技术领域Technical Field
本发明属于通信技术领域，涉及一种多径信息辅助的城市拐角场景非视距三维多目标定位方法。The invention belongs to the field of communication technology and relates to a non-line-of-sight three-dimensional multi-target positioning method for urban corner scenes assisted by multipath information.
背景技术Background technique
在6G移动通信系统中，更高的频段(毫米波乃至太赫兹)、更宽的带宽使高精度、高分辨率感知成为可能，从而可以在一个系统中实现通信感知一体化，使通信与感知功能相辅相成。2023年，通信感知一体化已被ITU-R确认为IMT-2030的六大应用场景之一。定位技术作为通感一体中重要技术，一方面可以感知周围环境，根据环境进行波束赋形，提高通信质量，另一方面定位技术本身也是多种应用的基础，包括智慧城市、智能交通、城市应急救援等。在楼宇密布的城市环境中，辐射源目标往往受到建筑物遮挡，传统卫星定位技术无法进行精确定位，越来越多研究者将目光投向了使用基站或者移动站点对目标进行定位。在城市“L”型拐角非视距场景下，辐射源目标发射的信号无法通过视距传播到达基站接收端，定位拐角非视距目标较为困难。因此，利用街道两侧反射到达基站的多径信息定位拐角后非视距目标是当前无线定位面临的关键问题。In the 6G mobile communication system, higher frequency bands (millimeter waves and even terahertz) and wider bandwidths make high-precision and high-resolution perception possible, so that communication and perception can be integrated in one system, making communication and perception functions complement each other. In 2023, communication and perception integration has been confirmed by ITU-R as one of the six major application scenarios of IMT-2030. Positioning technology, as an important technology in interawareness, can sense the surrounding environment, perform beamforming according to the environment, and improve communication quality. On the other hand, positioning technology itself is also the basis of many applications, including smart cities, intelligent transportation, and urban emergency rescue. In a densely populated urban environment, the radiation source target is often blocked by buildings, and traditional satellite positioning technology cannot accurately locate it. More and more researchers have turned their attention to using base stations or mobile stations to locate the target. In the "L"-shaped corner non-line-of-sight scenario in the city, the signal emitted by the radiation source target cannot reach the base station receiving end through line-of-sight propagation, and it is difficult to locate the corner non-line-of-sight target. Therefore, using the multipath information reflected from both sides of the street to reach the base station to locate the non-line-of-sight target behind the corner is a key problem facing current wireless positioning.
使用多径信号的测距信息对目标进行定位是城市非视距环境下的常用方法。传统非视距目标定位方法利用单个辐射源目标信号参数，构建定位目标函数，通过优化算法对目标函数进行求解获得目标位置。然而，在实际定位场景下，通常有多个非视距目标需要位置信息估计。当多个非视距目标多径信号同时到达基站接收端，基站接收端难以区分不同目标多径信息，故此对多个非视距目标进行定位十分困难。因此需要研究在建筑信息辅助情况下如何利用多径信息对多个非视距目标进行精准定位。Using the ranging information of multipath signals to locate targets is a common method in urban non-line-of-sight environments. Traditional non-line-of-sight target positioning methods use the target signal parameters of a single radiation source to construct a positioning objective function, and solve the objective function through an optimization algorithm to obtain the target position. However, in actual positioning scenarios, there are usually multiple non-line-of-sight targets that require position information estimation. When the multipath signals of multiple non-line-of-sight targets arrive at the base station receiver at the same time, it is difficult for the base station receiver to distinguish the multipath information of different targets, so it is very difficult to locate multiple non-line-of-sight targets. Therefore, it is necessary to study how to use multipath information to accurately locate multiple non-line-of-sight targets with the assistance of building information.
发明内容Summary of the invention
有鉴于此，本发明的目的在于提供一种多径信息辅助的城市拐角场景非视距三维多目标定位方法。首先，利用基站在不同高度接收多个目标多径信息，使用获得多径水平到达角AAOA、垂直到达角EAOA和到达时间TOA计算所有初始鬼影点位置。其次，利用高斯核函数过滤干扰鬼影点后，运用改进K-means算法确定精炼鬼影点位置。最后，运用鬼影点匹配算法估计多个目标位置，实现城市拐角环境非视距多目标定位。In view of this, the purpose of the present invention is to provide a non-line-of-sight three-dimensional multi-target positioning method for urban corner scenes assisted by multipath information. First, a base station is used to receive multipath information of multiple targets at different heights, and the multipath horizontal arrival angle AAOA, vertical arrival angle EAOA and arrival time TOA are obtained to calculate the positions of all initial ghost points. Secondly, after filtering the interfering ghost points using the Gaussian kernel function, the improved K-means algorithm is used to determine the refined ghost point positions. Finally, the ghost point matching algorithm is used to estimate the positions of multiple targets to achieve non-line-of-sight multi-target positioning in urban corner environments.
