包括以下步骤:获取至少两个目标物体在目标时刻的第一实际观测值集合;根据所述目标时刻所处的目标时间段确定每个所述目标物体的关联中心值;基于联合极大似然估计模型,根据所述关联中心值从所述第一实际观测值集合中确定每个所述目标物体的第二实际观测值集合;基于单目标跟踪模型,根据所述第二实际观测值集合确定每个所述目标物体在所述目标时刻的运动状态信息,所述运动状态信息用于确定每个所述目标物体在所述目标时间段中的航迹。In a first aspect, an embodiment of the present application provides a multi-target data association tracking method for a fixed single station, the method comprising the following steps: obtaining a first set of actual observation values of at least two target objects at a target moment; determining an association center value of each of the target objects based on a target time period in which the target moment is located; based on a joint maximum likelihood estimation model, determining a second set of actual observation values of each of the target objects from the first set of actual observation values based on the association center value; based on a single target tracking model, determining motion state information of each of the target objects at the target moment based on the second set of actual observation values, the motion state information being used to determine the track of each of the target objects in the target time period.

第二方面,本申请实施例提供了一种无源定位系统,所述无源定位系统包括存储器、处理器、存储在所述存储器上并可在所述处理器上运行的程序以及用于实现所述处理器和所述存储器之间的连接通信的数据总线,所述程序被所述处理器执行时实现前述方法的步骤。In a second aspect, an embodiment of the present application provides a passive positioning system, which includes a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for realizing connection and communication between the processor and the memory, wherein the program implements the steps of the aforementioned method when executed by the processor.

第三方面,本申请实施例提供了一种存储介质,用于计算机可读存储,所述存储介质存储有一个或者多个程序,所述一个或者多个程序可被一个或者多个处理器执行,以实现前述方法的步骤。In a third aspect, an embodiment of the present application provides a storage medium for computer-readable storage, wherein the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the aforementioned method.

本申请提出的基于信息熵的联合极大似然数据关联方法和相关装置,通过根据至少两个目标物体在目标时刻所处的目标时间段,确定在该目标时间段的关联中心值,根据每个目标物体的第二实际观测值集合确定每个目标物体在目标时刻的运动状态信息,并进一步对每个目标物体的跟踪,可以实现在航迹的不同阶段根据不同阶段航迹的不同特点使用不同的关联中心值从至少两个目标物体的第一实际观测值集合中确定每个目标物体的第二实际观测值集合,从而更准确地对多目标物体的观测值进行数据关联。The information entropy-based joint maximum likelihood data association method and related devices proposed in the present application determine the association center value in the target time period according to the target time period in which at least two target objects are located at the target moment, determine the motion state information of each target object at the target moment according to the second actual observation value set of each target object, and further track each target object. It is possible to use different association center values at different stages of the track according to the different characteristics of the track at different stages to determine the second actual observation value set of each target object from the first actual observation value set of at least two target objects, thereby more accurately performing data association on the observation values of multiple target objects.

附图说明BRIEF DESCRIPTION OF THE DRAWINGS

为了更清楚地说明本申请实施例技术方案,下面将对实施例描述中所需要使用的附图作简单地介绍,显而易见地,下面描述中的附图是本申请的一些实施例,对于本领域普通技术人员来讲,在不付出创造性劳动的前提下,还可以根据这些附图获得其他的附图。In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

图1为本申请实施例提供的固定单站无源定位多目标跟踪方法的架构示意图;FIG1 is a schematic diagram of the architecture of a fixed single-station passive positioning multi-target tracking method provided in an embodiment of the present application;

图2为本申请实施例提供的固定单站的多目标数据关联跟踪方法的一个步骤流程示意图;FIG2 is a schematic diagram of a step flow chart of a method for multi-target data association tracking at a fixed single station provided in an embodiment of the present application;

图3为本申请实施例提供的固定单站的多目标数据关联方法的架构示意图;FIG3 is a schematic diagram of the architecture of a multi-target data association method for a fixed single station provided in an embodiment of the present application;

图4为本申请实施例提供的固定单站的多目标数据关联跟踪方法的另一步骤流程示意图;FIG4 is a schematic diagram of another step flow chart of the method for multi-target data association tracking of a fixed single station provided in an embodiment of the present application;

图5为本申请实施例提供的固定单站的多目标数据关联跟踪方法的另一步骤流程示意图;FIG5 is a schematic diagram of another step flow chart of the method for multi-target data association tracking of a fixed single station provided in an embodiment of the present application;

图6为本申请实施例提供的固定单站的多目标数据关联跟踪方法的另一步骤流程示意图;FIG6 is a schematic diagram of another step flow chart of the method for multi-target data association tracking of a fixed single station provided in an embodiment of the present application;

图7为本申请实施例提供的固定单站的多目标数据关联跟踪方法的另一步骤流程示意图;FIG7 is a schematic diagram of another step flow chart of the method for multi-target data association tracking of a fixed single station provided in an embodiment of the present application;

图8为本申请实施例提供的固定单站的多目标数据关联跟踪方法的另一步骤流程示意图;FIG8 is a schematic diagram of another step flow chart of the method for multi-target data association tracking of a fixed single station provided in an embodiment of the present application;

图9为本申请实施例提供的固定单站的多目标数据关联跟踪方法的应用场景中的一个真实轨迹图;FIG9 is a real trajectory diagram in an application scenario of the multi-target data association tracking method for a fixed single station provided in an embodiment of the present application;

图10为本申请实施例提供的固定单站的多目标数据关联跟踪方法的应用场景中的一个真实和估计轨迹图;FIG10 is a diagram of real and estimated trajectories in an application scenario of the method for multi-target data association tracking at a fixed single station provided by an embodiment of the present application;

图11为本申请实施例提供的固定单站的多目标数据关联跟踪方法的应用场景中的一个八个目标的均方根误差图;FIG11 is a root mean square error diagram of eight targets in an application scenario of the multi-target data association tracking method of a fixed single station provided in an embodiment of the present application;

图12为本申请实施例提供的固定单站的多目标数据关联跟踪方法的应用场景中的一个八个目标平均位置误差图;FIG12 is a graph showing average position errors of eight targets in an application scenario of the method for multi-target data association tracking at a fixed single station provided by an embodiment of the present application;

图13为本申请实施例提供的无源定位系统的结构示意图。FIG. 13 is a schematic diagram of the structure of a passive positioning system provided in an embodiment of the present application.

具体实施方式Detailed ways

雷达数据处理与雷达信号处理都属于现代雷达系统中的重要组成部分。信号处理是用来检测目标的,利用一定的方法获取目标的各种有用信息,如距离、速度和目标的形状等。而数据处理则可以进一步对目标的点迹和航迹进行处理,预测目标未来时刻的位置,形成可靠的目标航迹,从而实现对目标的实时跟踪。Radar data processing and radar signal processing are both important components of modern radar systems. Signal processing is used to detect targets and obtain various useful information about targets, such as distance, speed, and shape, using certain methods. Data processing can further process the target's point traces and tracks, predict the target's position at future times, form a reliable target track, and thus achieve real-time tracking of the target.

雷达数据处理包括点迹凝聚,航迹起始,目标跟踪,多目标关联等几个主要环节。它研究的两个基本问题是不同环境下的点迹与点迹、点迹与航迹的关联问题。前者涉及航迹起始,注重点迹相关范围的控制和相关算法的选取;后者则涉及目标跟踪,注重目标运动模型和滤波算法的应用。雷达数据处理的目的是利用雷达提供的目标的信息估计目标的航迹并给出目标在下一时刻的位置。Radar data processing includes several main links, such as point track condensation, track initiation, target tracking, and multi-target association. The two basic problems it studies are the association between point tracks and point tracks, and point tracks and tracks in different environments. The former involves track initiation, focusing on the control of the point track correlation range and the selection of related algorithms; the latter involves target tracking, focusing on the application of target motion models and filtering algorithms. The purpose of radar data processing is to use the target information provided by the radar to estimate the target's track and give the target's position at the next moment.

