通信辐射源运动轨迹相似性分析

doi:10.3969/j.issn.1001-506X.2020.09.06

Abstract

Abstract:

Aiming at the problem of the inefficiency of spatio-temporal information in traditional trajectory similarity analysis, combined with the main characteristic of communication transmitter, an algorithm of transmitter trajectory similarity analysis based on spatio-temporal and semantic information is proposed. In this algorithm, the distance measurement of similar motion trajectory is improved, and a trajectory spatio-temporal similarity algorithm based on Mahalanobis distance is proposed, which computes the similarity after trajectory segmentation, overcomes the influence of time mismatch trajectory and different sampling intervals on trajectory similarity judgment. At the same time, combined the semantic similarity data search algorithm based on efficient K-nearest neighbor (KNN) similarity search, accurate judgment of the similar trajectory of the transmitter is realized. The algorithm can judge the similar trajectory accurately, which is superior to the existing similar trajectory judgment methods, and adapts to the noise environment. The simulation experiment results verify the performance of the algorithm.

Key words: communication transmitter trajectory, spatio-temporal information, semantic information, simi-larity, similarity search

CLC Number:

TN919.5

Yufan JI, Lunwen WANG, Mengbo ZHANG. Similarity analysis of motion trajectory of communication transmitter[J]. Systems Engineering and Electronics, 2020, 42(9): 1920-1926.