为达到上述目的，本发明提供如下技术方案：In order to achieve the above object, the present invention provides the following technical solutions:
该方法包括以下步骤：The method comprises the following steps:
步骤一：使用基站在不同高度hi接收多径信号，i＝1,2,...,M，得到每条多径信号的水平到达角AAOA垂直到达角EAOA和到达时间TOA其中j＝1,2,...,N，M表示基站接收多径的高度个数，N表示一个高度所接收多径信号条数；Step 1: Use the base station to receive multipath signals at different heights hi , i = 1, 2, ..., M, and obtain the horizontal angle of arrival AAOA of each multipath signal Vertical angle of arrival EAOA and arrival time TOA Where j = 1, 2, ..., N, M represents the number of heights at which the base station receives multipaths, and N represents the number of multipath signals received at one height;
步骤二：获取“L”型街道拐角建筑信息，基站位置到街道拐点水平距离为D1，街道宽度为D2；Step 2: Obtain the building information of the “L”-shaped street corner. The horizontal distance from the base station location to the street turning point is D1 , and the street width is D2 ;
步骤三：根据步骤一中接收全部多径信息，计算所有初始鬼影点位置；Step 3: Calculate all initial ghost point positions based on all multipath information received in step 1;
步骤四：考虑到干扰鬼影点对定位精度的影响，使用高斯核函数对干扰鬼影点进行过滤；Step 4: Considering the impact of interfering ghost points on positioning accuracy, use Gaussian kernel function to filter out interfering ghost points;
步骤五：使用改进K-means算法确定精炼鬼影点位置；Step 5: Use the improved K-means algorithm to determine the refined ghost point location;
步骤六：根据步骤二中所获取的街道拐角建筑信息，完成候选目标位置计算；Step 6: Based on the street corner building information obtained in step 2, complete the calculation of candidate target locations;
步骤七：使用所提出的基于鬼影点匹配的多目标定位算法估计目标位置；Step 7: Estimate the target position using the proposed multi-target localization algorithm based on ghost point matching;
步骤八：定位结束，输出估计的目标位置Ws＝[xs,ys,zs]T，s＝1,2,...,S，S表示目标个数。Step 8: Positioning is completed and the estimated target position Ws = [xs , ys , zs ]T is output, s = 1, 2, ..., S, where S represents the number of targets.
进一步，所述步骤三具体为：Further, the step three is specifically as follows:
设基站二维位置为[xB,yB]T，基站位于高度hi时接收到第j条多径的参数分别为和多径信号传播距离其中c为光速，故初始鬼影点可由多径参数得到，表示为公式(1)所示，所有初始鬼影点集合表示为Assume that the two-dimensional position of the base station is [xB ,yB ]T , and the parameters of the j-th multipath received by the base station at height hi are and Multipath signal propagation distance Where c is the speed of light, so the initial ghost point It can be obtained from the multipath parameter, which is expressed as shown in formula (1). The set of all initial ghost points is expressed as
进一步，所述步骤四具体为：Further, the step 4 is specifically as follows:
步骤四-一：对于两个初始鬼影点坐标与这两个初始鬼影点的高斯核为：Step 4-1: For the two initial ghost point coordinates and The Gaussian kernels of these two initial ghost points are:
其中，σ2为所有初始鬼影点间欧式距离的方差；Among them, σ2 is the variance of the Euclidean distance between all initial ghost points;
步骤四-二：计算所有伪散射体的高斯核函数，并将所有高斯核函数取平均作为参考高斯核函数如公式(3)所示；Step 4-2: Calculate the Gaussian kernel functions of all pseudo-scatterers and take the average of all Gaussian kernel functions as the reference Gaussian kernel function as shown in formula (3);
其中，表示任意两个不相同的初始鬼影点的高斯核，不能同时满足i＝u,j＝v，AMN表示任取两个不相同初始鬼影点的情况数量，U是所有初始鬼影点集合；in, Represents any two different initial ghost points The Gaussian kernel cannot satisfy i＝u,j＝v at the same time.AMN represents the number of cases where two different initial ghost points are selected at random, and U is the set of all initial ghost points.
