SSRG International Journal of Electronics and Communication Engineering (SSRG-IJECE) Volume 2 Issue 5 May 2015

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1 SSRG Internatonal Journal of Electroncs and Communcaton Engneerng (SSRG-IJECE Volume 2 Issue 5 May 215 Navgatonal Error Control by Model based Flterng and Smoothng for GPS/INS Integraton Dr. S. S. Sreea Mole Professor & Head, Dept.of ECE Narayana Guru College of Engneerng, Kanyaumar, Inda navgaton systems because they can sgnfcantly mprove ther accuracy. But the KF s a lnear estmator [1], [3], [5], t does not gve better estmaton n non lnear moton of the vehcle. So the EKF s ntroduced to gve accurate results for non lnear moton model. The EKF lnearzes the dynamc state model usng Taylor seres [8]. An Interactng Multple Model (IMM algorthm uses the collecton of flters, and each flter s assgned wth partcular moton model of the movng vehcle [7]. The estmaton accuracy of IMM s better than the sngle model flter le EKF. There are some flaws n EKF, we can use EKF n non lnear case; however, t requres lnearzaton of the state model and the measurement model. Ths leads to poor Abstract: Nowadays Global Postonng System (GPS s the wdely used system for navgatonal ads and tracng of vehcles. However, GPS wll not offer unnterrupted and consstent poston values.e. Lattude, Longtude and Alttude all the tmes as t lely to be bloced by buldngs, mountans, etc. Inertal Navgaton Systems (INS provdes contnues nformaton of poston, velocty and atttude all the tme. However, the performance of INS deterorates wth tme due to the perfromance degradaton of nertal sensors. GPS/INS ntegraton provdes relable navgaton soluton. Exstng GPS/INS ntegraton usng Kalman Flter (KF can gve correct results only when the system dynamc models are completely nown. To estmate the state of vehcle, Extended Kalman Flter (EKF s used. Snce EKF provdes the naccurate navgaton durng the non lnear moton of the vehcle, an Unscented Kalman Flter (UKF has been employed. Interactng Multple Model (IMM flter s more effcent than the conventonal sngle model flter n determnng the adequate values of process nose covarance. In ths paper, the applcaton of Interactng Multple Model Unscented Kalman Flter Two Flter Smoothng (IMM-UKFTFS approach to GPS/INS ntegraton for the maneuverng vehcle s proposed. The resultng IMM-UKFTFS strategy effectvely deals wth the non-lnear moton and nose covarance problem of navgaton. The performance of the proposed IMM-UKFTFS method s examned for a non-lnear traectory whch consst of Constant Acceleraton (CA and Coordnated Turn (CT models. The smulaton results show that the proposed IMM-UKFTFS gves better estmate than the exstng conventonal estmators such as UKF and IMM-UKF. Keywords: GPS; INS; EKF; UKF; IMM-EKF; IMM-UKF; IMM-UKFTFS; TFS 1. Introducton Navgaton s the estmaton of the state of the movng vehcle. An estmator s an algorthm that uses avalable measurements to refne the state varables. The GPS measurements are obtaned by placng the GPS sensors on the movng vehcle [8]. Estmaton technques, especally the Kalman flterng have found extensve applcaton n performance of EKF. In order to overcome the problems n EKF we are gong for transformaton based statstcal approach of Unscented Kalman Flter (UKF. The UKF can be used n IMM algorthm. The estmate of IMM-UKF s better than that of the sngle model UKF [12] and IMM-EKF. Smoothng s the post processng technque whch resolves the estmaton problem. Smoothng s better than flterng by usng the addtonal measurements made after the tme of the estmated state vector [14]. Smoothng can be ncorporated to the IMM algorthm to acheve better navgaton accuracy. There are three types of smoothng, namely Fxed Pont Smoothng, Fxed Lag Smoothng and Fxed Interval Smoothng. Due to less complexty, the Fxed Interval Smoothng s popular. The Fxed Interval Smoothng has two types. They are Rauch Tung Strebel Smoother (RTSS and Two Flter Smoother (TFS [1]. RTSS s confrmed to wor well only for the lnear system. TFS gves better results than RTSS durng hghly non lnear behavor of vehcle moton and also the computatonal tme of both the methods are proved to be the same. Hence TFS s chosen whch wll gve relable result n non lnear system and ncorporatng the TFS nto the IMM algorthm for GPS/INS navgaton. The paper s organzed as follows. Secton 2 deals wth dfferent model based approach, secton 3 deals wth the proposed IMM- UKFTFS method, secton 4 gves the dea of modelng parameters of GPS/INS navgaton, secton 5 deals wth the concept of GPS/INS ntegraton, secton 6 deals wth the analyss of varous estmaton methods. ISSN: Page 45