雷达数据处理过程主要包括数据预处理、航迹起始、数据关联、跟踪滤波、航迹消亡以及质量评估等。The radar data processing process mainly includes data preprocessing, track initiation, data association, tracking filtering, track disappearance and quality assessment.

雷达数据处理的输入数据也叫观测,观测并不是雷达直接扫描得到的数据,而是将雷达扫描到的数据首先经过雷达信号处理,再经过数据录取器得到的数据。一般观测包括雷达扫描周期、雷达扫描批次、每批次扫描到的目标的数目以及每个目标的具体信息(径向距离、方位角、俯仰角)。在实际工程中,观测一般掺杂着噪声的污染,这些污染主要来自以下几个方面:The input data of radar data processing is also called observation. Observation is not the data directly scanned by radar, but the data scanned by radar is first processed by radar signal and then obtained by data acquisition. General observation includes radar scanning cycle, radar scanning batch, the number of targets scanned in each batch and the specific information of each target (radial distance, azimuth, pitch angle). In actual engineering, observation is generally mixed with noise pollution, which mainly comes from the following aspects:

1)扫描过程中存在的随机的虚警;1) Random false alarms during the scanning process;

2)虚假目标产生的杂波;2) Clutter generated by false targets;

3)干扰目标;3) Interference target;

4)诱饵等。4) Bait, etc.

虽然现代雷达信号处理技术得到了很大的发展,但即使经过信号处理后的观测中还是会参杂一些干扰,而且一般观测数据数量较大,对后续计算机存储和处理方面的要求较高。数据预处理即是在观测数据进行起始、关联等其它数据处理过程之前先进行一个数据的筛选,将那些不在门限之内的数据剔除,只有经过所有判决门限的数据才被保留。Although modern radar signal processing technology has made great progress, there will still be some interference in the observation even after signal processing, and the amount of observation data is generally large, which places high requirements on subsequent computer storage and processing. Data preprocessing is to screen the data before the observation data is started, associated, or other data processing processes, and those data that are not within the threshold are eliminated. Only data that passes all judgment thresholds is retained.

观测数据预处理的好处在于使得后续数据处理过程中数据的规模明显减小,计算量大幅下降,在一定程度上能够减轻计算机的负担,提高数据处理的速度和目标跟踪的精度,同时使虚假航迹形成的可能性降低。The benefit of observation data preprocessing is that it can significantly reduce the size of data in the subsequent data processing process and greatly reduce the amount of calculation. To a certain extent, it can reduce the burden on the computer, improve the speed of data processing and the accuracy of target tracking, and reduce the possibility of false tracks.

波门是数据处理过程中很重要的一个概念,航迹起始、数据关联等过程中都将用到这个概念。波门其实就是一块区域,一般分为初始波门和相关波门。Gate is a very important concept in data processing, and it is used in track initiation, data association, etc. A gate is actually an area, which is generally divided into initial gate and related gate.

初始波门一般用于航迹起始阶段,是以任意点为中心的一块区域,该区域规定了目标的观测值可能出现的一个空间范围。由于航迹起始时目标距离较远,为了更好的捕获目标,初始波门一般建立大波门。The initial gate is generally used in the initial stage of the track. It is an area centered on an arbitrary point, which specifies a spatial range where the target's observation value may appear. Since the target is far away at the beginning of the track, in order to better capture the target, the initial gate is generally established as a large gate.

所谓相关波门,是指以被跟踪的目标的预测值为中心的一个空间区域,此区域规定了被跟踪目标的观测值可能出现的范围。相关波形的形状和尺寸的确定准则是,一方面要使落入波门内的真实观测有很高的概率,另一方面不允许相关波门内有过多无关观测点迹。一般相关波门的尺寸应该与目标类型相互匹配,比如固定目标的波门一般只取决于观测的精度,直线目标的波门就要取决于观测值和预测滤波器的精度,而机动目标的波门还要考虑加速度的因素等。比较常用的相关波门有矩形波门、环形波门、椭圆形波门以及极坐标系下的扇形波门等。The so-called correlation gate refers to a spatial area centered on the predicted value of the tracked target. This area specifies the possible range of the observed value of the tracked target. The shape and size of the correlation waveform are determined by, on the one hand, making the probability of the real observation falling into the gate very high, and on the other hand, not allowing too many irrelevant observation points in the correlation gate. Generally, the size of the correlation gate should match the target type. For example, the gate of a fixed target generally only depends on the accuracy of the observation, the gate of a linear target depends on the accuracy of the observed value and the prediction filter, and the gate of a maneuvering target also needs to consider the acceleration factor. The more commonly used correlation gates include rectangular gates, circular gates, elliptical gates, and fan gates in polar coordinate systems.

航迹起始是指建立目标航迹的第一点,也就是从目标落入雷达检测范围内到该目标的航迹建立的过程。航迹起始过程是雷达数据处理过程中重要的一个环节。俗话说“好的开头是成功的一半”,从反面讲,如果航迹起始不成功,航迹的建立就会不顺利,而且很有可能就无法建立可靠的航迹,从而不能实现对目标的正确跟踪。Track initiation refers to the first point of establishing the target track, that is, the process from the target falling into the radar detection range to the target track establishment. The track initiation process is an important link in the radar data processing process. As the saying goes, "well begun is half done." On the contrary, if the track initiation is not successful, the track establishment will not be smooth, and it is very likely that a reliable track cannot be established, thus failing to achieve correct tracking of the target.

航迹起始过程是雷达数据处理过程中重要的环节之一,航迹起始的任务之一是为进入雷达威力区的目标快速地建立航迹,任务之二是要尽可能的避免虚假点迹建立虚假航迹。但是为了避免虚假航迹的建立就要等较长时间进行起始,可见两个任务之间存在一定的矛盾,速度和质量的矛盾,因此两者之间需要寻找一个最佳的折衷。航迹起始算法有很多,比较常用的有直观法、逻辑法、修正的逻辑法等。The track initiation process is one of the important links in the radar data processing process. One of the tasks of track initiation is to quickly establish a track for the target entering the radar power area, and the second task is to avoid false tracks as much as possible. However, in order to avoid the establishment of false tracks, it takes a long time to start. It can be seen that there is a certain contradiction between the two tasks, the contradiction between speed and quality, so it is necessary to find an optimal compromise between the two. There are many track initiation algorithms, and the more commonly used ones are intuitive method, logical method, modified logical method, etc.

在理想的目标运动模型中,总认为观测环境是“干净”的,每次只检测到一个观测值,并且这个观测值就是来自于正被跟踪的目标。但是,在实际的系统中环境并非是理想的。由于观测噪声等因素的存在,可能出现虚警等现象,另外被观测区域存在的随机干扰将导致目标可能出现的区域会出现杂波。总而言之,一次检测可能得到多个观测值,而且在这些观测值中,不知道哪些来自被跟踪的目标,哪些是虚假的观测值。这个因素决定了数据关联过程是雷达数据处理系统中重要的一个环节。In the ideal target motion model, the observation environment is always considered to be "clean", and only one observation value is detected each time, and this observation value comes from the target being tracked. However, in the actual system, the environment is not ideal. Due to the existence of factors such as observation noise, false alarms and other phenomena may occur. In addition, the random interference in the observed area will cause clutter in the area where the target may appear. In short, multiple observation values may be obtained in one detection, and among these observation values, it is unknown which ones come from the tracked target and which ones are false observation values. This factor determines that the data association process is an important link in the radar data processing system.