Figures/Tables 11

Fig.1

Fig.2

Fig.3

Fig.4

Table 1

Fig.5

Table 2

Table 3

Table 4

Fig.6

Fig.7

References 29

1	吉根林, 赵斌. 时空轨迹大数据模式挖掘研究进展[J]. 数据采集与处理, 2015, 30 (1): 47- 58. doi: 10.16337/j.1004-9037.2015.01.004
	JI G L , ZHAO B . Research progress in big data pattern mining for spatio-temporal trajectories[J]. Journal of Data Acquisition and Progressing, 2015, 30 (1): 47- 58. doi: 10.16337/j.1004-9037.2015.01.004
2	毛嘉莉, 金澈清, 章志刚. 轨迹大数据异常检测:研究进展及系统框架[J]. 软件学报, 2017, 28 (1): 17- 34. doi: 10.13328/j.cnki.j0s.005151
	MAO J L , JIN C Q , ZHANG Z G . Abnormal detection for tra-jectory big data: advancements and framework[J]. Journal of Software, 2017, 28 (1): 17- 34. doi: 10.13328/j.cnki.j0s.005151
3	SAN R I , MARTIN D I , CRISTINA C , et al. Outlier trajectory detection through a context-aware distance[J]. Pattern Analysis and Applications, 2019, 22, 831- 839. doi: 10.1007/s10044-018-0732-1
4	SHARIF M , ALESHEIKH A A . Context-awareness in similarity measures and pattern discoveries of trajectories: a context-based dynamic time warping method[J]. GIScience & Remote Sensing, 2017, 54 (3): 426- 452. doi: 10.1080/15481603.2017.1278644
5	ALI A R , SHARIF M , CHEHREGHAN A . A method for similarity measurement in spatial trajectories[J]. Spatial Information Research, 2017, 25 (3): 491- 500. doi: 10.1007/s41324-017-0115-5
6	ZHANG D Z , LEE K , LEE I . Semantic periodic pattern mining from spatio-temporal trajectories[J]. Information Sciences, 2019, 502, 162- 189.
7	ZHANG D Z , LEE K , LEE I . Mining hierarchical semantic periodic patterns from GPS-collected spatio-temporal trajectories[J]. Expert Systems with Applications, 2019, 122, 85- 101.
8	黄亚锋, 邢峰, 张航峰, 等. 基于语义轨迹模型的态势评估方法研究[J]. 中国电子科学研究院学报, 2019, 14 (3): 243- 250.
	HUANG Y F , XING F , ZHANG H F , et al. Research on situa-tion assessment method based on semantic trajectory model[J]. Journal of China Academy of Electronic Sciences, 2019, 14 (3): 243- 250.
9	LEE I , CAI G , LEE K . Exploration of geo-tagged photos through data mining approaches[J]. Expert Systems with Applications, 2014, 41 (2): 397- 405.
10	CAI G C, LEE K J, LEE I. A framework for mining semantic level tourist movement behaviors from geo-tagged photos[C]//Proc.of the 29th Australasian Joint Conference in AI, 2016: 519-524.
11	CAI G C , LEE K J , LEE I . Itinerary recommender system with semantic trajectory pattern mining from geo-tagged photos[J]. Expert Systems with Applications, 2018, 94, 32- 40.
12	CAI G C , LEE K J , LEE I . Mining semantic trajectory patterns from geo-tagged data[J]. Journal of Computer Science and Technology, 2018, 33 (4): 849- 862.
13	章静蕾, 石海龙, 崔莉. 基于出行方式及语义轨迹的位置预测模型[J]. 计算机研究与发展, 2019, 56 (7): 1357- 1369.
	ZHANG J L , SHI H L , CUI L . Location prediction model based on travel mode and semantic trajectory[J]. Computer Research and Development, 2019, 56 (7): 1357- 1369.
14	姚迪, 张超, 黄建辉, 等. 时空数据语义理解:技术与应用[J]. 软件学报, 2018, 29 (7): 2018- 2045. doi: 10.13328/j.cnki.jos.005576
	YAO D , ZHANG C , HUANG J H , et al. Semantic understanding of spatio-temporal data: technology & application[J]. Journal of Software, 2018, 29 (7): 2018- 2045. doi: 10.13328/j.cnki.jos.005576
15	PATHAK G, TIWARI S, MADRIA S K. Generalized distance metric as a robust similarity measure for mobile object trajectories[C]//Proc.of the IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing, 2006: 160-167.
16	BOMBELLI A , SOLER L , MEASE K D . Strategic air traffic planning with frechet distance aggregation and rerouting[J]. Journal of Guidance Control Dynamics, 2017, 40 (5): 1117- 1129.
17	YU Q Y , LUO Y L , CHEN C , et al. Trajectory outlier detection approach based on common slices sub-sequence[J]. Applied Intelligence, 2018, 48 (9): 2661- 2680.
18	HO S S , DAI P , RUDZICZ F . Manifold learning for multiva-riate variable-length sequences with an application to similarity search[J]. IEEE Trans.on Neural Networks & Learning Systems, 2015, 27 (6): 1333- 1344. doi: 10.1109/TNNLS.2015.2399102
19	CHEN Y J, SHEN H, TIAN H. Clustering subtrajectories of moving objects based on a distance metric with multi-dimensional weights[C]//Proc.of the IEEE 6th International Symposium on Parallel Architectures, Algorithms and Programming, 2014: 203-208.
20	刘宇, 王前东. 基于最长公共子序列的非同步相似轨迹判断[J]. 电讯技术, 2017, 57 (10): 1165- 1170. doi: 10.3969/j.issn.1001-893x.2017.10.011
	LIU Y , WANG Q D . Asynchronous similar trajectory judgment based on the longest common subsequence[J]. Telecommunication Technology, 2017, 57 (10): 1165- 1170. doi: 10.3969/j.issn.1001-893x.2017.10.011
21	DEEPAK P , DESHPANDE P M . Advanced operators for similarity search[M]. Bangalore: Springer, 2015.
22	SHI Y, ZHANG A. A shrinking-based dimension reduction approach for multi-dimensional analysis[C]//Proc.of the IEEE 16th International Conference on Scientific and Statistical Database Management, 2004: 427-434.
23	SHI Y, GRAHAM B. A similarity search approach to solving the multi-query problems[C]//Proc.of the IEEE/ACIS International Conference on Computer & Information Science, 2012: 237-242.
24	SHI Y, GRAHAM B. Similarity search problem research on multi-dimensional data sets[C]//Proc.of the 10th International Confe-rence on Information Technology: New Generation, 2013.
25	GUPTA S, DWIVEDI A, ISAC R K, et al. EKSS: an efficient approach for similarity search[C]//Proc.of the IEEE International Conference on Communication, Information & Computing Technology, 2012.
26	GUPTA S, DWIVEDI A, ISSAC R K, et al. An efficient similarity search approach to incremental multidimensional data in presence of obstacles[C]//Proc.of the International Conference on Frontiers of Intelligent Computing: Theory and Applications, 2013: 93-101.
27	LI Z J, JIAN X, LIAN X, et al. An efficient probabilistic approach for graph similarity search[C]//Proc.of the IEEE 34th International Conference on Data Engineering, 2018: 533-544.
28	VLACHOS M, GUNOPULOS D, KOLLIOS G. Robust similarity measures for mobile object trajectories[C]//Proc.of the 13th International Workshop on Database and Expert Systems Applications, 2002.
29	DURAJ L , KUNNEMANN M , POLAK A . Tight conditional lower bounds for longest common increasing subsequence[J]. Algorithmica, 2019, 81 (10): 3968- 3992.

组别	电平/dBm	中心频率/kHz	带宽/kHz	调制样式
1	12	2 004.45	1.93	模式1
2	27	3 173.29	2.02	模式2
3	45	4 312.79	2.47	模式3

待分析轨迹	本文算法的时空相似度结果/%	是否符合实验设置	基于LCS法的相似度结果/%	是否符合实验设置
轨迹1	84	是	84	是
轨迹2	64	是	52	否
轨迹3	48	是	48	是
轨迹4	56	是	80	否
轨迹5 (时间不匹配)	4	是	84	否

轨迹所携带的相似信号特征参数个数	平均正确率/%
1	72
2	91.3
3	99.6
4	100

轨迹缺失的信号特征参数	平均正确率/%
电平	82
中心频率	88
带宽	90
调制样式	88

[1]	Runlin LI, Huanxin ZOU, Xu CAO, Fei CHENG, Shitian HE, Meilin LI. Multi-direction remote sensing ship detection based on center point and semantic information [J]. Systems Engineering and Electronics, 2022, 44(6): 1772-1781.
[2]	LI Zhengxin, ZHANG Fengming, WANG Ying, TAO Qian, LI Chao. Method of missing data imputation for multivariate time series [J]. Systems Engineering and Electronics, 2018, 40(1): 225-230.

Similarity analysis of motion trajectory of communication transmitter

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 11

References 29

Related Articles 2

Recommended Articles

Metrics

Comments