2 SSRG Internatonal Journal of Electroncs and Communcaton Engneerng (SSRG-IJECE Volume 2 Issue 5 May Model Based Approach 2.1. Estmaton Methods A vtal problem for GPS/INS navgaton s selectng a sutable estmaton method. Several approaches have been recognzed. They are, Extended Kalman Flter (EKF, Unscented Kalman Flter (UKF, IMM based EKF, and IMM based UKF. However, they suffer from dvergence problem.e., estmaton does not converge to the real traectory Unscented Kalman Flter (UKF In the EKF, the state dstrbuton s propagated analytcally through the frst-order lnearzaton of the nonlnear system due to whch, the posteror mean and covarance could be corrupted. The UKF, whch s a dervatve-free alternatve to the EKF, overcomes ths problem by usng a determnstc samplng approach. The state dstrbuton s represented usng a mnmal set of carefully chosen sample ponts, called sgma ponts. Le EKF, UKF conssts of the same two steps: model forecast and data assmlaton, except they are preceded now by another step for the selecton of sgma ponts. The UKF s founded on the ntuton that t s easer to approxmate a probablty dstrbuton than t s to approxmate an arbtrary nonlnear functon or transformaton. The sgma ponts are chosen so that ther mean and covarance to be exactly x(-1 and P(-1 respectvely. Each sgma pont s then propagated through the nonlnear functons yeldng a cloud of transformed ponts at the end. The new estmated mean and covarance are then computed based on ther statstcs. Ths process s called unscented transformaton [2]. The n-dmensonal random varable wth mean x and covarance P xx s approxmated by 2n+1 weghted pont by x = x x = x + (n + p xx, = 1,, n x = x (n + p xx n, = n + 1,,2n Weghts for state w s = w s = w c = 1 2 n+ n+ and weghts for covarance w c = n+ + (1 α2 + β (1 In equaton (1 the term x denotes sgma pont, term w denotes weght, term s a constant, the term α denotes the spread of sgma ponts and β s related to dstrbuton of x. The sgma ponts are passed through the non-lnear functon to yeld the set of transformed sgma ponts. ς = f(x The mean and covarance are gven by the weghted average and the weghted outer product of the transformed ponts y 2n = w s ς = P yy 2n (2 = w c (ς = y(ς y T (3 The tme predcton and the measurement update s done usng ths mean and covarance. ISSN: Page 46

3 SSRG Internatonal Journal of Electroncs and Communcaton Engneerng (SSRG-IJECE Volume 2 Issue 5 May IMM-UKF Snce the moton model of the vehcle has been changed frequently, more than one estmator s to be consdered to meet the changng envronment, so IMM has been ntroduced. As the name mples here, multple models can be utlzed smultaneously [6], [7], [11]. To reduce the nose uncertanty and the system non lnearty problem concurrently, the IMM-UKF s ntroduced [12]. In the IMM-UKF, two UKF flters are used [7]. By usng the model probablty, the IMM algorthm weghts the output of the ndvdual flters. The fnal combned estmate of IMM-UKF s better than the estmate of UKF whch uses the sngle model. The accuracy of the IMM-UKF s hgher than that of the IMM-EKF Two Flter Smoother (TFS Two Flter Smoother (TFS conssts of two flters. They are the forward flter runnng forward n tme and a bacward flter runnng bacward n tme. At each nstant of tme, the estmate from the forward flter s based on all the measurements made up to that tme and the estmate from the bacward flter s based on the measurements made after that tme. At each nstant of tme, the assocated estmaton uncertanty covarance characterzes the estmaton uncertanty based on all these measurements. When the tme s reversed, the sgn on the dynamc coeffcent matrx A s changed, whch can mae performance of the bacward flter model dfferent from that of forward flter model. At each tme t, the forward flter generates the covarance matrx P [f] (t representng the mean-squared uncertanty n the estmate x f t usng all measurements z(s for s t. Smlarly the bacward flter generates the covarance matrx P [b] (t matrx representng the mean-squared uncertanty n the estmate x b t usng all measurements z(s for s t. The optmal smoother combnes x f t and x b t, usng P [f] (t and P [b] (t to mnmze the resultng covarance matrx P [s] (t as shown n Fgure 1. [9], [1], [4]. 3. Proposed Method (IMM-UKFTFS Fgure 1. TFS based flter estmates To mprove the postonng accuracy, IMM based fxed nterval Two Flter Smoothng s ntroduced n ths paper. The proposed method ncorporates smoothng algorthm nto the Interactng Multple Model approach as shown n Fgure 2. Two IMM flters are runnng parallel and they are assgned wth partcular model. For two flter smoothng [14], two estmates at tme t, one based on forward flterng and other based on bacward flterng are consdered. The dea s to obtan a smoothed mproved estmate by fuson of these two estmates x f t and x b t, and ts assocated co-varances P [f] (t and P [b] (t [9]. The IMM-UKFTFS uses the estmaton and model probabltes of the forward Unscented Kalman Flter and the bacward Unscented Kalman Flter. The flters use ts own tme update and measurement update equatons for flterng as gven below. ISSN: Page 47