当雷达扫描区域内只有一个目标,且没有干扰的情况下,目标的相关波门内只会有一个点迹,此时不存在数据关联的问题。但是当雷达扫描区域内出现多个目标,或者存在杂波的情况下,同一点迹就可能落在多个波门内或者同一波门内会出现多个点迹,此时就涉及数据关联的问题。数据关联也就是判断某一时刻雷达观测数据和其它时刻观测数据或者已存在航迹之间的关系,从而实现点迹和航迹配对的过程。When there is only one target in the radar scanning area and there is no interference, there will be only one point track in the relevant wave gate of the target, and there is no problem of data association. However, when there are multiple targets in the radar scanning area, or there is clutter, the same point track may fall into multiple wave gates or multiple point tracks may appear in the same wave gate, which involves the problem of data association. Data association is to determine the relationship between the radar observation data at a certain moment and the observation data at other moments or the existing tracks, so as to achieve the process of pairing the point track and the track.

一般而言,根据互相关联对象的不同,数据关联可分为以下几种情况:Generally speaking, data association can be divided into the following situations according to the different objects that are related to each other:

1)航迹起始:点迹与点迹的互联;1) Track start: the connection between track points;

2)航迹更新:点迹与航迹(航迹预测点)的互联,也可以称为航迹保持;2) Track update: the interconnection between point track and track (track prediction point), which can also be called track keeping;

3)航迹融合:航迹与航迹的互联。3) Track fusion: the interconnection of tracks.

数据关联的方法有很多,大致可以分为两类,一类是贝叶斯类数据关联算法,另一类是极大似然类数据关联算法。其中贝叶斯类算法主要包括最近领域算法、概率数据关联算法等,贝叶斯类算法都是以贝叶斯准则为基础的。而极大似然类算法主要包括航积分叉法和联合极大似然算法等,极大似然类算法都是以观测序列的似然比为基础的。There are many methods for data association, which can be roughly divided into two categories: one is the Bayesian data association algorithm, and the other is the maximum likelihood data association algorithm. Bayesian algorithms mainly include the nearest field algorithm, the probabilistic data association algorithm, etc. Bayesian algorithms are all based on the Bayesian criterion. The maximum likelihood algorithms mainly include the aerial integral bifurcation method and the joint maximum likelihood algorithm, etc., and the maximum likelihood algorithms are all based on the likelihood ratio of the observation sequence.

本申请实施例提供了一种固定单站的多目标数据关联跟踪方法和相关装置,能够实现在航迹的不同阶段根据不同阶段航迹的不同特点使用不同的关联中心值从至少两个目标物体的第一实际观测值集合中确定每个目标物体的第二实际观测值集合,从而更准确地对多目标物体的观测值进行数据关联。The embodiments of the present application provide a fixed single-station multi-target data association tracking method and related devices, which can use different association center values at different stages of the track according to different characteristics of the track at different stages to determine the second actual observation value set of each target object from the first actual observation value set of at least two target objects, thereby more accurately performing data association on the observation values of multiple target objects.

下面将结合本申请实施例中的附图,对本申请实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例是本申请一部分实施例,而不是全部的实施例。基于本申请中的实施例,本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例,都属于本申请保护的范围。The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

附图中所示的流程图仅是示例说明,不是必须包括所有的内容和操作/步骤,也不是必须按所描述的顺序执行。例如,有的操作/步骤还可以分解、组合或部分合并,因此实际执行的顺序有可能根据实际情况改变。The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations/steps, nor must they be executed in the order described. For example, some operations/steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

应当理解,在此本申请说明书中所使用的术语仅仅是出于描述特定实施例的目的而并不意在限制本申请。如在本申请说明书和所附权利要求书中所使用的那样,除非上下文清楚地指明其它情况,否则单数形式的“一”、“一个”及“该”意在包括复数形式。It should be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

还应当理解,在本申请说明书和所附权利要求书中使用的术语“和/或”是指相关联列出的项中的一个或多个的任何组合以及所有可能组合,并且包括这些组合。It should also be understood that the term “and/or” used in the specification and appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

下面结合附图,对本申请的一些实施方式作详细说明。在不冲突的情况下,下述的实施例及实施例中的特征可以相互组合。In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

请参阅图1,图1为本申请实施例提供的固定单站无源定位多目标跟踪方法的架构示意图。Please refer to Figure 1, which is a schematic diagram of the architecture of the fixed single-station passive positioning multi-target tracking method provided in an embodiment of the present application.

无源定位系统获取多个目标物体的在不同时刻的实际观测值集合,实际观测值可以包括方位角信息、方位角变化率信息、多普勒频率信息和多普勒频率变化率信息等。多个目标物体的实际观测值集合中的每个实际观测值不一定与目标物体有一一对应的关系,由于随机扰动和环境噪声的存在,即使确定了初始时刻的实际观测值与每个目标物体的对应关系,也无法确定后续时刻的实际观测值与每个目标物体的对应关系。The passive positioning system obtains a set of actual observation values of multiple target objects at different times. The actual observation values may include azimuth information, azimuth change rate information, Doppler frequency information, Doppler frequency change rate information, etc. Each actual observation value in the set of actual observation values of multiple target objects does not necessarily have a one-to-one correspondence with the target object. Due to the existence of random disturbances and environmental noise, even if the correspondence between the actual observation value at the initial time and each target object is determined, the correspondence between the actual observation value at subsequent times and each target object cannot be determined.

通过数据关联,可以将多目标的实际观测值与每个目标物体进行关联,确定每个目标物体在目标时刻对应的实际观测值。Through data association, the actual observation values of multiple targets can be associated with each target object to determine the actual observation value corresponding to each target object at the target time.

当完成数据关联,确定每个目标物体在目标时刻对应的实际观测值后,多目标跟踪问题转化为单目标跟踪问题,需要对每个目标物体进行机动判决,并利用跟踪门规则和滤波与预测后,确定每个目标物体的航迹起始与终结。After completing data association and determining the actual observation value corresponding to each target object at the target time, the multi-target tracking problem is transformed into a single target tracking problem. It is necessary to make a maneuver decision for each target object and determine the start and end of the track of each target object by using the tracking gate rules, filtering and prediction.

当确定了每个目标物体的航迹起始与终结后,就可以将多个目标物体综合分析,最终确定多个目标物体所构成系统的状态。Once the start and end of the track of each target object are determined, multiple target objects can be comprehensively analyzed to ultimately determine the state of the system composed of the multiple target objects.

基于图1所示的固定单站无源定位多目标跟踪方法的架构示意图,本申请实施例提供了一种联合极大似然数据关联的多目标跟踪方法。Based on the architectural diagram of the fixed single-station passive positioning multi-target tracking method shown in Figure 1, an embodiment of the present application provides a multi-target tracking method with joint maximum likelihood data association.

请参阅图2,本申请实施例提供的固定单站的多目标数据关联跟踪方法的一个步骤流程示意图。Please refer to FIG. 2 , which is a schematic flow chart of a step flow of a method for multi-target data association tracking of a fixed single station provided in an embodiment of the present application.

201、获取至少两个目标物体在目标时刻的第一实际观测值集合。201. Obtain a first actual observation value set of at least two target objects at a target time.

无源定位系统中的观测站对包含至少两个目标物体的运动系统进行观测,通过分析该包含至少两个目标物体的运动系统产生的且被观测站接收的电磁波信号分析得到该包含至少两个目标物体的运动系统在目标时刻的第一实际观测值集合,该第一实际观测值集合表示在目标时刻该包含至少两个目标物体的运动系统对应的实际观测值集合,运动系统中每个目标物体与第一实际观测值集合中每个实际观测值的对应关系没有准确地建立。An observation station in a passive positioning system observes a motion system including at least two target objects, and obtains a first actual observation value set of the motion system including at least two target objects at a target time by analyzing electromagnetic wave signals generated by the motion system including at least two target objects and received by the observation station. The first actual observation value set represents the actual observation value set corresponding to the motion system including at least two target objects at the target time, and the correspondence between each target object in the motion system and each actual observation value in the first actual observation value set is not accurately established.