步骤四-三：设置所有初始鬼影点的临近鬼影点初始数量为0，令u＝v＝1；Step 4-3: Set the initial number of adjacent ghost points for all initial ghost points is 0, let u＝v＝1;
步骤四-四：计算初始鬼影点与另一初始鬼影点的高斯核不能同时满足i＝u,j＝v，将与dave比较，若则是的临近鬼影点，同时v＝v+1；Step 4-4: Calculate the initial ghost point With another initial ghost point Gaussian kernel It is impossible to satisfy i＝u,j＝v at the same time. Compared with dave , if but yes The nearby ghost point, At the same time, v = v + 1;
步骤四-五：若v＞N，则令u＝u+1，若u≤M，则令v＝1转入步骤四-四，否则转入步骤四-六；Step 4-5: If v>N, set u=u+1; if u≤M, set v=1 and go to step 4-4; otherwise, go to step 4-6;
步骤四-六：将从小到大排列，选取临近鬼影点数量最少的N/5个初始鬼影点作为干扰鬼影点，将干扰鬼影点从初始鬼影点中剔除，过滤后的初始鬼影点集合为U′＝{U1′,…,Ui′},i＝1,…,4/5MN。Steps 4-6: Arrange from small to large, select N/5 initial ghost points with the least number of adjacent ghost points as interference ghost points, remove the interference ghost points from the initial ghost points, and the filtered initial ghost point set is U′＝{U1 ′,…,Ui ′}, i＝1,…,4/5MN.
进一步，所述步骤五具体为：Further, the step five is specifically as follows:
步骤五-一：将过滤后的初始鬼影点集合U′＝{U1′,…,Ui′},i＝1,…,4/5MN作为K-means算法的输入数据集，令过滤后的初始鬼影点数为N′＝4/5MN，观察初始鬼影点分布给定分簇数目为K；Step 5-1: Use the filtered initial ghost point set U′＝{U1 ′,…,Ui ′}, i＝1,…,4/5MN as the input data set of the K-means algorithm, let the number of filtered initial ghost points be N′＝4/5MN, and observe the distribution of the initial ghost points and give the number of clusters as K;
步骤五-二：计算数据集U′中所有鬼影点与原点之间的欧氏距离，根据欧式距离将数据集U′中所有初始鬼影点进行升序排列得到新的数据集U″＝{U1″,…,Ui″},i＝1,…,N′，根据给定分簇数目将数据集U″中初始鬼影点分为K个部分，将第一部分中第一个初始鬼影点U1″确定为第一个确定聚类中心c1，其后每个部分中的第一个初始鬼影点U″(k-1)N′/K+1暂定为初始聚类中心c′k，k＝2,…,K，令k＝2,t＝1；Step 5-2: Calculate the Euclidean distance between all ghost points in the data set U′ and the origin, and arrange all the initial ghost points in the data set U′ in ascending order according to the Euclidean distance to obtain a new data set U″＝{U1 ″,…,Ui ″}, i＝1,…,N′, divide the initial ghost points in the data set U″ into K parts according to the given number of clusters, and determine the first initial ghost point U1 ″ in the first part as the first determined cluster center c1 , and then the first initial ghost point U″(k-1)N′/K+1 in each part is temporarily determined as the initial cluster center c′k , k＝2,…,K, let k＝2, t＝1;
步骤五-三：计算初始聚类中心c′k＝[xk′,yk′,zk′]T与确定聚类中心cm＝[xm,ym,zm]T,m＝1,…,k-1之间的欧式距离，表示为式(4)所示，给定聚类中心匹配阈值ε，计算初始聚类中心c′k的匹配因子Nk，表示为式(5)所示；Step 5-3: Calculate the Euclidean distance between the initial cluster center c′k = [xk ′, yk ′, zk ′]T and the determined cluster centercm = [xm , ym , zm ]T , m = 1, …, k-1, as shown in formula (4). Given the cluster center matching threshold ε, calculate the matching factor Nk of the initial cluster center c′k , as shown in formula (5);
步骤五-四：若Nk＝0，则初始聚类中心c′k可判定为确定聚类中心ck，k＝k+1,t＝1，转到步骤五-六，反之，则转到步骤五-五；Step 5-4: If Nk = 0, the initial cluster center c′k can be determined as the determined cluster center ck , k = k+1, t = 1, and go to step 5-6; otherwise, go to step 5-5;
步骤五-五：若(k-1)N′/K+1+t＞N′，则确定聚类中心转到五-六，反之，若则将初始鬼影点U″(k-1)N′/K+1+t确定为新的初始聚类中心c′k，令t＝t+1，转到五-三；Step 5-5: If (k-1)N′/K+1+t＞N′, determine the cluster center Go to five-six. Otherwise, if the initial ghost point U″(k-1)N′/K+1+t is determined as the new initial cluster center c′k , let t=t+1, and go to five-three;
步骤五-六：若k＞K，则转到步骤五-七，反之转到步骤五-三；Step 5-6: If k>K, go to step 5-7, otherwise go to step 5-3;
步骤五-七：将簇划分C初始化为Ck＝φ,k＝1,…,K，令a＝0，给定簇划分最大迭代次数为MaxIterations；Steps 5-7: Initialize the cluster partition C to Ck =φ, k = 1, ..., K, set a = 0, and set the maximum number of iterations of the cluster partition to MaxIterations;