4 SSRG Internatonal Journal of Electroncs and Communcaton Engneerng (SSRG-IJECE Volume 2 Issue 5 May 215 The IMM smoothed estmate Let x,s x,s be the IMM smoothed estmate error gven by, s,s x of (4 The IMM smoothed estmate s gven by, s 1,f 2.,b (5 From the equaton (4 we can wrte x x (x x (x x where,s 1 x, f, x,b (6,f x 2 Fgure 2. Bloc dagram of IMM-UKFTFS x s estmated as a lnear combnaton of two IMM flters,b are the estmated errors of the IMM forward and bacward flters., s (1 2 Ix 1x,f 2x,b (7 To mae our estmate unbased, E x 2 1 I I 2 1 (8 (, s (9 Substtutng 2 n the equaton (5, we obtan the smoothed estmate., s 1.,f (I 1,b (1 Also t can be wrtten as, s,b 1(,f,b (11 The error covarance matrx of the smoother estmate s obtaned by x, s 1x,f 2x,b 1x,f (I 1 x,b (12,f and, b. ISSN: Page 48

5 SSRG Internatonal Journal of Electroncs and Communcaton Engneerng (SSRG-IJECE Volume 2 Issue 5 May 215 P T T, s 1P,f.1 (I 1P,b (I 1 (13 By mnmzng the equaton (13 for gan K 1 2 P 2(I P 1,f 1,b ( P,b (P,f P,b I I ( I P,b (P,f P,b ( P,f (P,f P,b (17 Substtutng the equatons (15 and (17 n the equaton (13, we wll get P 1 1 1, s P,f P,b (18 The equaton for smoothed covarance of forward and bacward IMM flter can be wrtten as P 1 1 1, s [P,f P,b ] (19 The equaton (19 proves that the smoothed uncertanty covarance s less than the uncertanty covarance of both forward and bacward IMM-UKF. The equaton for the IMM smoothed estmate s obtaned by substtutng (15 and (17 n equaton (1 1 1, s P,b (P,f P,b.,f P,f.(P,f P,b.,b,s P (P,b,f.P P (2,f,b [(P,f 1.,f (P,b 1.,b ] (21 The IMM smoothed estmate s obtaned by usng the forward and bacward uncertanty covarance whch s gven by,,s P,s [(P,f (22 1.,f (P,b 1.,b The model condtoned smoothed estmate x P n s,, s μ 1, s 1,s n 1 μ s, 1 (23. P,s ] x, s x x (24 The mxed smoothed probablty s calculated by, s, 1 μ 1 p Λ r The term n r p 1,s (25 r s computed by, Λ The lelhood (26 Λ s gven by,,s,s,s and ts uncertanty covarance T s P, s gven by the equaton ISSN: Page 49