202、根据目标时刻所处的目标时间段确定每个目标物体的关联中心。202. Determine the association center of each target object according to the target time period at the target moment.

实现对包含至少两个目标物体的运动系统进行定位跟踪需要将表示运动系统整体的实际观测值的第一实际观测值集合中的实际观测值与每个目标物体关联起来,即确定每个目标物体的实际观测值。关联中心是将第一实际观测值集合中的每个实际观测值与每个目标物体建立关联过程中的依据。关联中心不是固定的,而是根据目标物体在目标时刻运动时,该目标时刻所处的目标时间段来动态调整每个目标物体的关联中心的选择。To realize the positioning and tracking of a motion system including at least two target objects, it is necessary to associate the actual observation values in the first actual observation value set representing the actual observation values of the motion system as a whole with each target object, that is, to determine the actual observation value of each target object. The association center is the basis for establishing the association process between each actual observation value in the first actual observation value set and each target object. The association center is not fixed, but is dynamically adjusted according to the target time period of the target moment when the target object moves at the target moment.

203、基于联合极大似然估计模型,根据关联中心值从第一实际观测值集合中确定每个目标物体的第二实际观测值集合。203. Based on the joint maximum likelihood estimation model, determine a second actual observation value set for each target object from the first actual observation value set according to the association center value.

根据包含至少两个目标物体的运动系统的目标时刻所处的目标时间段确定目标时刻的关联中心值后,基于联合极大似然估计模型,将关联中心值作为将第一实际观测值集合中每个实际观测值与每个目标物体建立关联的依据,从第一实际观测值集合中确定每个目标物体的第二实际观测值集合,第二实际观测值集合中每个实际观测值与每个目标物体均有对应关系。After determining the association center value of the target moment according to the target time period in which the target moment of a motion system including at least two target objects is located, based on the joint maximum likelihood estimation model, the association center value is used as the basis for establishing an association between each actual observation value in the first actual observation value set and each target object, and a second actual observation value set for each target object is determined from the first actual observation value set, and each actual observation value in the second actual observation value set has a corresponding relationship with each target object.

204、基于单目标跟踪模型,根据第二实际观测值集合确定每个目标物体在目标时刻的运动状态信息,运动状态信息用于确定每个目标物体在目标时间段中的航迹。204. Based on the single target tracking model, determine the motion state information of each target object at the target time according to the second actual observation value set, and the motion state information is used to determine the track of each target object in the target time period.

将第一实际观测值集合中每个实际观测值与每个目标物体建立关联关系后,多目标跟踪问题转化为了单目标跟踪问题,即对包含至少两个目标物体的运动系统的运动状态的确定转化为对运动系统中每个目标物体的运动状态的确定。基于单目标跟踪模型,根据每个目标物体对应的第二实际观测值集合确定每个目标物体在目标时刻的运动状态信息,并且依据多个时刻的每个目标物体的运动状态信息确定每个目标物体在目标时间段中的航迹,实现对每个目标物体的定位跟踪。After establishing an association relationship between each actual observation value in the first actual observation value set and each target object, the multi-target tracking problem is transformed into a single target tracking problem, that is, the determination of the motion state of a motion system containing at least two target objects is transformed into the determination of the motion state of each target object in the motion system. Based on the single target tracking model, the motion state information of each target object at the target time is determined according to the second actual observation value set corresponding to each target object, and the track of each target object in the target time period is determined according to the motion state information of each target object at multiple times, so as to realize the positioning and tracking of each target object.

基于图2所示的固定单站的多目标数据关联跟踪方法,在步骤202中根据目标时刻所处的目标时间段确定每个目标物体的关联中心可以有多种方式,根据包含至少两个目标物体的运动系统在不同阶段的运动特点和观测值特点在不同阶段确定不同的关联中心进行数据关联可以提高准确性,下面对在不同阶段选择不同的关联中心进行介绍。Based on the multi-target data association tracking method of a fixed single station shown in FIG2 , there are multiple ways to determine the association center of each target object according to the target time period at the target moment in step 202. Determining different association centers for data association at different stages according to the motion characteristics and observation value characteristics of a motion system including at least two target objects at different stages can improve accuracy. The following introduces the selection of different association centers at different stages.

请参阅图3,图3为固定单站的多目标数据关联方法的架构示意图。Please refer to FIG. 3 , which is a schematic diagram of the architecture of a multi-target data association method for a fixed single station.

数据关联是多目标跟踪方案的核心,能够将多目标跟踪问题转化为单目标跟踪,多目标跟踪中的数据关联包括关联预处理、关联中心的选择、信息熵不确定度的计算并关联三个步骤。Data association is the core of the multi-target tracking solution, which can transform the multi-target tracking problem into single target tracking. Data association in multi-target tracking includes three steps: association preprocessing, selection of association center, calculation and association of information entropy uncertainty.

首先是量测信息预处理,设置一个跟踪波门,观测站接收到的所有观测都会经过这个波门,关联波门把误差过大的观测过滤掉,通过关联波门的观测为候选观测。The first step is to preprocess the measurement information. A tracking gate is set up. All observations received by the observation station will pass through this gate. The correlation gate will filter out observations with excessive errors. Observations that pass the correlation gate are candidate observations.

然后是关联中心的选择,由于固定单站的初始定位误差过大,所以在航迹起始阶段使用观测的灰色预测值作为关联中心,待目标跟踪稳定之后再采用观测预测值作为关联中心。Then comes the selection of the association center. Since the initial positioning error of a fixed single station is too large, the observed grey prediction value is used as the association center at the beginning of the track. After the target tracking is stable, the observed prediction value is used as the association center.

确定好了关联中心之后,最后就用一种基于信息熵的不确定度的关联方法将各个目标的观测和关联中心进行匹配构建关联对,每个关联对表示每个目标都会有一个观测和它配对,这就将多目标跟踪问题转化为单目标跟踪问题。After determining the association center, we use an uncertainty association method based on information entropy to match the observations and association centers of each target to construct association pairs. Each association pair means that each target has an observation paired with it, which transforms the multi-target tracking problem into a single-target tracking problem.

数据关联构建好关联对之后,各个目标的跟踪就是单目标跟踪问题,单个目标的跟踪包括目标的初始定位、机动判决、以及滤波。After data association builds the association pairs, the tracking of each target is a single target tracking problem. The tracking of a single target includes the initial positioning of the target, maneuver judgment, and filtering.

在航迹起始时刻,利用各个目标的观测和目标状态的关系可以直接计算出目标的初始状态,以各目标的初始状态为起点,以后时刻各目标航迹的跟踪都是利用观测信息和滤波算法进行估计。At the beginning of the track, the initial state of the target can be directly calculated using the relationship between the observations of each target and the target state. Taking the initial state of each target as the starting point, the tracking of each target track at subsequent moments is estimated using observation information and filtering algorithms.

利用观测和滤波算法对目标状态进行更新前都会做一个目标机动的判决。当检测到目标发生机动,就会自适应调节协方差后再滤波以此完成机动目标的跟踪。Before updating the target state using the observation and filtering algorithm, a judgment of the target maneuver is made. When the target maneuvers are detected, the covariance is adaptively adjusted and then filtered to complete the tracking of the maneuvering target.

最后利用滤波算法以及关联对里的观测,对关联对里的目标进行状态更新估计。Finally, the filtering algorithm and the observations in the associated pairs are used to estimate the state of the targets in the associated pairs.

结合图2与图3所示实施例中在不同阶段使用不同的关联中心,下面基于图2所示实施例对图3所示实施例中的航迹起始阶段进行介绍。In combination with the embodiments shown in FIG. 2 and FIG. 3 , different association centers are used in different stages. The track start stage in the embodiment shown in FIG. 3 is introduced below based on the embodiment shown in FIG. 2 .