步骤五-八：对于输入数据集U′＝{U1′,…,Ui′},i＝1,…,4/5N′，计算样本Ui′和各确定聚类中心ck,k＝1,…,K的距离：将Ui′标记最小的Jik所对应的类别λi，此时更新Steps 5-8: For the input data set U′＝{U1 ′,…,Ui ′}, i＝1,…,4/5N′, calculate the distance between the sample Ui ′ and each determined cluster center ck , k＝1,…,K: Mark the category λi corresponding to the smallestJik in Ui ′, and update
步骤五-九：对于k＝1,2,...,K对Ck中的所有样本冲洗计算新的聚类中心如公式(6)所示，同时，a＝a+1；Steps 5-9: For k = 1, 2, ..., K, flush all samples in Ck and calculate new cluster centers as shown in formula (6), and at the same time, a = a + 1;
步骤五-十：若所有K个聚类中心没有变化则转到步骤五-十一，否则判断a是否等于最大迭代次数，若是等于则转到步骤五-十一，否则转到步骤五-八；Step 5-10: If all K cluster centers have not changed, go to step 5-11, otherwise determine whether a is equal to the maximum number of iterations, if so, go to step 5-11, otherwise go to step 5-8;
步骤五-十一：输出簇划分C＝{C1,C2,...,CK}和聚类中心{c1,…,cK}，将聚类中心{c1,…,ck,…,cK}作为精炼鬼影点Steps 5-11: Output cluster partition C = {C1 , C2 , ..., CK } and cluster centers {c1 , ..., cK }, and use cluster centers {c1 , ..., ck , ..., cK } as refined ghost points
进一步，所述步骤六具体为：Further, the step six is specifically as follows:
步骤六-一：结合“L”型街道拐角建筑信息，基站位置到街道拐点水平距离为D1，街道宽度为D2，以及一次反射对称几何关系可计算精炼鬼影点的初始候选目标位置为计算方式为式(7)所示；Step 6-1: Combined with the building information of the "L"-shaped street corner, the horizontal distance from the base station location to the street turning point is D1 , the street width is D2 , and the first reflection symmetric geometric relationship, the ghost point can be calculated and refined The initial candidate target position is The calculation method is shown in formula (7);
步骤六-二：结合街道建筑信息，使用式(8)对初始候选目标Uc进行筛选，可获得候选目标位置Step 6-2: Combined with street building information, use formula (8) to screen the initial candidate target Uc to obtain the candidate target location
进一步，所述步骤七具体为：Further, the step seven is specifically as follows:
步骤七-一：根据定位实际情况给出基站可能接收到的最高多径反射次数Q，令p＝1,s＝1，鬼影点匹配门限ψ；Step 7-1: According to the actual positioning situation, the maximum number of multipath reflections Q that the base station may receive is given, and p=1, s=1, and the ghost point matching threshold ψ is set;
步骤七-二：根据反射对称几何关系计算候选目标次反射依次对应候选鬼影点计算方式如(9)所示；Step 7-2: Calculate candidate targets based on reflective symmetric geometric relationships The secondary reflections correspond to the candidate ghost points in turn. The calculation method is shown in (9);
步骤七-二：将候选鬼影点集合中所有候选鬼影点与精炼鬼影点UR中所有精炼鬼影点进行匹配计算，表示为式(10)所示，其中表示计算与之间的欧式距离，q＝1,…,Q、k＝1,…,K，精炼鬼影点中UR满足式(10)的鬼影点表示集合Np表示满足式(10)鬼影点个数；Step 7-2: Collect candidate ghost points All candidate ghost points in UR are matched with all refined ghost points inUR , which can be expressed as shown in formula (10), where Representation calculation and The Euclidean distance between them, q = 1, ..., Q, k = 1, ..., K, the ghost points in the refined ghost points UR satisfying formula (10) represent the set Np represents the number of ghost points satisfying formula (10);
步骤七-三：若Np≥2，则候选目标为确定目标位置，且令同时更新精炼鬼影点集合令p＝p+1,s＝s+1，反之，则p＝p+1；Step 7-3: If Np ≥ 2, then the candidate target To determine the target position, let Update the refined ghost point set at the same time Let p＝p+1,s＝s+1, otherwise, p＝p+1;
步骤七-四：若p＞P，则转到步骤七-五，否则转到步骤七-二；Step 7-4: If p>P, go to step 7-5, otherwise go to step 7-2;
步骤七-五：所有目标位置Ws＝[xs,ys,zs]T存入集合W，得到目标位置集合W＝{W1,…,Ws,…WS},s＝1,…,S。Step 7-5: All target positions Ws = [xs , ys , zs ]T are stored in a set W, and a target position set W = {W1 , …, Ws , … WS }, s = 1, …, S is obtained.