6 SSRG Internatonal Journal of Electroncs and Communcaton Engneerng (SSRG-IJECE Volume 2 Issue 5 May 215 Λ N(Δ,D (27 Where N( s the probablty functon of measurement n nnovaton dstrbuton, b, f, Δ x s equvalent to measurement nnovaton. D Where b, Pˆ P s combned covarance of forward and bacward flter, b,, f, b, Pˆ are model condtoned bacward- tme one-step predcted mean and co-varances, f, f, x, P are model-condtoned forward-tme fltered means and co-varances, The overall optmal smoothed estmate and ts uncertanty covarance s gven by, Pˆ s s n 1 n 1 μ μ s, s, x,s (28. P,s s s x x,s (29 The smoothed model probabltes are computed as s, 1 μ rμ r (3 where n r r μ 1,s T μ s the forward tme fltered model probablty and r s the normalzaton constant gven by, (31 4. Modelng Parameters for GPS/INS Navgaton To estmate the poston of the movng vehcle usng IMM-UKFTFS, two moton models are consdered. They are Coordnated Turn or Crcular Turn (CT model and Constant Acceleraton (CA model [13]. The system used n ths wor ncludes four sensors and one movng vehcle. Here 2 dmensonal Cartesan coordnate system s consdered n whch the postve x and postve y axes correspond to East and North of navgaton axs respectvely Constant Acceleraton (CA Model The dynamc model for constant acceleraton model can be represented as, δx = Fδx 1 + Gv( 1 (32 The constant acceleraton model has sxth order state vector and conssts of poston error, velocty error and acceleraton error of the vehcle n x and y drectons and turn rate parameter. The error state vector s gven by, δx = [ δx e δy n δx e δy n δx e δy n ] (33 In equaton (33 δx e and δy n are the poston error parameters n east and north drecton, δx e and δy n are the velocty error parameters n east and north drecton, and δx e and δy n are the acceleraton error parameters n east and north drecton. The error space representaton of the constant acceleraton model s gven by, δx e ( δy n ( δx e( δy = n( δx e( δy n ( (34 1 T T T T T 1 T 1 δx e ( 1 δy n ( 1 δx e( 1 δy n( 1 δx e( 1 δy n ( 1 + T 3 6 T 2 2 T T 3 6 T 2 2 T w( 1 ISSN: Page 5

7 SSRG Internatonal Journal of Electroncs and Communcaton Engneerng (SSRG-IJECE Volume 2 Issue 5 May 215 The observaton matrx H can be represented by, 1 H = 1 (35 The model transton probablty matrx s set to,.9.1 P =.1.9 (36 The ntal model probabltes are set to, μ = [.95.5] ( Coordnated Turn (CT Model A common way of modellng a turnng vehcle s to use the coordnated turn model. In ths model, a turn rate parameter Ω s ncluded n the state vector. The dynamc model for coordnated turn model s as follows, δx = Fδx 1 + Gv( 1 (38 The coordnated turn model has ffth order state vector and conssts of poston error and velocty error of the vehcle n x and y drectons and turn rate parameter. The error state vector s gven by, δx = [ δx e δy n δx e δy n δω ] (39 The state space representaton of the coordnated turn model s gven by, δx e ( δy n ( δx e( δy n( δω( 1 sn ΩT Ω 1 cos ΩT cosωt sn ΩT 1 1 cos ΩT sn ΩT Ω δx e ( 1 δy n ( 1 = δx e ( 1 + Ω Ω δy sn ΩT cosωt n( 1 1 δω( 1 (4 The observaton matrx H for CT model can be represented by, 1 H = 1 (41 The model transton probablty matrx s set to,.9.1 P =.1.9 (42 The ntal model probabltes are set to, μ = [.95.5] ( T 2 T 1 2 T 2 T w( 1 ISSN: Page 51

8 SSRG Internatonal Journal of Electroncs and Communcaton Engneerng (SSRG-IJECE Volume 2 Issue 5 May GPS/INS Integraton usng IMM-UKFTFS: The GPS/INS ntegraton can be done by comparng the output of GPS wth that of INS as shown n Fgure 3. The dfference (δz between the output of INS and the output of GPS (Z s gven to the nput of the IMM- UKFTFS. The smoothed estmate of the vehcle poston s feedbac to the INS to get the corrected INS output. Fgure 3. Confguraton of Integrated navgaton usng IMM-UKFTFS The INS measurements are created wth respect to the GPS measurements by addng error. The GPS measurements are consdered as most trusted measurements and are created around the true traectory as shown n Fgure 4. The dfference between GPS and INS measurements are taen and are consdered as an error n measurement. The error measurement δz s gven as an nput to CA-CT error modeled IMM-UKFTFS. The error estmated value s subtracted from the INS value and the INS value whch resembles the GPS value s obtaned. [7, 8] The traectory of the error between the GPS and INS values are smulated for 2 tme steps wth step sze T=.1.The movement of vehcle s gven n Table 2. The vehcle starts from orgn wth acceleraton x, y = (1,, and at 21s, vehcle starts to turn left wth rate = 1, and at 8s, vehcle stops turnng and moves for 6 seconds wth a constant total acceleraton of one, and at 121s, vehcle starts to turn left wth rate = 1 and at 18s, vehcle stops turnng and moves for 2 seconds wth the same acceleraton. The error between the GPS poston (true traectory and the INS measurement s shown n Fgure 5. The corrected INS usng IMM- UKFTFS s depcted n Fgure 6. Fgures 7 and 8 show the vehcle error poston n north and east drecton. Fgures 9 and 1 show the model probablty of CA and CT models n IMM-UKFTFS. Fgure 4. GPS (True traectory and INS measurement of smulated vehcle Fgure 5. Error between GPS and INS measurement ISSN: Page 52