请参阅图4,图4为表示航迹起始阶段的固定单站的多目标数据关联跟踪方法的另一步骤流程示意图,图4中步骤202包括步骤2021和步骤2022。Please refer to FIG. 4 , which is a schematic diagram of another step flow chart of a method for multi-target data association tracking of a fixed single station at the initial stage of the track. Step 202 in FIG. 4 includes step 2021 and step 2022 .

2021、当目标时间段表示至少两个目标物体处于航迹起始阶段时,基于灰色预测模型确定每个目标物体在目标时刻的灰色预测值。2021. When the target time period indicates that at least two target objects are at the start stage of the track, the grey prediction value of each target object at the target time is determined based on the grey prediction model.

当至少两个目标物体的当前的目标时刻所处的目标时间段表示航迹起始阶段时,利用已关联成功的前n次量测作为预测准备数据,然后利用灰色预测模型得到一组预测数据。灰色预测模型是一种只需利用少量的、不完全的信息就能建立的数学模型,然后可以用该模型去预测下一步的预测值。当前,典型的灰色预测方法很多,如回归分析等。When the target time period of the current target moment of at least two target objects represents the initial stage of the track, the previous n measurements that have been successfully associated are used as prediction preparation data, and then a set of prediction data is obtained using the gray prediction model. The gray prediction model is a mathematical model that can be established using only a small amount of incomplete information, and then the model can be used to predict the next prediction value. At present, there are many typical gray prediction methods, such as regression analysis.

首先是累计生成,将前时刻的数据依次累加形成一个新的数列,设原始数列为The first step is cumulative generation. The data of the previous moment is accumulated in sequence to form a new sequence. Let the original sequence be

z(0)=(z(0)(0),z(0)(1),…z(0)(k)) (1)z (0) =(z (0) (0),z (0) (1),…z (0) (k)) (1)

一次累加生成表示为:One accumulation generation is expressed as:

GM(1.1)是灰色预测系统之一,是一种一阶微分方程模型,其形式为:GM (1.1) is one of the grey prediction systems and is a first-order differential equation model in the form of:

预测公式如下:The prediction formula is as follows:

由导数定义得:By the definition of derivative:

当Δt取1,所以:When Δt is 1, then:

整理式(4)、式(6)得预测值的求解公式为:Arranging equations (4) and (6) to obtain the solution formula for the predicted value is:

相应的得到k+1时刻的预测值为:The corresponding prediction value at time k+1 is:

2022、确定所述灰色预测值为所述关联中心值。2022. Determine the grey prediction value as the association center value.

在基于灰色预测模型得到灰色预测值后,将灰色预测值作为航迹起始阶段目标物体在目标时刻的关联中心值。After the grey prediction value is obtained based on the grey prediction model, the grey prediction value is used as the correlation center value of the target object at the target time in the initial stage of the track.

基于图4所示实施例,根据关联中心值确定每个物体对应的实际观测值具体使用相关波门从第一实际观测值集合中进行筛选。Based on the embodiment shown in FIG. 4 , the actual observation value corresponding to each object is determined according to the correlation center value and is specifically screened from the first actual observation value set using a correlation gate.

请参阅图5,图5为固定单站的多目标数据关联跟踪方法的另一步骤流程示意图,图5中步骤203包括步骤2031至步骤2033。Please refer to FIG. 5 , which is a schematic diagram of another step flow chart of the multi-target data association tracking method for a fixed single station. Step 203 in FIG. 5 includes steps 2031 to 2033 .

2031、根据灰色预测值确定每个目标物体的相关波门。2031. Determine the relevant wave gate of each target object according to the grey prediction value.

单个目标的观测都应该通过一个相关波门,将每个目标物体在目标时刻的灰色预测值作为每个目标物体的波门中心,确定波门形状后可以确定每个目标物体在目标时刻的相关波门。The observation of a single target should pass through a relevant wave gate, and the gray prediction value of each target object at the target time is used as the wave gate center of each target object. After determining the wave gate shape, the relevant wave gate of each target object at the target time can be determined.

需要说明的是,波门形状的选择可以是多种形状,例如椭圆波门或方形波门,本实施例中固定单站多目标跟踪方案选择椭圆波门作为相关波门。It should be noted that the gate shape can be selected from a variety of shapes, such as an elliptical gate or a square gate. In this embodiment, the fixed single-station multi-target tracking solution selects an elliptical gate as the relevant gate.

2032、根据相关波门从第一实际观测值集合中确定每个目标物体的候选观测值集合。2032. Determine a candidate observation value set for each target object from the first actual observation value set according to the relevant wave gate.

相关波门可以限定参与相关判别的观测数目,把误差过大的观测过滤掉。每个目标物体都存在一个相关波门,依据每个目标物体的相关波门可以从第一实际观测值集合中筛选每个目标物体的候选观测值集合。The correlation gate can limit the number of observations involved in the correlation judgment and filter out observations with too large errors. Each target object has a correlation gate, and the candidate observation value set of each target object can be screened from the first actual observation value set based on the correlation gate of each target object.

2033、基于联合极大似然估计模型,根据候选观测值集合和灰色预测值确定第二实际观测值集合。2033. Based on the joint maximum likelihood estimation model, determine the second actual observation value set according to the candidate observation value set and the grey prediction value.

当确定每个目标物体的候选观测值集合后,基于联合极大似然估计模型,根据每个目标物体的候选观测值集合和灰色预测值确定每个目标物体的第二实际观测值集合。After the candidate observation value set of each target object is determined, a second actual observation value set of each target object is determined according to the candidate observation value set of each target object and the grey prediction value based on the joint maximum likelihood estimation model.

结合图5所示实施例,下面对本申请实施例中根据每个目标物体的候选观测值集合确定每个目标物体的第二实际观测值集合进行介绍。In conjunction with the embodiment shown in FIG. 5 , the following describes how to determine the second actual observation value set for each target object based on the candidate observation value set for each target object in an embodiment of the present application.

请参阅图6,图6中步骤2033包括步骤20331至步骤20333。Please refer to FIG. 6 , in which step 2033 includes steps 20331 to 20333 .

20331、计算灰色预测值与候选观测值集合中每个候选观测值的差值。20331. Calculate the difference between the grey prediction value and each candidate observation value in the candidate observation value set.

设由系统m个目标和n个量测指标组成矩阵为:Suppose the matrix composed of m system objectives and n measurement indicators is:

式中,λij表示第j个目标的第j个量测指标。Where λ ij represents the jth measurement indicator of the jth target.

将矩阵A中的量测值和量测预测值作差,然后取绝对值,记为:Difference the measured value and the measured predicted value in matrix A, and then take the absolute value, recorded as:

式中,Δij表示目标i的第j个量测指标与其对应的预测值之间作差,即In the formula, Δij represents the difference between the jth measurement indicator of target i and its corresponding predicted value, that is,

20332、基于信息熵计算模型,以差值为输入,得到每个候选观测值的扩展不确定度。20332. Based on the information entropy calculation model, the difference is taken as input to obtain the expanded uncertainty of each candidate observation value.

信息熵是对系统无序程度的一种度量,假设系统包含多个测量指标j(j=1,2,…,n),每个目标i(i=1,2,…,m)的单个测量指标所占的比重为pij,则该测量指标的熵的定义为:Information entropy is a measure of the disorder of a system. Assuming that the system contains multiple measurement indicators j (j = 1, 2, ..., n), the proportion of a single measurement indicator of each target i (i = 1, 2, ..., m) is p ij , then the entropy of the measurement indicator is defined as:

时,即各个量测指标占比相同时,最大熵可表示为:when When , that is, when the proportions of various measurement indicators are the same, the maximum entropy can be expressed as:

H=log2mj max (13)H=log 2 m j max (13)

因此,如果测量指标的信息熵越小,则该测量指标提供的信息量越大。Therefore, if the information entropy of a measurement indicator is smaller, the amount of information provided by the measurement indicator is greater.