本发明的有益效果在于：能够使用单基站在城市拐角非视距环境下，充分利用多径信息对多个信号源目标进行精确定位。其中考虑到干扰鬼影点对精炼鬼影点位置确定的影响，使用高斯核函数过滤干扰鬼影点；考虑到传统K-means算法聚类效果易受到初始聚类中心影响，提出改进K-means算法确定精炼鬼影点位置来提高定位精度；基于多径鬼影点分布特性提出基于鬼影点匹配的目标定位算法完成多个辐射源目标位置估计。在范围为50×100m×20的街道定位场景中，在多径水平到达角AAOA、垂直到达角EAOA和到达时间TOA的测量误差均服从均值为0，标准差为2％的高斯分布情况下，多提定位方法实现了多个非视距目标三维定位且定位平均误差为小于1m。The beneficial effects of the present invention are: it is possible to use a single base station in a non-line-of-sight environment at a corner of a city to fully utilize multipath information to accurately locate multiple signal source targets. Considering the influence of interfering ghost points on the determination of refined ghost point positions, a Gaussian kernel function is used to filter interfering ghost points; considering that the clustering effect of the traditional K-means algorithm is easily affected by the initial clustering center, an improved K-means algorithm is proposed to determine the refined ghost point positions to improve positioning accuracy; based on the distribution characteristics of multipath ghost points, a target positioning algorithm based on ghost point matching is proposed to complete the estimation of the positions of multiple radiation source targets. In a street positioning scenario with a range of 50×100m×20, when the measurement errors of the multipath horizontal arrival angle AAOA, vertical arrival angle EAOA and arrival time TOA all obey a Gaussian distribution with a mean of 0 and a standard deviation of 2%, the multi-proposition positioning method realizes three-dimensional positioning of multiple non-line-of-sight targets and the average positioning error is less than 1m.
本发明的其他优点、目标和特征在某种程度上将在随后的说明书中进行阐述，并且在某种程度上，基于对下文的考察研究对本领域技术人员而言将是显而易见的，或者可以从本发明的实践中得到教导。本发明的目标和其他优点可以通过下面的说明书来实现和获得。Other advantages, objectives and features of the present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
为了使本发明的目的、技术方案和优点更加清楚，下面将结合附图对本发明作优选的详细描述，其中：In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below in conjunction with the accompanying drawings, wherein:
图1为多径信息辅助的城市拐角场景非视距三维多目标定位方法流程图；FIG1 is a flow chart of a method for non-line-of-sight three-dimensional multi-target positioning in an urban corner scene assisted by multipath information;
图2为城市拐角环境多径传播示意图；Figure 2 is a schematic diagram of multipath propagation in an urban corner environment;
图3为多径鬼影点分布规律示意图；FIG3 is a schematic diagram of the distribution law of multipath ghost points;
图4为改进K-means算法流程图；Figure 4 is a flowchart of the improved K-means algorithm;
图5为基于鬼影点匹配的多目标定位算法流程图。FIG5 is a flow chart of a multi-target positioning algorithm based on ghost point matching.
具体实施方式Detailed ways
以下通过特定的具体实例说明本发明的实施方式，本领域技术人员可由本说明书所揭露的内容轻易地了解本发明的其他优点与功效。本发明还可以通过另外不同的具体实施方式加以实施或应用，本说明书中的各项细节也可以基于不同观点与应用，在没有背离本发明的精神下进行各种修饰或改变。需要说明的是，以下实施例中所提供的图示仅以示意方式说明本发明的基本构想，在不冲突的情况下，以下实施例及实施例中的特征可以相互组合。The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner, and the following embodiments and the features in the embodiments can be combined with each other without conflict.
其中，附图仅用于示例性说明，表示的仅是示意图，而非实物图，不能理解为对本发明的限制；为了更好地说明本发明的实施例，附图某些部件会有省略、放大或缩小，并不代表实际产品的尺寸；对本领域技术人员来说，附图中某些公知结构及其说明可能省略是可以理解的。Among them, the drawings are only used for illustrative explanations, and they only represent schematic diagrams rather than actual pictures, and should not be understood as limitations on the present invention. In order to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.
本发明实施例的附图中相同或相似的标号对应相同或相似的部件；在本发明的描述中，需要理解的是，若有术语“上”、“下”、“左”、“右”、“前”、“后”等指示的方位或位置关系为基于附图所示的方位或位置关系，仅是为了便于描述本发明和简化描述，而不是指示或暗示所指的装置或元件必须具有特定的方位、以特定的方位构造和操作，因此附图中描述位置关系的用语仅用于示例性说明，不能理解为对本发明的限制，对于本领域的普通技术人员而言，可以根据具体情况理解上述术语的具体含义。The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right", "front", "back" and the like indicate directions or positional relationships, they are based on the directions or positional relationships shown in the drawings, which are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction. Therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.