9 SSRG Internatonal Journal of Electroncs and Communcaton Engneerng (SSRG-IJECE Volume 2 Issue 5 May 215 Fgure 6. Corrected INS usng IMMUKFTFS Fgure 7. Vehcle poston error estmate n north drecton usng IMM-UKFTFS Fgure 8. Vehcle poston error estmate n east drecton usng IMM-UKFTFS Fgure 9. Constant acceleraton model probablty n IMM- UKFTFS Fgure 1. Constant acceleraton model probablty n IMM- UKFTFS ISSN: Page 53

10 SSRG Internatonal Journal of Electroncs and Communcaton Engneerng (SSRG-IJECE Volume 2 Issue 5 May Performance Analyss The estmates of erroneous INS and corrected INS usng UKF, IMM-UKF, and IMM-UKFTFS are depcted n Fgures 11 and 12. From these fgures t can be revealed that the estmated traectory s estmated and smoothed as close as to the true traectory usng IMM-UKFTFS. The Fgures 13 and 14 gve the constant acceleraton and coordnated turn model probabltes for IMM-UKF and IMM-UKFTFS. The model probabltes depcted by IMM-UKFTFS s more lely to the true model than that depcted by IMM-UKF. The Fgures 13 and 14 gve the dea about the changes n model probablty between constant acceleraton model and coordnated turn model wth respect to the actual vehcle dynamcs. The Fgures 15 and 16 show the vehcle poston error estmate of proposed schemes n north and east components. From Fgures 15 and 16 t can be proved that the error n north and east drecton s very less n IMM-UKFTFS when compared to UKF and IMM-UKF. Fgure 11. Estmates of error produced by UKF, IMMUKF, IMMUKF-TFS Fgure 12. Estmates of Corrected INS produced by UKF, IMMUKF, IMMUKF-TFS Fgure 13. Constant acceleraton model probablty Fgure 14. Coordnated Turn model probablty Fgure 14. Vehcle poston estmate error n north Fgure 15. Vehcle poston estmate error n east ISSN: Page 54

11 Mean Square Error (m SSRG Internatonal Journal of Electroncs and Communcaton Engneerng (SSRG-IJECE Volume 2 Issue 5 May 215 Table 1. Descrpton of the Vehcle moton Segment Number Tme Interval (sec Moton -2 Constant Acceleraton straght lne 21-8 Counter clocwse turn wth turn rate Ω= Constant Acceleraton straght lne Counter clocwse turn wth turn rate Ω= Constant Acceleraton straght lne Table 2. Comparson of Mean Square Error of dfferent estmaton methods Integrated CA-CT Error models Integrated CA-CT Error Model Methods North East UKF IMM-UKF IMM-UKFTFS UKF IMM-UKF IMM-UKFTFS North East Fgure 16. Comparson of Mean Square Error of dfferent estmaton methods for CA-CT Error models 7. Concluson In ths paper, GPS navgaton wth error modelng usng IMM-UKFTFS method s presented. Snce TFS provdes better results than RTS durng measurements. Further, the mean square error of IMM-UKFTFS s lesser than other estmators. The comparson of Mean square error estmates of UKF, IMM-UKF and IMM-UKFTFS usng CA and CT hgh nonlnear characterstcs of navgaton models are gven n Table2. In both the cases t s applcatons and also the computatonal tmes for both TFS and RTS methods were almost the found that IMM-UKFTFS method gves less mean square error compared to UKF and IMM-UKF same[1], the IMM based TFS algorthm s methods. Even though the computatonal consdered n ths wor. The proposed methods have been tested by usng two combnatons of model traectores. The navgaton accuracy based on the complexty s hgher for IMM-UKFTFS than other estmaton technques, t has been confrmed that there s sgnfcant mprovement n both navgaton proposed methods has been compared to the sngle accuracy and tracng capablty. From the model flter, UKF and multple model flter, IMM- UKF. Whle comparng UKF and IMM-UKF, the output of IMM-UKF s better than UKF. Because smulaton results, we can conclude that IMM- UKFTFS s better than IMM-UKF and gves accurate postonng. IMM-UKF uses both CA and CT models for vehcle moton whereas UKF uses sngle model ether CA 8. References or CT for vehcle moton. But a smoothed estmate [1] M.S. Grewal and A.P. Andrews, Kalman Flterng Theory and Practce usng Matlab, 3 obtaned from IMM-UKFTFS s superor to IMM- edton, John & Wley Publcatons, 28. UKF because they use addtonal smoothng ISSN: Page 55