目标i的第j个量测指标所占的比重为:The proportion of the jth measurement indicator of target i is:

第l个测量指标的扩展不确定度定义为:The expanded uncertainty of the lth measurement indicator is defined as:

若s越大,则说明该测量指标在目标跟踪的过程中的作用越大。The larger s is, the greater the role of the measurement indicator in the target tracking process.

20333、基于联合极大似然估计模型,根据每个有效观测值的扩展不确定度确定第二实际观测值集合。20333. Based on the joint maximum likelihood estimation model, determine the second set of actual observation values according to the expanded uncertainty of each valid observation value.

假设k-1时刻得到了n个目标的状态估计k时刻有mk个量测通过关联门限,记k时刻全部确认量测的集合记为:Assume that the state estimates of n targets are obtained at time k-1 At time k, there are m k measurements that pass the correlation threshold. The set of all confirmed measurements at time k is recorded as:

k时刻确认量测集合中的第j个量测属于目标i的事件记为将各个波门内量测划分为多个可行划分,设The event that the jth measurement in the measurement set at time k is confirmed to belong to target i is recorded as Divide the measurements in each gate into multiple feasible partitions,

为其某个划分。可行划分r内的量测应满足下列要求:The measurements within the feasible partition r shall meet the following requirements:

and

为空集,上式就表示一个测量只能属于一条航迹。 is an empty set, the above formula means that a measurement can only belong to one track.

对于每一个可行划分r,可以定义一个事件For each feasible partition r, we can define an event

θ(r)={划分量测组合r为真} (20)θ(r) = {partition measurement combination r is true} (20)

对于全部可行划分构成的集合可以定义为:The set of all feasible partitions can be defined as:

Γ={r} (21)Γ={r} (21)

利用信息熵的不确定度作为某一量测划分第l个指标与量测预测值第l个指标的度量距离。The uncertainty of information entropy is used as the metric distance between the lth indicator of a certain measurement partition and the lth indicator of the measurement prediction value.

信息熵不确定度的结果会出现复数,对结果取模表示某一量测划分第l个指标与量测预测值对应指标的度量距离,即The result of information entropy uncertainty will appear complex, and the result modulo represents the metric distance between the lth indicator of a certain measurement partition and the corresponding indicator of the measurement prediction value, that is,

改进的信息熵不确定度,更好地体现量测划分某一指标与量测预测的差异。不确定度越小,表明该量测划分第l个指标与量测预测值对应指标越接近。故对某一量测划分所有指标的信息熵不确定度进行联合,信息熵联合不确定值最小的即为最大可能量测划分,即The improved information entropy uncertainty better reflects the difference between a certain indicator of the measurement partition and the measurement prediction. The smaller the uncertainty, the closer the indicator of the measurement partition is to the corresponding indicator of the measurement prediction value. Therefore, the information entropy uncertainty of all indicators of a certain measurement partition is combined, and the one with the smallest information entropy combined uncertainty value is the maximum possible measurement partition, that is,

基于图2至图6所示实施例,根据相关波门从第一实际观测值集合中确定每个目标物体的候选观测值集合,还可以通用添加波门约束的方式从第一实际观测值集合中筛选。Based on the embodiments shown in FIG. 2 to FIG. 6 , the candidate observation value set of each target object is determined from the first actual observation value set according to the relevant wave gate, and can also be screened from the first actual observation value set in a general manner of adding wave gate constraints.

请参阅图7,图2、图4至图6中步骤2032包括步骤20321至步骤20323。Please refer to FIG. 7 . Step 2032 in FIG. 2 , FIG. 4 to FIG. 6 includes steps 20321 to 20323 .

20321、确定每个目标物体在目标时刻的量测指标预测值集合。20321. Determine a set of predicted values of measurement indicators of each target object at a target time.

确定每个目标物体在目标时刻的量测指标预测值集合,量测指标预测值集合包括方位角预测值集合、多普勒频率预测值集合、方位角变化率预测值集合和多普勒频率变化率预测值集合中的至少一种。A measurement indicator prediction value set of each target object at a target time is determined, wherein the measurement indicator prediction value set includes at least one of an azimuth angle prediction value set, a Doppler frequency prediction value set, an azimuth angle change rate prediction value set, and a Doppler frequency change rate prediction value set.

20322、根据至少两个量测指标预测值集合和每个目标物体在目标时刻的前一时刻的量测指标实际值集合确定相关波门对应的波门约束。20322. Determine the gate constraints corresponding to the relevant gates according to at least two sets of predicted measurement index values and a set of actual measurement index values of each target object at a moment before the target moment.

确定至少两个量测指标预测值集合,本实施例中选择方位角预测值集合和多普勒频率预测值集合。At least two measurement indicator prediction value sets are determined. In this embodiment, an azimuth angle prediction value set and a Doppler frequency prediction value set are selected.

在椭圆波门作为相关波门的基础上,增加了对方位角和多普勒频率两种量测指标的波门约束。Based on the elliptical wave gate as the correlation wave gate, wave gate constraints on two measurement indicators, azimuth angle and Doppler frequency, are added.

记k-1时刻各目标已关联量测的方位角多普勒频率为/>k时刻的量测方位角预测为/>量测多普勒频率预测为/>则方位角的约束范围为:The azimuth angle of each target that has been associated and measured at time k-1 is The Doppler frequency is/> The measured azimuth at time k is predicted to be/> The measured Doppler frequency is predicted to be/> Then the constraint range of the azimuth is:

其中βlimit为方位角约束大小。Where β limit is the azimuth constraint size.

多普勒频率的约束范围为:The constraint range of Doppler frequency is:

其中flimit为多普勒频率约束大小。Where f limit is the Doppler frequency constraint.

20323、根据波门约束从第一实际观测值集合中确定候选观测值集合。20323. Determine a candidate observation value set from the first actual observation value set according to the gate constraint.

观测落入椭圆波门且在方位角约束和多普勒频率约束范围内的才为该目标有效量测。若没有观测落入该波门内,则该点用观测的预测值进行状态更新,若连续多次都没有观测落入该航迹上,则记录该航迹消失。根据波门约束从第一实际观测值集合中确定每个目标物体的候选观测值集合。Only observations that fall into the elliptical gate and are within the azimuth constraint and Doppler frequency constraint are valid measurements of the target. If no observation falls into the gate, the point is updated with the predicted value of the observation. If no observation falls on the track for multiple consecutive times, the track is recorded as disappeared. The candidate observation value set for each target object is determined from the first actual observation value set according to the gate constraint.

结合图2与图3所示实施例中在不同阶段使用不同的关联中心,下面基于图2所示实施例对图3所示实施例中的航迹稳定阶段进行介绍。In combination with the embodiments shown in FIG. 2 and FIG. 3 , different association centers are used in different stages. The track stabilization stage in the embodiment shown in FIG. 3 is introduced below based on the embodiment shown in FIG. 2 .

请参阅图8,图8为表示航迹稳定阶段的固定单站的多目标数据关联跟踪方法的另一步骤流程示意图,图8中步骤202包括步骤2023和步骤2024。Please refer to FIG. 8 , which is a schematic flow chart of another step of the multi-target data association tracking method for a fixed single station in the track stabilization phase. Step 202 in FIG. 8 includes step 2023 and step 2024 .

2023、当目标时间段表示至少两个目标物体处于航迹稳定阶段时,基于航迹预测模型确定每个目标物体在目标时刻的观测预测值;2023. When the target time period indicates that at least two target objects are in a stable track phase, determining an observation prediction value of each target object at the target time based on the track prediction model;

当至少两个目标物体的目标时刻处于表示航迹稳定阶段的目标时间段时,基于航迹预测模型,确定每个目标物体在目标时刻的观测预测值。观测预测值是由观测实际值作为输入,基于航迹预测模型得到的。When the target time of at least two target objects is in the target time period representing the track stabilization stage, the observation prediction value of each target object at the target time is determined based on the track prediction model. The observation prediction value is obtained based on the track prediction model using the observation actual value as input.

