A Preliminary Study on Targets Association Algorithm of Radar and AIS Using BP Neural Network

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1 Avalable onlne at Proceda Engneerng 5 ( A Prelmnary Study on Targets Assocaton Algorthm of Radar and AIS Usng BP Neural Networ Hu Xaoru a, Ln Changchuan a a Navgaton Insttute of Jme Unversty, Xamen 362, Chna Abstract Targets assocaton judgment s one of ernels n the processng of mult-targets nformaton fuson. An assocaton algorthm of the target nformaton of the marne radar and AIS was proposed based on BP neural networ theory. Frst, the desgn of the networ structure was dscussed n detal, then, the computer smulaton based on Matlab was carred out. The results ndcated that ths method can acheve the assocaton judgment effectvely for dfferent targets nformaton. Keywords: Targets Assocaton; BP Neural Networ; Radar; AIS. Introducton AIS (Automatc Identfcaton System s a new nd of navgaton ads system at sea, whch can provde the dynamc and statc nformaton of shps. The dynamc nformaton ncludes lattude, longtude, course, speed and UTC tme etc., whle the statc comprsng wth MMSI, IMO number, shp types and the poston of GPS antenna. AIS nformaton obtans the advantages of rch content and hgh accuracy, but t s lmted by a passve wor manner and so on. Relatvely, the radar can detect the targets actvely and gve the targets panoramc pcture. But t s susceptble to the outsde crcumstances and ts data accuracy sn t so hgh. So the nformaton fuson of radar and AIS can complement each other to mprove the accuracy and relablty of the targets. Therefore, research on the two s fuson has a sgnfcant mportance. Targets assocaton judgment s an mportant part n multple targets nformaton processng. Many scholars have done somethng on ths, for example, the membershp method n fuzzy mathematcs, the method based on gray theory and other methods based on Statstcs. As the NN (Neural Networ s used and studed more and more wdely especally for pattern recognton, targets assocaton, predcton and data compresson. So t s meanngful to ntroduce ths method n data fuson of radar and AIS. Ths paper dscussed the targets assocaton of the radar and AIS based on BP neural networ.. A targets assocaton algorthm based on BP networ In the process of radar and AIS data assocaton, to determne whether a radar target s assocated wth an AIS targets s to determne whether ths two dfferent nformaton belongs to the same target. Under the condton of lots of radar and AIS targets, we can transform ths problem nto targets classfcaton. The flowchart of ths algorthm s presented as Fg.... Characterstc extracton As radar and AIS nformaton are provded by dfferent tme and space, they need to be unfed to the same tme and space reference pont and need to be measured by same features. Our study assumed that the above wor has been carred out. To ndcate a shp, the features we selected follow as ths: target s dstance (wth symbol of Ds, bearng (wth symbol of, COG and SOG. Consderng the practcal use, the networ s structure s desgned wth sngle hdden layer, that s, there s only one nput layer, one hdden layer and one output layer. The detaled structure s shown as Fg.2 as followng: The project s supported by the major project of the unversty-ndustry-cooperaton of the Fujan provnce (2H67 and by the Scence Foundaton of Jme Unversty (ZQ24. Correspondng author:ln Changchuan. Tel: ,E-mal address:ccln@jmu.edu.cn Publshed by Elsever Ltd. do:.6/j.proeng Open access under CC BY-NC-ND lcense.

2 442 Hu Xaoru and Ln Changchuan / Proceda Engneerng 5 ( Start Set the assocaton rules Data of radar Data of AIS Enter the well-traned networ Tme and space unfcaton Characterstc extracton Data standardzaton Execute assocaton judgment accordng to the networ s output Save the assocaton pars End Fg.. Procedure of targets assocaton Input layer Hdden layer Output layer COG ( h( ( hj SOG ( y Ds ( ( Fg.2. Networ s structure The nput vector s desgned as P = ( COG (, SOG (, Ds (, ( that s the 4 nput nodes s formed as formula ( as followng: COG ( = A_COG ( R_COG ( SOG ( = A_SOG ( R_SOG ( ( Ds (= A_Ds ( R_Ds ( ( = A_ ( R_ ( Where symbols above denote respectvely as followng:

3 Hu Xaoru and Ln Changchuan / Proceda Engneerng 5 ( COG ( the absolute dfference between the th AIS course and the jth radar course at moment ; SOG ( the absolute dfference between the th AIS t course and the jth radar speed at moment ; Ds ( the absolute dfference between the th AIS course and the jth radar dstance at moment ; ( the absolute dfference between the th AIS course and the jth radar bearng at moment ; h ( the weght between nput layer and hdden layer at moment ; hj ( the weght between hdden layer and output layer at moment ; y the networ s output at moment... Data standardzaton As there are dfferences among the dmensons and numerc range of the four features, some approprate transformatons should be carred out to the raw data before the networ s computng, whch s nown as data standardzaton or normalzaton. In ths paper, we adopt the method of standard devaton. Frst, the nput vector should be standardzed as formula (2 as followng: x xˆ (2 s Where x s the th sample value, and x represents the average of the samples, n 2 s ( x x n n x n s standard devaton of the sample, then accordng to formula (3 below, we can obtan the fnal nput data. ' xˆ mn( x,, xn x (3 max( x,, x mn( x,, x n.2. Networ s tranng Frst, we ntalze the networ s structure, the nput layer s settng refers to secton 2.2. As to the output layer, the bnary output s enough for the assocaton judgment, the node s numbers are set as one. Number of the hdden layer s node can be estmated usng the followng formula (4 and be automatcally adjusted to the optmum n the smulaton program. l m n a(4 Where, l s the hdden layer s nodes number, m s the nput layer s nodes number n s output layer s nodes number, a s an nteger among ~. 2. Descrpton of BP algorthm BP algorthm s a nd of bac propagaton learnng algorthm. In practce, t conssts of two parts whch are networ tranng and networ testng. Steps of ths algorthm s descrbed as follows [5] : Step Intalze the parameters such as net structure, layer numbers, nodes number of each layer, the nput vector X D,the weght h between nput layer and hdden layer, the weght hj between hdden layer and output layer the learnng rate, the momentum coeffcent the MSE eps and so on. Step 2 Select a pattern and pass forward to calculate the hdden layer output p h h Then calculate the output layer s result: where f ( x ( e x net ( x a q j hj h h n Oh net ( O b O j ( netj ( x ( e or ( x f( net h yj O Step 3 Pass bacward to calculate the neuron error begnnng from the output layer j (5 (6 ' ( d O ( O j j j j O hj j h (7 Then calculate the hdden layer s error r ' h ( j hj( Oh j x (8 h h

