阅读说明：本技术 一种基于希尔排序的距离差分多扩展目标点迹凝聚方法 (Distance difference multi-extension target point trace aggregation method based on Hill sorting ) 是由 王长杰 王伟 龙腾 刘泉华 于 2019-08-01 设计创作，主要内容包括：本发明公开了一种基于希尔排序的距离差分多扩展目标点迹凝聚方法,涉及目标检测跟踪技术领域,是一种性能稳定、计算效率高的目标点迹凝聚方法。包括如下步骤：获取目标的量测点迹集合。选取参考点,计算点迹集合中每个点迹的曼哈顿距离。对所有点迹的曼哈顿距离采用希尔排序法进行升序排列,获得升序序列。对升序序列进行差分运算,获得差分序列。对差分序列进行点迹划分检测,获得不同目标点迹集合。(the invention discloses a distance difference multi-extension target point trace condensation method based on Hill sorting, relates to the technical field of target detection and tracking, and is a target point trace condensation method with stable performance and high calculation efficiency. The method comprises the following steps: and acquiring a measuring trace set of the target. And selecting a reference point, and calculating the Manhattan distance of each point trace in the point trace set. And performing ascending arrangement on the Manhattan distances of all the point traces by adopting a Hill sorting method to obtain an ascending sequence. And carrying out differential operation on the ascending sequence to obtain a differential sequence. And carrying out trace point division detection on the difference sequence to obtain different target trace point sets.)

1. a distance difference multi-extension target point trace aggregation method based on Hill sorting is characterized by comprising the following steps:

Acquiring a measuring trace set of a target;

Selecting a reference point, and calculating the Manhattan distance of each point trace in the point trace set relative to the reference point;

Performing ascending arrangement on the Manhattan distances of all the point traces by adopting a Hill sorting method to obtain ascending sequences;

carrying out differential operation on the ascending sequence to obtain a differential sequence;

And carrying out trace point division detection on the difference sequence to obtain different target trace point sets.

2. The method of claim 1, wherein the selecting a reference point and calculating a manhattan distance of each trace in the set of traces relative to the reference point comprises: and selecting an original point as a reference point, and calculating the 1-norm distance of each point trace, namely the Manhattan distance.

3. The method according to claim 1, wherein the differential operation is performed on the ascending sequence to obtain a differential sequence, specifically:

the ascending sequence is Dsort ═ d1, d2, …, dn }, wherein n is the number of the point traces in the point trace set, and d 1-dn are the Manhattan distances of the point traces arranged in the ascending sequence;

performing difference operation on the ascending sequence Dsort, namely subtracting a previous element from a next element in the Dsort to obtain a difference sequence delta D ═ delta D1, delta D2, … and delta dn-1 }; and delta di is the difference between di +1 and di, i is the serial number in the differential sequence, and i takes the value of 1-n-1.

4. The method according to claim 1, wherein the performing trace point division detection on the differential sequence to obtain different target trace point sets specifically comprises:

Setting an empirical threshold, dividing the differential sequence into 2 or more differential point sets by taking differential points which are greater than or equal to the empirical threshold as dividing points in the differential sequence, wherein the trace points corresponding to the differential points in each differential point set are trace points belonging to the same target.

Technical Field

The invention relates to the technical field of target detection and tracking, in particular to a distance differential multiple expansion target point trace aggregation method based on Hill sorting.

Background

In a traditional low-resolution radar, most of energy of target echoes is concentrated in a distance resolution unit, only one detection point is formed after target detection is carried out, and information of the point is extracted to be used for follow-up target tracking. Along with the functions and performance requirements of precise guidance, multi-target precise measurement, tracking and identification of the broadband phased array radar, the radar resolution is continuously improved, and for large targets such as civil aircrafts, vehicles and the like, the size of the radar spans a plurality of distance units and a plurality of azimuth units and a plurality of scattering points exist.

the parameter estimation of the multi-extension targets is generally realized through point trace aggregation, and the first problem to be solved is measurement point trace division, that is, measurement point traces of the same target are divided into the same subset, and measurement point traces of different targets can be accurately distinguished. The existing trace point agglomeration algorithm comprises a K-means algorithm, a connected domain judgment method and the like, wherein the K-means algorithm has the defects of unstable performance and more external influence, and the connected domain judgment method has the defect of larger calculated amount.

therefore, the research of a stable and high-calculation-efficiency target point trace condensation algorithm is of great significance for realizing multi-target accurate measurement and tracking.

Disclosure of Invention

in view of this, the invention provides a distance difference multi-extension target point trace aggregation method based on hilt sorting, which is a target point trace aggregation method with stable performance and high calculation efficiency.

In order to achieve the purpose, the technical scheme of the invention is as follows: a distance difference multi-extension target point trace aggregation method based on Hill sorting comprises the following steps:

And acquiring a measuring trace set of the target.

And selecting a reference point, and calculating the Manhattan distance of each point trace in the point trace set relative to the reference point.

And performing ascending arrangement on the Manhattan distances of all the point traces by adopting a Hill sorting method to obtain an ascending sequence.

and carrying out differential operation on the ascending sequence to obtain a differential sequence.

And carrying out trace point division detection on the difference sequence to obtain different target trace point sets.

Further, selecting a reference point, and calculating a manhattan distance of each point trace in the point trace set relative to the reference point, specifically: and selecting an original point as a reference point, and calculating the 1-norm distance of each point trace, namely the Manhattan distance.