2024、确定所述观测预测值为所述关联中心值。2024. Determine the observed prediction value as the associated center value.

确定观测预测值后,当目标时间段处于航机稳定阶段时采用观测预测值作为关联中心。After the observation prediction value is determined, it is used as the correlation center when the target time period is in the stable stage of the aircraft.

需要说明的是,图5至图7所示实施例的技术方案均可应用于图8所示实施例表示的航机稳定阶段的技术方案,即图8与图5,图8与图5、图6,图8与图5、图6和图7均可以组合形成类似图5图6和图7所示实施例的技术方案,具体此处不再赘述。It should be noted that the technical solutions of the embodiments shown in Figures 5 to 7 can all be applied to the technical solutions of the aircraft stabilization stage represented by the embodiment shown in Figure 8, that is, Figure 8 and Figure 5, Figure 8 and Figure 5 and Figure 6, Figure 8 and Figure 5, Figure 6 and Figure 7 can all be combined to form technical solutions similar to the embodiments shown in Figures 5, 6 and 7, and the details will not be repeated here.

图2至图8所示实施例组合形成的技术方案的一个应用场景如图9所示。An application scenario of the technical solution formed by combining the embodiments shown in FIG. 2 to FIG. 8 is shown in FIG. 9 .

请参阅图9,图9为本申请实施例提供的固定单站的多目标数据关联跟踪方法的应用场景中的一个真实轨迹图。Please refer to FIG. 9 , which is a real trajectory diagram in an application scenario of the multi-target data association tracking method of a fixed single station provided in an embodiment of the present application.

二维情景下,观测站位于坐标原点,目标距离观测站200-300km,目标辐射源作带有加速度扰动的匀速运动。目标运动总时长为300s。目标辐射源频率为10GHz,目标状态为:观测量包括方位角α、方位角变化率/>多普勒频率f、多普勒频率变化率/>观测噪声标准差分别为:σα=2°、/>σf=1Hz、/>观测周期T=1s,仿真多种运动场景,记录各种场景对性能指标的影响。仿真场景:8个目标带转弯机动的匀速运动,目标的初始状态分别为:In the two-dimensional scenario, the observation station is located at the origin of the coordinate system, the target is 200-300km away from the observation station, and the target radiation source moves at a uniform speed with acceleration disturbance. The total duration of the target movement is 300s. The frequency of the target radiation source is 10GHz, and the target state is: The observed quantities include azimuth angle α, azimuth angle change rate/> Doppler frequency f, Doppler frequency change rate/> The standard deviations of the observation noise are: σ α = 2°, /> σ f = 1 Hz, /> The observation period is T = 1s, and various motion scenarios are simulated to record the impact of various scenarios on performance indicators. Simulation scenario: 8 targets move at a constant speed with turning maneuvers. The initial states of the targets are:

(-20km,0.3km/s,285km,0.1km/s)、(100km,-0.3km/s,285km,0.1km/s)、(-20km, 0.3km/s, 285km, 0.1km/s), (100km, -0.3km/s, 285km, 0.1km/s),

(-10km,0.3km/s,310km,0km/s)、(-10km,0.3km/s,290km,0km/s)、(-10km, 0.3km/s, 310km, 0km/s), (-10km, 0.3km/s, 290km, 0km/s),

(60km,-0.3km/s,285km,0.1km/s)、(-45km,0.3km/s,270km,0.3km/s)、(60km,-0.3km/s,285km,0.1km/s), (-45km,0.3km/s,270km,0.3km/s),

(90km,0.3km/s,305km,-0.2km/s)、(-50km,-0.3km/s,260km,0.2km/s);(90km, 0.3km/s, 305km, -0.2km/s), (-50km, -0.3km/s, 260km, 0.2km/s);

二维平面内,目标运动区间在第一和第二象限,目标1-5做匀速直线运动,目标6做方向调整的匀速直线运动,分别在100s和200s时发生了机动;目标7和目标8时先做一段匀速直线运动,然后做匀速转弯运动,最后再做一段匀速直线运动,转弯率为2.9°/s,转弯半径为10km。In the two-dimensional plane, the target movement range is in the first and second quadrants. Targets 1-5 perform uniform linear motion, and target 6 performs uniform linear motion with direction adjustment. Maneuvers occur at 100s and 200s respectively. Targets 7 and 8 first perform a section of uniform linear motion, then perform uniform turning motion, and finally perform another section of uniform linear motion. The turning rate is 2.9°/s and the turning radius is 10km.

图9为各目标的真实航迹,图10为各个目标的跟踪航迹。通过图9、图10可以看出本文算法对无论是对匀速直线运动目标、匀速转弯的机动目标还是航向角改变的机动目标都能有效地进行跟踪。图11为各个目标的位置均方根误差图,图12为各个目标平均位置误差图。图11清楚地反映了各个目标的跟踪性能,匀速直线运动目标跟踪效果理想,而对机动目标的跟踪误差明显比匀速直线运动目标的高,这是由于机动检测时检测到目标发生机动就自适应调整了协方差矩阵,模糊高斯粒子滤波在较短时间内重新对目标进行跟踪,跟踪误差然后就慢慢变小。图12能够反映整个多目标跟踪系统的性能好坏,清楚地看出本文提出的数据关联算法的有效性。Figure 9 shows the real track of each target, and Figure 10 shows the tracked track of each target. It can be seen from Figures 9 and 10 that the proposed algorithm can effectively track uniform linear motion targets, uniform turning maneuvering targets, and maneuvering targets with changing heading angles. Figure 11 is the root mean square error diagram of the positions of each target, and Figure 12 is the average position error diagram of each target. Figure 11 clearly reflects the tracking performance of each target. The tracking effect of uniform linear motion targets is ideal, while the tracking error of maneuvering targets is significantly higher than that of uniform linear motion targets. This is because the covariance matrix is adaptively adjusted when the target is detected to be maneuvering during maneuver detection. The fuzzy Gaussian particle filter re-tracks the target in a short time, and the tracking error then gradually decreases. Figure 12 can reflect the performance of the entire multi-target tracking system, and clearly shows the effectiveness of the data association algorithm proposed in this paper.

请参阅图13,本申请实施例提供了一种无源定位系统,该无源定位系统包括通过系统总线连接的处理器、存储器和网络接口,其中,存储器可以包括非易失性存储介质和内存储器。Please refer to FIG. 13 . An embodiment of the present application provides a passive positioning system, which includes a processor, a memory, and a network interface connected via a system bus, wherein the memory may include a non-volatile storage medium and an internal memory.

非易失性存储介质可存储操作系统和计算机程序。该计算机程序包括程序指令,该程序指令被执行时,可使得处理器执行任意一种视频数据的处理方法。The non-volatile storage medium can store an operating system and a computer program. The computer program includes program instructions, and when the program instructions are executed, the processor can execute any video data processing method.

处理器用于提供计算和控制能力,支撑整个计算机设备的运行。The processor is used to provide computing and control capabilities and support the operation of the entire computer equipment.

内存储器为非易失性存储介质中的计算机程序的运行提供环境,该计算机程序被处理器执行时,可使得处理器执行任意一种固定单站的多目标数据关联跟踪方法。The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor, the processor can execute any fixed single-station multi-target data association tracking method.