如图1所示，本发明所述的多径信息辅助的城市拐角场景非视距三维多目标定位方法，包括以下步骤：As shown in FIG1 , the multipath information-assisted non-line-of-sight three-dimensional multi-target positioning method for urban corner scenes of the present invention comprises the following steps:
步骤一：使用基站在不同高度hi接收多径信号，i＝1,2,...,M，得到每条多径信号的水平到达角AAOA垂直到达角EAOA和到达时间TOA其中j＝1,2,...,N，M表示基站接收多径的高度个数，N表示一个高度所接收多径信号条数；Step 1: Use the base station to receive multipath signals at different heights hi , i = 1, 2, ..., M, and obtain the horizontal angle of arrival AAOA of each multipath signal Vertical angle of arrival EAOA and arrival time TOA Where j = 1, 2, ..., N, M represents the number of heights at which the base station receives multipaths, and N represents the number of multipath signals received at one height;
如图2所示，为城市拐角环境多径传播示意图。As shown in Figure 2, it is a schematic diagram of multipath propagation in an urban corner environment.
步骤二：获取“L”型街道拐角建筑信息，基站位置到街道拐点水平距离为D1，街道宽度为D2；Step 2: Obtain the building information of the “L”-shaped street corner. The horizontal distance from the base station location to the street turning point is D1 , and the street width is D2 ;
步骤三：根据步骤一中接收全部多径信息，计算所有初始鬼影点位置，设基站二维位置为[xB,yB]T，基站位于高度hi时接收到第j条多径的参数分别为和多径信号传播距离其中c为光速，故初始鬼影点可由多径参数得到，表示为公式(1)所示，所有初始鬼影点集合表示为Step 3: Based on all the multipath information received in step 1, calculate the positions of all initial ghost points. Assume that the two-dimensional position of the base station is [xB ,yB ]T . When the base station is at height hi , the parameters of the jth multipath received are and Multipath signal propagation distance Where c is the speed of light, so the initial ghost point It can be obtained from the multipath parameter, which is expressed as shown in formula (1). The set of all initial ghost points is expressed as
多径鬼影点分布如图3所示。The distribution of multipath ghost points is shown in Figure 3.
步骤四：考虑到干扰鬼影点对定位精度的影响，使用高斯核函数对干扰鬼影点进行过滤；Step 4: Considering the impact of interfering ghost points on positioning accuracy, use Gaussian kernel function to filter out interfering ghost points;
步骤四-一：对于两个初始鬼影点坐标与这两个初始鬼影点的高斯核为：Step 4-1: For the two initial ghost point coordinates and The Gaussian kernels of these two initial ghost points are:
其中，σ2为所有初始鬼影点间欧式距离的方差；Among them, σ2 is the variance of the Euclidean distance between all initial ghost points;
步骤四-二：计算所有伪散射体的高斯核函数，并将所有高斯核函数取平均作为参考高斯核函数如公式(3)所示；Step 4-2: Calculate the Gaussian kernel functions of all pseudo-scatterers and take the average of all Gaussian kernel functions as the reference Gaussian kernel function as shown in formula (3);
其中，表示任意两个不相同的初始鬼影点的高斯核，不能同时满足i＝u,j＝v，AMN表示任取两个不相同初始鬼影点的情况数量，U是所有初始鬼影点集合；in, Represents any two different initial ghost points The Gaussian kernel cannot satisfy i＝u,j＝v at the same time.AMN represents the number of cases where two different initial ghost points are selected at random, and U is the set of all initial ghost points.
步骤四-三：设置所有初始鬼影点的临近鬼影点初始数量为0，令u＝v＝1；Step 4-3: Set the initial number of adjacent ghost points for all initial ghost points is 0, let u＝v＝1;
步骤四-四：计算初始鬼影点与另一初始鬼影点的高斯核不能同时满足i＝u,j＝v，将与dave比较，若则是的临近鬼影点，同时v＝v+1；Step 4-4: Calculate the initial ghost point With another initial ghost point Gaussian kernel It is impossible to satisfy i＝u,j＝v at the same time. Compared with dave , if but yes The nearby ghost point, At the same time, v = v + 1;
步骤四-五：若v＞N，则令u＝u+1，若u≤M，则令v＝1转入步骤四-四，否则转入步骤四-六；Step 4-5: If v>N, set u=u+1; if u≤M, set v=1 and go to step 4-4; otherwise, go to step 4-6;
步骤四-六：将从小到大排列，选取临近鬼影点数量最少的N/5个初始鬼影点作为干扰鬼影点，将干扰鬼影点从初始鬼影点中剔除，过滤后的初始鬼影点集合为U′＝{U1′,…,Ui′},i＝1,…,4/5MN。Steps 4-6: Arrange from small to large, select N/5 initial ghost points with the least number of adjacent ghost points as interference ghost points, remove the interference ghost points from the initial ghost points, and the filtered initial ghost point set is U′＝{U1 ′,…,Ui ′}, i＝1,…,4/5MN.