12 SSRG Internatonal Journal of Electroncs and Communcaton Engneerng (SSRG-IJECE Volume 2 Issue 5 May 215 [2] Smon Hayn, Kalman Flterng and Neural Networs, Wley Interscence Publcatons, 21. [3] M. S. Grewal, L.R. Well and A.P. Andrews, Global Postonng Systems, Inertal Navgaton and Integraton, Wley Interscence Publcatons, 21. [4] Modellng and Parameter Estmaton of Dynamc systems by J.R.Raol,G.Gra and J.Sngh. [5] Yaaov Bar Shalom, X.Rong L, Thagalngam Krubaraan, Estmatons wth applcatons to tracng and navgaton, Wley Interscence Publcatons, 21. [6] Honghu Q, Drect Kalman Flterng approach for GPS/INS Integraton, IEEE Transacton on Aerospace and Electronc systems, 37,pp , 22. [7] X. Rong L, Yaaov Bar-Shalom Desgn of an Interactng Multple Model Algorthm for Ar Traffc Control Tracng, IEEE Transactons on Control Systems Technology. vol.1(3, September [8] Chen-HaoTseng and Dae-Jung Jwo GPS navgaton processng usng the IMM based Extended alman flter for ntegrated navgaton sensor fuson, Sensor Publcatons, vol.6, pp. 4-17, 29. [9] Ronald E.Helm, W. Dale Blar and Scott A. Hoffman, One-Step Fxed Lag Smoothers for Marovan Swtchng Systems, IEEE Transactons on Automatc Control, vol.41(7, pp , [1] Hang Lu, Sameh Nassar and Naser El-Shemy, Two Flter Smoothng for Accurate INS/GPS Land Vehcle Navgaton n Urban Centers IEEE Transactons on Vehcular Technology, pp , vol. 59(9, November 21. [11] L.X, and Bar-shalom.Y, Performance predcton of the nteractng multple model algorthm, IEEE Transactons on Aerospace and Electronc Systems, vol.29(3, pp , [12] Qan. H, An. D, Xa. Q, IMM-UKF based land vehcle navagon wth low-cost GPS/INS, Proceedngs of the 21 IEEE Internatonal Conferene on Informaton and Automaton, Harbn, Chna, pp , June 21. [13] X.Rong L and Vesseln P.Jlov Survey of Maneuverng Target Tracng.Part.I:Dynamc Models IEEE Transacton on Aerospace and Electronc systems, 39,No.4., 23. [14] Donald c. Fraser and James E. Potter," The optmum lnear smoother as a combnaton of two optmum lnear flters" IEEE Transactons on automatc control, PP , August Authors Dr.Sreea Mole S.S completed her B.E n Electroncs and Communcaton Engneerng and M.E n Communcaton Systems from Thagaraa College of Engneerng, Madura. She has authored fourteen publcatons n reputed Internatonal Journals, four papers n proceedng eleven natonal Conference and three papers publshed n Internatonal Conferences. She has completed her Ph.d n mage processng on Anna Unversty. Her area of nterest ncludes mage processng, multmeda and compressons.. Two text boo was authored by her for the theory course Transmsson Lnes and Wave Gudes and Advanced Dgtal System Desgn for the undergraduate and post graduate Engneerng students. She s the Lfe Fellow of IETE & IE (I, Lfe member of ISTE, ACM, IDES and Internatonal Assocaton of Engneers (IAENG. She has been a revewer of Elsever, ACM and Indan Journal of Scentfc Research and Technology ournal. She has wor as a Professor n department of Electroncs and Communcaton engneerng, SCET, Manvla. Now she s worng as a professor and HOD,ECE department n NGCE,Manalumoodu. ISSN: Page 56