4 444 Hu Xaoru and Ln Changchuan / Proceda Engneerng 5 ( Step 4 Update the weghts Step 5 Calculate the networ s output error - hj hj hj hj - h h h h (9 n ( r 2 yj d j ( j If ths value s greater than eps, then turn bac to Step 2, or come to the ends of the algorthm. After the above steps, the net wll be traned to the pre-set accuracy, then tested and assessed by a ple of patterns, the networ wll be come to use. 3. Smulaton and Results 3.. Smulaton parameters settng The smulaton n ths paper s based on Matlab neural networ toolbox. To smplfy ths process, the own shp s selected as a reference wth headng and speed both set as, the ntal poston of own shp s set as orgnal pont of axs. Lots of samples are requred for tranng, that s targets AIS and Radar data. The radar s antenna rotates one round every 2 or 4 seconds. So n [,297s] we tae a sample every 3s, tmes for each target. The same target s Radar and AIS data s vewed as assocaton patterns whle the dfferent targets data seemed as not assocated, then the nput patterns are obtaned. Shown as Table, data from target to 4 wll be taen as tranng patterns (the total number s 3 after standardzaton and specfc process. MSE of radar features on course, speed, dstance and bearng error s.5, n,35 m and.2 whle that of AIS s 5,.5 n,5.5 m and.8 repectvely.usng the same way, target 5 and target,2,3,4 are treated as testng patterns (the total number s 9. Table. Orgnal targets nformaton Intal speed not Intal course degree Intal dstance nm Intal bearng degree Target Target Target Target Target Results and Analyss Performance s e-5, Goal s. Tranng-Blue Goal-Blac Epochs Fg. 3. Error change n the net tranng stage

5 Hu Xaoru and Ln Changchuan / Proceda Engneerng 5 ( Fgure 3 above shows the change of MSE durng the process of net tranng, where there re 9 hdden layer nodes. When eps s set as., 7 teratons are needed to acheve the preset goal. Due to BP networ s own restrctons, at the ntal stage, the curve comes to faster convergence, at the latter t becomes more gently. For the networ tranng results, the desred output of assocaton s set as.98 whle the non-assocaton set as.3. As can be seen from the graph, the two nds of tranng samples are separated obvously. Both of he related samples and the unrelated samples are close to the desred output, and the absolute value of dfference of tranng output and desred output remans below.3,.e. y D( <.3, whch has reached preset effect. output value Output dfference Comparson of actual output and desred output of tranng samples Desred output Tranng output Dfference analyss of tranng output and desred output Assocaton degree Output dfference Comparson of desred output and actual output of testng samples Desred output Actual output Dfference of desred output and actual output of testng samples Fg. 4. (acomparson and Analyss of tranng and desred output; (bcomparson and Analyss of testng and desred output The networ testng results are shown as followng, as can be seen from t, the related samples and the unrelated samples are separated obvously. The assocaton output s close to whle the non-assocaton close. Besdes, dfference between actual output and desred output also remans at the nterval of [,.5]. 4. Concluson In ths paper, a targets assocaton algorthm of Radar and AIS s proposed based on BP networ. Smulaton based on Matlab demonstrates that the algorthm s effectveness. Under the condton of 9 hdden layer nodes and the MSE =., dfference between the net output and the desred output of the testng samples can reman wthn an acceptable range, whch has separated the samples obvously. However, due to the slow convergence and easly fallng nto local mnma, and the testng samples ddn t consder some specal cases, so our next wor s to mprove the algorthm. References []Ln Changchuan. Algorthm and Smulaton of Fuzzy Correlaton of Tracs from Radar and AIS. Journal of System Smulaton, 26(8,p [2]Ou Yangpng, Ln Changchuan. Arthmetc and Implementaton of the Fuson Assocaton of the Target Informaton from AIS wth Radar. Journal of Jme Unversty, 2(4,p [3]Deng Shuzhang. Research and Implementaton of Marne ARPA Radar and AIS Informaton Fuson. SHIP & OCEAN ENGINEERING, 29(6: [4]Wang Chenx. A Survey of Radar and AIS Informaton Fuson. Command Control & Smulaton,29(2,p.-4. [5]Satsh Kumar. Neural Networs. Beng: Tsnghua Unversty Press;26. [6]Fect Technology R&D Center. Theory of Neural Networ and Realzaton wth Matlab. Beng: Electronc Industry Press; 25. [7]MATLAB Chnese Forum. 3 EXAMPLES of MATLAB NEURAL NETWORK. Beng: BUAA Press;2.

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