步骤五：使用改进K-means算法确定精炼鬼影点位置；Step 5: Use the improved K-means algorithm to determine the refined ghost point location;
改进K-means算法流程如图4所示；The improved K-means algorithm process is shown in Figure 4;
步骤五-一：将过滤后的初始鬼影点集合U′＝{U1′,…,Ui′},i＝1,…,4/5MN作为K-means算法的输入数据集，令过滤后的初始鬼影点数为N′＝4/5MN，观察初始鬼影点分布给定分簇数目为K；Step 5-1: Use the filtered initial ghost point set U′＝{U1 ′,…,Ui ′}, i＝1,…,4/5MN as the input data set of the K-means algorithm, let the number of filtered initial ghost points be N′＝4/5MN, and observe the distribution of the initial ghost points and give the number of clusters as K;
步骤五-二：计算数据集U′中所有鬼影点与原点之间的欧氏距离，根据欧式距离将数据集U′中所有初始鬼影点进行升序排列得到新的数据集U″＝{U1″,…,Ui″},i＝1,…,N′，根据给定分簇数目将数据集U″中初始鬼影点分为K个部分，将第一部分中第一个初始鬼影点U1″确定为第一个确定聚类中心c1，其后每个部分中的第一个初始鬼影点U″(k-1)N′/K+1暂定为初始聚类中心c′k，k＝2,…,K，令k＝2,t＝1；Step 5-2: Calculate the Euclidean distance between all ghost points in the data set U′ and the origin, and arrange all the initial ghost points in the data set U′ in ascending order according to the Euclidean distance to obtain a new data set U″＝{U1 ″,…,Ui ″}, i＝1,…,N′, divide the initial ghost points in the data set U″ into K parts according to the given number of clusters, and determine the first initial ghost point U1 ″ in the first part as the first determined cluster center c1 , and then the first initial ghost point U″(k-1)N′/K+1 in each part is temporarily determined as the initial cluster center c′k , k＝2,…,K, let k＝2, t＝1;
步骤五-三：计算初始聚类中心c′k＝[xk′,yk′,zk′]T与确定聚类中心cm＝[xm,ym,zm]T,m＝1,…,k-1之间的欧式距离，表示为式(4)所示，给定聚类中心匹配阈值ε，计算初始聚类中心c′k的匹配因子Nk，表示为式(5)所示；Step 5-3: Calculate the Euclidean distance between the initial cluster center c′k = [xk ′, yk ′, zk ′]T and the determined cluster centercm = [xm , ym , zm ]T , m = 1, …, k-1, as shown in formula (4). Given the cluster center matching threshold ε, calculate the matching factor Nk of the initial cluster center c′k , as shown in formula (5);
步骤五-四：若Nk＝0，则初始聚类中心c′k可判定为确定聚类中心ck，k＝k+1,t＝1，转到步骤五-六，反之，则转到步骤五-五；Step 5-4: If Nk = 0, the initial cluster center c′k can be determined as the determined cluster center ck , k = k+1, t = 1, and go to step 5-6; otherwise, go to step 5-5;
步骤五-五：若(k-1)N′/K+1+t＞N′，则确定聚类中心k＝k+1,t＝1，转到五-六，反之，若则将初始鬼影点U″(k-1)N′/K+1+t确定为新的初始聚类中心c′k，令t＝t+1，转到五-三；Step 5-5: If (k-1)N′/K+1+t＞N′, determine the cluster center k＝k+1, t＝1, go to five-six, otherwise, if the initial ghost point U″(k-1)N′/K+1+t is determined as the new initial cluster center c′k , let t＝t+1, go to five-three;
步骤五-六：若k＞K，则转到步骤五-七，反之转到步骤五-三；Step 5-6: If k>K, go to step 5-7, otherwise go to step 5-3;
步骤五-七：将簇划分C初始化为Ck＝φ,k＝1,…,K，令a＝0，给定簇划分最大迭代次数为MaxIterations；Steps 5-7: Initialize the cluster partition C to Ck =φ, k = 1, ..., K, set a = 0, and set the maximum number of iterations of the cluster partition to MaxIterations;
步骤五-八：对于输入数据集U′＝{U1′,…,Ui′},i＝1,…,4/5N′，计算样本Ui′和各确定聚类中心ck,k＝1,…,K的距离：将Ui′标记最小的Jik所对应的类别λi，此时更新Steps 5-8: For the input data set U′＝{U1 ′,…,Ui ′}, i＝1,…,4/5N′, calculate the distance between the sample Ui ′ and each determined cluster center ck , k＝1,…,K: Mark the category λi corresponding to the smallestJik in Ui ′, and update
步骤五-九：对于k＝1,2,...,K对Ck中的所有样本冲洗计算新的聚类中心如公式(6)所示，同时，a＝a+1；Steps 5-9: For k = 1, 2, ..., K, flush all samples in Ck and calculate new cluster centers as shown in formula (6), and at the same time, a = a + 1;
步骤五-十：若所有K个聚类中心没有变化则转到步骤五-十一，否则判断a是否等于最大迭代次数，若是等于则转到步骤五-十一，否则转到步骤五-八；Step 5-10: If all K cluster centers have not changed, go to step 5-11, otherwise determine whether a is equal to the maximum number of iterations, if so, go to step 5-11, otherwise go to step 5-8;
