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A Parallel Processing Method for Trajectory Prediction of Multiple Targets in Air

A technology of trajectory prediction and parallel processing, applied in multi-programming devices, resource allocation, etc., can solve the problems of shortening communication time, low processing capacity of computer nodes, and inability to meet the real-time performance of the system, so as to achieve balanced task allocation and reduce communication time Effect

Active Publication Date: 2018-06-26
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] The technical problem to be solved by the present invention is to provide a parallel processing method for multi-target trajectory prediction in the air, considering the horizontal independence and vertical correlation of multi-target trajectory prediction, and propose a parallel processing method based on computer clusters. A processing plan is proposed, and a task allocation strategy based on the state of the aircraft is proposed, which makes the task allocation of each computing node more balanced under the premise of effectively shortening the communication time, and solves the technical problems of low processing capacity of a single computer node and the inability to meet the real-time performance of the system

Method used

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  • A Parallel Processing Method for Trajectory Prediction of Multiple Targets in Air
  • A Parallel Processing Method for Trajectory Prediction of Multiple Targets in Air

Examples

Experimental program
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Effect test

example 1

[0076] Example 1: The target data ph5 arrives (that is, the existing target data arrives and has not disappeared)

[0077] Step 1: Thread0 of the Master node receives the target data through the network interface, puts it into the opened buffer buffer, and receives the data of the ph5 target this time;

[0078] Step 2: Thread1 reads the data ph5 from the buffer, and queries the map table; at this time, there is data of the target ph5 in the map table, so add data to the value of the row where ph5 is located in the map table, and it becomes the form of Figure 5(2), At the same time record the arrival time of the target data;

[0079] Step 3: Check the length len of the data packet in the value of ph5, if len is less than the set sending threshold N, no data will be sent, and the processing of ph5 will end; if len is equal to the sending threshold N, then prepare to send data, first traverse Taskassign table ( Figure 6 ), find the computing node 1 where the target is located,...

example 2

[0085] Example 2: The target data ph6 arrives (that is, the new target data arrives)

[0086] Step 1: Thread0 of the Master node receives the target data through the network interface, and puts it into the opened buffer buffer, and receives the data of the ph6 target this time;

[0087] Step 2: Thread1 reads the data ph6 from the buffer and queries the map table. At this time, the data of the target ph6 does not exist in the map table, so add data to the value of the row where ph6 is located in the map table, and record the arrival time of the target data at the same time;

[0088] Step 3: At this time, the length len of the data packet in the value of ph6 is 1, which is less than the set sending threshold N, then the data will not be sent, and the first round of processing for ph6 is over, and then the data of ph6 will come continuously. If the length is equal to the sending threshold N, use the task scheduling strategy to prepare to send data;

[0089] Step 4: Use the formu...

example 3

[0091] Example 3: Target disappearance processing, taking ph4 as an example

[0092] Step 1: Every time the Master node Thread1 inserts the incoming data ph4 into the map table, it will calculate the difference between the current time and the target timestamp record;

[0093] Step 2: If the difference is greater than N, it is considered that the previous ph4 target has disappeared, delete the previous record from the map table, and update the Taskassign table and Proctasknum table at the same time;

[0094] Step 3: consider the target ph4 as a new target, and handle it according to the arrival of a new target, as in Example 1;

[0095] Step 4: sThread0 of the slave node uses the same method to maintain the partial map table, but the threshold at this time is at least N×T+a, where a is the upper limit of network transmission delay.

[0096] It can be seen from the above description of the implementation manners that those skilled in the art can clearly understand that the pre...

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Abstract

The invention discloses a parallel processing method for aerial multi-target-trace prediction, and belongs to the technical field of aerial multi-target-trace prediction. In a cluster environment, a distributed frame including a main node responsible for task scheduling and logic transaction management and calculating nodes for predicting a plurality of target traces is constructed; communication between the main node and the calculating nodes as well as parallel processing of logic transaction are realized in a two-level parallel manner of MPI standards and Pthreads standards; and a task distributing policy based on an aircraft state is proposed, so that the task distribution of each calculating node is more balanced on the premise of effectively shortening communication time, and the technical problems of low processing capacity and incapability of meeting system real-time performance for a single computer node is solved.

Description

technical field [0001] The invention discloses a parallel processing method for trajectory prediction of multiple targets in the air, and belongs to the technical field of trajectory prediction of multiple targets in the air. Background technique [0002] Air multi-target trajectory prediction needs to track a large number of air targets, and provide information such as the target's instantaneous position, velocity, and predicted landing point. The system must allocate and calculate a large amount of data in a very short period of time, and the real-time requirements are extremely high. [0003] The multi-target trajectory prediction task data has the following characteristics: ① Horizontal independence, that is, each batch of targets is independent of each other; ② Vertical correlation, that is, data backtracking is required for the same batch of target orbit determination and trajectory correction; ③ Single target data stability , that is, the target data will be continuo...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F9/50
Inventor 王雪袁家斌刘爽赵兴方
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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