该网络接口用于进行网络通信,如发送分配的任务等。本领域技术人员可以理解,图13中示出的结构,仅仅是与本申请方案相关的部分结构的框图,并不构成对本申请方案所应用于其上的计算机设备的限定,具体的计算机设备可以包括比图中所示更多或更少的部件,或者组合某些部件,或者具有不同的部件布置。The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art will appreciate that the structure shown in FIG13 is only a block diagram of a portion of the structure related to the present application solution, and does not constitute a limitation on the computer device to which the present application solution is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

应当理解的是,处理器可以是中央处理单元(Central Processing Unit,CPU),该处理器还可以是其他通用处理器、数字信号处理器(Digital Signal Processor,DSP)、专用集成电路(Application Specific Integrated Circuit,ASIC)、现场可编程门阵列(Field-Programmable Gate Array,FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。其中,通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。It should be understood that the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

本申请的实施例中还提供一种计算机可读存储介质,所述计算机可读存储介质存储有计算机程序,所述计算机程序中包括程序指令,所述处理器执行所述程序指令,实现本申请实施例提供的任一项固定单站的多目标数据关联跟踪方法。A computer-readable storage medium is also provided in an embodiment of the present application, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and the processor executes the program instructions to implement any fixed single-station multi-target data association tracking method provided in an embodiment of the present application.

其中,所述计算机可读存储介质可以是前述实施例所述的计算机设备的内部存储单元,例如所述计算机设备的硬盘或内存。所述计算机可读存储介质也可以是所述计算机设备的外部存储设备,例如所述计算机设备上配备的插接式硬盘、智能存储卡(SmartMedia Card,SMC)、安全数字(Secure Digital,SD)卡、闪存卡(Flash Card)等。The computer-readable storage medium may be an internal storage unit of the computer device described in the above embodiment, such as a hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the computer device.

以上所述,仅为本申请的具体实施方式,但本申请的保护范围并不局限于此,任何熟悉本技术领域的技术人员在本申请揭露的技术范围内,可轻易想到各种等效的修改或替换,这些修改或替换都应涵盖在本申请的保护范围之内。因此,本申请的保护范围应以权利要求的保护范围为准。The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims (3)

1.一种固定单站的多目标数据关联跟踪方法,其特征在于,包括:1. A method for multi-target data association tracking of a fixed single station, characterized by comprising: 获取至少两个目标物体在目标时刻的第一实际观测值集合;Acquire a first set of actual observation values of at least two target objects at a target time; 根据所述目标时刻所处的目标时间段确定每个所述目标物体的关联中心值,包括:Determining the association center value of each target object according to the target time period in which the target moment is located includes: 当所述目标时间段表示至少两个所述目标物体处于航迹起始阶段时,基于灰色预测模型确定每个所述目标物体在所述目标时刻的灰色预测值;确定所述灰色预测值为所述关联中心值;When the target time period indicates that at least two target objects are in the initial stage of the track, determining a grey prediction value of each target object at the target time based on a grey prediction model; determining the grey prediction value as the correlation center value; 当所述目标时间段表示至少两个所述目标物体处于航迹稳定阶段时,基于航迹预测模型确定每个所述目标物体在所述目标时刻的观测预测值;确定所述观测预测值为所述关联中心值;When the target time period indicates that at least two of the target objects are in a stable track phase, determining an observation prediction value of each of the target objects at the target time based on a track prediction model; determining the observation prediction value as the correlation center value; 基于联合极大似然估计模型,根据所述关联中心值从所述第一实际观测值集合中确定每个所述目标物体的第二实际观测值集合,包括:Based on the joint maximum likelihood estimation model, determining a second actual observation value set for each of the target objects from the first actual observation value set according to the association center value, comprising: 根据所述灰色预测值确定每个所述目标物体的相关波门;根据所述相关波门从所述第一实际观测值集合中确定每个所述目标物体的候选观测值集合;基于所述联合极大似然估计模型,根据所述候选观测值集合和所述灰色预测值确定所述第二实际观测值集合,包括:计算所述灰色预测值与所述候选观测值集合中每个候选观测值的差值;基于信息熵计算模型,以所述差值为输入,得到所述每个候选观测值的扩展不确定度;基于所述联合极大似然估计模型,根据所述每个有效观测值的扩展不确定度确定所述第二实际观测值集合;Determine the relevant wave gate of each of the target objects according to the grey prediction value; determine the candidate observation value set of each of the target objects from the first actual observation value set according to the relevant wave gate; determine the second actual observation value set according to the candidate observation value set and the grey prediction value based on the joint maximum likelihood estimation model, including: calculating the difference between the grey prediction value and each candidate observation value in the candidate observation value set; based on the information entropy calculation model, take the difference as input to obtain the expanded uncertainty of each candidate observation value; based on the joint maximum likelihood estimation model, determine the second actual observation value set according to the expanded uncertainty of each valid observation value; 根据所述观测预测值确定每个所述目标物体的相关波门;根据所述相关波门从所述第一实际观测值集合中确定每个所述目标物体的候选观测值集合;基于所述联合极大似然估计模型,根据所述候选观测值集合和所述观测预测值确定所述第二实际观测值集合,包括:计算所述观测预测值与所述候选观测值集合中每个候选观测值的差值;基于信息熵计算模型,以所述差值为输入,得到所述每个候选观测值的扩展不确定度;基于所述联合极大似然估计模型,根据所述每个有效观测值的扩展不确定度确定所述第二实际观测值集合;Determine the relevant wave gate of each of the target objects according to the observation prediction value; determine the candidate observation value set of each of the target objects from the first actual observation value set according to the relevant wave gate; determine the second actual observation value set according to the candidate observation value set and the observation prediction value based on the joint maximum likelihood estimation model, including: calculating the difference between the observation prediction value and each candidate observation value in the candidate observation value set; based on the information entropy calculation model, take the difference as input to obtain the expanded uncertainty of each candidate observation value; based on the joint maximum likelihood estimation model, determine the second actual observation value set according to the expanded uncertainty of each valid observation value; 所述根据所述相关波门从所述第一实际观测值集合中确定每个所述目标物体的候选观测值集合,包括:The step of determining a candidate observation value set for each target object from the first actual observation value set according to the correlation gate comprises: 确定每个所述目标物体在所述目标时刻的量测指标预测值集合,所述量测指标预测值集合包括方位角预测值集合、多普勒频率预测值集合、方位角变化率预测值集合和多普勒频率变化率预测值集合中的至少一种;根据所述量测指标预测值集合和每个所述目标物体在目标时刻的前一时刻的量测指标实际值集合确定所述相关波门对应的波门约束;根据所述波门约束从所述第一实际观测值集合中确定所述候选观测值集合;Determine a set of measurement index prediction values for each target object at the target time, wherein the set of measurement index prediction values includes at least one of an azimuth angle prediction value set, a Doppler frequency prediction value set, an azimuth angle change rate prediction value set, and a Doppler frequency change rate prediction value set; determine a gate constraint corresponding to the relevant gate according to the set of measurement index prediction values and a set of actual measurement index values of each target object at a moment before the target time; determine the candidate observation value set from the first actual observation value set according to the gate constraint; 基于单目标跟踪模型,根据所述第二实际观测值集合确定每个所述目标物体在所述目标时刻的运动状态信息,所述运动状态信息用于确定每个所述目标物体在所述目标时间段中的航迹。Based on the single target tracking model, the motion state information of each target object at the target time is determined according to the second actual observation value set, and the motion state information is used to determine the track of each target object in the target time period. 2.一种无源定位系统,其特征在于,所述无源定位系统包括存储器、处理器、存储在所述存储器上并可在所述处理器上运行的程序以及用于实现所述处理器和所述存储器之间的连接通信的数据总线,所述程序被所述处理器执行时实现如权利要求1所述的固定单站的多目标数据关联跟踪方法的步骤。2. A passive positioning system, characterized in that the passive positioning system includes a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for realizing connection and communication between the processor and the memory, wherein when the program is executed by the processor, the steps of the multi-target data association tracking method of a fixed single station as described in claim 1 are realized. 3.一种存储介质,用于计算机可读存储,其特征在于,所述存储介质存储有一个或者多个程序,所述一个或者多个程序可被一个或者多个处理器执行,以实现权利要求1所述的固定单站的多目标数据关联跟踪方法的步骤。3. A storage medium for computer-readable storage, characterized in that the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the fixed single-station multi-target data association tracking method as described in claim 1.
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