步骤五-十一：输出簇划分C＝{C1,C2,...,CK}和聚类中心{c1,…,cK}，将聚类中心{c1,…,ck,…,cK}作为精炼鬼影点Steps 5-11: Output cluster partition C = {C1 , C2 , ..., CK } and cluster centers {c1 , ..., cK }, and use cluster centers {c1 , ..., ck , ..., cK } as refined ghost points
步骤六：根据步骤二中所获取的街道拐角建筑信息，完成候选目标位置计算；Step 6: Based on the street corner building information obtained in step 2, complete the calculation of candidate target locations;
步骤六-一：根据“L”型街道拐角建筑信息，基站位置到街道拐点水平距离为D1，街道宽度为D2，同时利用一次反射对称几何关系可计算精炼鬼影点的初始候选目标位置为计算方式为式(7)所示；Step 6-1: Based on the building information of the "L"-shaped street corner, the horizontal distance from the base station location to the street turning point is D1 , and the street width is D2 . At the same time, the ghost point can be refined by using the geometric relationship of single reflection symmetry. The initial candidate target position is The calculation method is shown in formula (7);
步骤六-二：结合街道建筑信息，使用式(8)对初始候选目标Uc进行筛选，可获得候选目标位置Step 6-2: Combined with street building information, use formula (8) to screen the initial candidate target Uc to obtain the candidate target location
步骤七：使用所提出的基于鬼影点匹配的多目标定位算法估计目标位置；Step 7: Estimate the target position using the proposed multi-target localization algorithm based on ghost point matching;
基于鬼影点匹配的多目标定位算法流程如图5所示。The process of multi-target positioning algorithm based on ghost point matching is shown in Figure 5.
步骤七-一：根据定位实际情况给出基站可能接收到的最高多径反射次数Q，令p＝1,s＝1，鬼影点匹配门限ψ；Step 7-1: According to the actual positioning situation, the maximum number of multipath reflections Q that the base station may receive is given, and p=1, s=1, and the ghost point matching threshold ψ is set;
步骤七-二：根据反射对称几何关系计算候选目标次反射依次对应候选鬼影点计算方式如(9)所示；Step 7-2: Calculate candidate targets based on reflective symmetric geometric relationships The secondary reflections correspond to the candidate ghost points in turn. The calculation method is shown in (9);
步骤七-二：将候选鬼影点集合中所有候选鬼影点与精炼鬼影点UR中所有精炼鬼影点进行匹配计算，表示为式(10)所示，其中表示计算与之间的欧式距离，q＝1,…,Q、k＝1,…,K，精炼鬼影点中UR满足式(10)的鬼影点表示集合Np表示满足式(10)鬼影点个数；Step 7-2: Collect candidate ghost points All candidate ghost points in UR are matched with all refined ghost points inUR , which can be expressed as shown in formula (10), where Representation calculation and The Euclidean distance between them, q = 1, ..., Q, k = 1, ..., K, the ghost points in the refined ghost points UR satisfying formula (10) represent the set Np represents the number of ghost points satisfying formula (10);
步骤七-三：若Np≥2，则候选目标为确定目标位置，且令同时更新精炼鬼影点集合令p＝p+1,s＝s+1，反之，则p＝p+1；Step 7-3: If Np ≥ 2, then the candidate target To determine the target position, let Update the refined ghost point set at the same time Let p＝p+1,s＝s+1, otherwise, p＝p+1;
步骤七-四：若p＞P，则转到步骤七-五，否则转到步骤七-二；Step 7-4: If p>P, go to step 7-5, otherwise go to step 7-2;
步骤七-五：所有目标位置Ws＝[xs,ys,zs]T存入集合W，得到目标位置集合W＝{W1,…,Ws,…WS},s＝1,…,S。Step 7-5: All target positions Ws = [xs , ys , zs ]T are stored in a set W, and a target position set W = {W1 , …, Ws , … WS }, s = 1, …, S is obtained.
步骤八：定位结束，输出估计的目标位置Ws＝[xs,ys,zs]T，s＝1,2,...,S，S表示目标个数。Step 8: Positioning is completed and the estimated target position Ws = [xs , ys , zs ]T is output, s = 1, 2, ..., S, where S represents the number of targets.
最后说明的是，以上实施例仅用以说明本发明的技术方案而非限制，尽管参照较佳实施例对本发明进行了详细说明，本领域的普通技术人员应当理解，可以对本发明的技术方案进行修改或者等同替换，而不脱离本技术方案的宗旨和范围，其均应涵盖在本发明的权利要求范围当中。Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution, which should be included in the scope of the claims of the present invention.
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