Further, performing differential operation on the ascending sequence to obtain a differential sequence, specifically:

The ascending sequence is Dsort { d1, d2, …, dn }, where n is the number of dot traces in the dot trace set, and d1 to dn are the manhattan distances of the dot traces arranged in ascending order.

Performing differential operation on the ascending sequence Dsort, namely subtracting a previous element from a next element in the Dsort to obtain a differential sequence delta D ═ delta D1, delta D2, … and delta dn-1; and delta di is the difference between di +1 and di, i is the serial number in the differential sequence, and i takes the value of 1-n-1.

further, performing trace point division detection on the differential sequence to obtain different target trace point sets, specifically:

Setting an empirical threshold, dividing the differential sequence into 2 or more differential point sets by taking differential points which are greater than or equal to the empirical threshold as segmentation points in the differential sequence, wherein point traces corresponding to the differential points in each differential point set are point traces belonging to the same target.

has the advantages that:

the invention provides a distance difference multi-expansion target point trace aggregation method based on Hill sorting, which is characterized in that the Manhattan distance of each trace is calculated aiming at the collected target measurement trace set, the ascending sequence of the Manhattan distance is obtained by adopting the Hill sorting algorithm, then the difference is carried out, and the trace division is carried out based on the difference sequence, the difference sequence of the Manhattan distance can realize the aggregation of the same target point trace, and the method has more stable performance, and greatly improves the calculation efficiency due to the use of the Hill sorting method, so the method is the target point trace aggregation method with stable performance and high calculation efficiency.

Drawings

FIG. 1 is a flowchart illustrating a method for distance-differential multi-extension target trace-based aggregation according to Hill-rank order;

FIG. 2 is a schematic diagram of a Hill sort process in an embodiment of the present invention;

FIG. 3 is a schematic diagram of a target metrology point trace set acquired in an embodiment of the present disclosure;

FIG. 4 is a diagram illustrating a Manhattan distance sequence and an ascending sequence according to an embodiment of the present invention;

FIG. 5 is a schematic diagram of a distance differential sequence according to an embodiment of the present invention;

FIG. 6 is a diagram illustrating trace point division results according to an embodiment of the present invention.

Detailed Description

the invention is described in detail below by way of example with reference to the accompanying drawings.

The invention provides a distance difference multi-extension target point trace aggregation method based on Hill sorting, which comprises the following steps as shown in figure 1:

And S1, acquiring a measuring trace set of the target.

And S2, selecting a reference point, and calculating the Manhattan distance of each point trace in the point trace set relative to the reference point.

Specifically, in the embodiment of the invention, the origin is selected as the reference point, and the 1-norm distance of each point relative to the reference point is calculated, namely the Manhattan distance.

for example, assuming that the spatial position coordinates of the target are (xi, yi), i is 1,2, … N, and the reference point is (x0, y0), the manhattan distance is:

And S3, performing ascending sequence arrangement on the Manhattan distances of all the point traces by using a Hill sequencing method to obtain an ascending sequence.

Specifically, in the embodiment of the present invention, the ascending sequence is Dsort ═ { d1, d2, …, dn }, where n is the number of dot traces in the dot trace set, and d1 to dn are manhattan distances of dot traces arranged in an ascending order.

The hill ordering, also called reduced delta ordering, is an improvement on the insertion ordering. By sequencing the subarrays with equal intervals, the low efficiency of the insertion sequencing under the conditions of more elements and larger disorder degree is avoided. The specific sorting process is shown in fig. 2:

(1) First round of sequencing

Firstly, selecting an integer gap as an interval, and dividing a sequence to be arranged into a plurality of subsequences;

Insert ordering for each subsequence;

(2) subsequent operation

Reducing the gap, repeating the above process;

until the last gap is 1.

And S4, carrying out differential operation on the ascending sequence to obtain a differential sequence.

Specifically, in the embodiment of the present invention, a difference operation is performed on the ascending sequence Dsort, that is, a next element in the Dsort is subtracted from a previous element to obtain a difference sequence Δ D ═ Δ D1, Δ D2, …, Δ dn-1 }; and delta di is the difference between di +1 and di, i is the serial number in the differential sequence, and i takes the value of 1-n-1.

And S5, performing trace point division detection on the differential sequence to obtain different target trace point sets.

specifically, an empirical threshold is set in the embodiment of the present invention, in the difference sequence, the difference point greater than or equal to the empirical threshold is taken as a partition point, the difference sequence is divided into 2 or more difference point sets, and the point trace corresponding to the difference point in each difference point set is the point trace belonging to the same target.

The following examples are given to illustrate the process of the invention:

Assume that the space has two target trace groups, and the trace distribution parameters are shown in table 1 below.

TABLE 1 simulation parameters

	Target group one	target group two
			Group center	(100,100)	(200,200)
Variance of distribution	20	20

The manhattan distance of each trace relative to the origin is calculated and the distance sequence is sorted in ascending order using hill ordering, the result of which is shown in fig. 4 below.

the distance ascending sequence is subjected to difference processing to obtain a distance difference sequence, and as shown in fig. 5, a sudden change of the distance occurs at the edge of the point trace group, thereby resulting in a peak value of the difference result.

through the differential detection in the fourth step, the differential peak value is used to perform the target trace division processing, the trace point before the peak value is the trace point group one, the trace point after the peak value is the trace point group two, and the trace point division result is shown in fig. 6.

The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.