Infrared Counter-Countermeasure Efficient Techniques using Neural Network, Fuzzy System and Kalman Filter

Transcription

1 Infrared Counter-Countereasure Effent ehnques usng Neural Networ, Fuzzy Syste and Kalan Flter M. R. Mosav* Downloaded fro eee.ust.a.r at 23:04 IRD on Monday June 8th 208 Abstrat: hs paper presents desgn and pleentaton of three new Infrared Counter- Countereasure (IRCCM) effent ethods usng Neural Networ (NN), Fuzzy Syste (FS), and Kalan Flter (KF). he proposed algorths estate trang error or orreton sgnal when ang ours. An experental test setup s desgned and pleented for perforane evaluaton of the proposed ethods. he ethods valdty s verfed wth experents on IR seeer retle based on a Dgtal Sgnal Proessng (DSP) proessor. he pratal results ephasze that the proposed algorths are hghly effetve and an redue the ang effets. he experental results obtaned strongly support the potental of the ethod usng FS to elnate the IRCM effet 83%. Keywords: Fuzzy Syste, IRCCM, Jang, Kalan Flter, Neural Networ, Seeer. Introduton After the appearane of the frst-generaton Infrared (IR) seeers and ther suess aganst arraft targets, a need for ountereasures aganst these seeers was eerged. A sple ountereasure aganst these seeers s an IR flare that an be deployed fro the arraft under atta. he flare ntensty s usually several tes that of the target radaton. he seeers bas ther trang ponts toward the ore ntense soure. he seeer thus tras a separatng flare and loses tra of the target. As the flare beae a versatle and relatvely effetve Countereasure (CM), seeer desgners began to develop tehnques to redue offset the effetveness of the flare []. he goal of atve Infrared Countereasures (IRCM) s add odulated IR energy to the IR sgnature of an arraft to ounter IR guded seeers. Arraft power ltatons, arraft sgnatures, sson analyss, IR seeer sgnal proessng, IR soures, and seeertarget sulaton eah play a rtal role n the suess of an atve IRCM syste [2]. Infrared Counter-Countereasure (IRCCM) tehnques aganst the IR flare ay be based on the followng dfferenes between the target and the IR flare haratersts: teporal sgnal hanges, spetral dfferenes, traetory dfferenes (relatve neats), and spatal sze and dstrbuton. hus, a rapd nrease n the seeer sgnal apltude ould Iranan Journal of Eletral & Eletron Engneerng, Paper frst reeved 8 Apr and n revsed for 6 Nov * he Author s wth the Departent of Eletral Engneerng, Iran Unversty of Sene and ehnology, Nara, ehran 6846, Iran. E-al: M_Mosav@ust.a.r be ndatve of a flare deployent. However, sgnal flutuatons also ould be aused by ntentonal or unntentonal target radaton level hanges [3]. he goal of ths paper s to propose three new IRCCM effent ethods usng Neural Networ (NN), Fuzzy Syste (FS), and Kalan Flter (KF). hese algorths estate trang error or orreton sgnal. he proposed ethods valdty s verfed wth experents on IR dgtal seeer retle based on a DSP proessor. hs paper s organzed as follow. Seton 2 desrbes ang effets on seeers. he trang error sgnal estatons based on NN, FS, and KF odelng are proposed n setons 3, 4, and 5, respetvely. Experents results are reported n seton 6 and fnally onlusons are presented n seton 7. 2 Jang Effets on Seeer ypal odulaton wavefors obtaned for a onstant radaton level target are shown n Fg. [4]. he wavefors onsst of an apltude-odulated arrer. Sgnal proessng reoves the arrer and reovers the envelope of the wavefor, whh s at the retle rotaton frequeny. he phase angle of ths wavefor relatve to soe referene deternes the angular dreton n whh the seeer s drven to brng the target age to the enter. hus, a null pont, where zero torque s appled, s obtaned at the enter of the retle pattern sne no odulaton s generated there. Iranan Journal of Eletral & Eletron Engneerng, Vol. 5, No. 4, De

2 Downloaded fro eee.ust.a.r at 23:04 IRD on Monday June 8th 208 Fg. Modulaton wavefors: (a) typal odulaton wavefor, (b) arrer odulaton funton and () aer odulaton wavefor Consder a general ase of a target wth a olloated aer that s odulated n te. he radaton power seen at the detetor P (t) ay be d represented by: = [ A + ] ( t) () d r where, A s the target radaton power fallng on the retle, P (t) s the te-odulated aer power arrvng at the retle, and (t) s the retle r odulaton funton. he retle odulaton s perod at the angular frequeny of ω and an be represented by a Fourer seres: ( t) = exp( ωt) (2) r n n = where: 2π = t n t dt n ( ) exp( ω ) ; = (3) r ω 0 If the aer wavefor s also perod at the angular frequeny of ω, P (t ) an be represented by: = d exp( ω t) (4) = where: 2π d = P t t dt ( ) exp( ω ) ; = (5) ω 0 Substtuton of Eq. (2) and Eq. (4) nto Eq. () yelds: = [ A + d exp( ω t)] d = (6) exp( ω t ) n n = At the detetor, P (t) s onverted nto a voltage d or urrent and s proessed through a arrer aplfer, an envelope detetor, and preesson aplfer ruts before the sgnal s appled to drve the seeer. Consder the retle odulaton funton s as shown n Fg..a;.e., ( t) = [ + α ( t) sn ω t] (7) r 2 t where, α s the rato of the radus of the age loaton (or the trang error) to the radus of the retle that provdes a splfed easure of the odulaton effeny ( 0 α ), (t) s a arrer t gatng funton (a square wave), as shown n Fg..b, and ω s the arrer frequeny. he Fourer seres representaton of (t) s: t 2 n ( ) ( t) = + sn[( 2n + ) ω t] (8) r 2 π 0 2 n = n + Assue that the aer odulaton P (t) also has the for of a arrer at the frequeny ω and s gated at the frequeny ω, as shown n Fg..;.e., B = ( t)( + sn ω t) (9) 2 where, (t) has the sae for as (t) exept that t ω s replaed by ω and B s the pea aer power. he Fourer seres representaton for (t) s: ( t) = 2 ( ) (0) + sn{( 2 + )[ ω t + ϕ ( t)]} 2 π 0 2 = + where, ϕ s an arbtrary phase angle relatve to (t). For ths speal ase Eq. () beoes: t 26 Iranan Journal of Eletral & Eletron Engneerng, Vol. 5, No. 4, De. 2009

3 Downloaded fro eee.ust.a.r at 23:04 IRD on Monday June 8th 208 = [ A + B ( t)( + sn ω t)] d 2 2 () [ + α ( t) sn ω t] t Assung that the arrer aplfer passes sgnals at or near the arrer frequeny only, the output of the arrer aplfer ay be approxated by: s ( t) α [ A + B ( t)] ( t) sn ω t 2 t (2) + B ( t) sn ω t 2 he envelope of the arrer odulaton n Eq. (2) s: B s ( t) α A ( t) + ( t)[ + α ( t)] (3) e t 2 t he envelope sgnal s (t) s further proessed by e a preesson aplfer, whh s tuned around the spn frequeny ω. Assung that ω s lose to ω, the seeer drvng sgnal s gven by: B P( t) α ( A + ) sn ω t 4 (4) B α + ( + ) sn[ ω t + ϕ ( t)] 2 2 he drvng sgnal torques a spnnng gyro (rotatng agnet). he nteraton of the rotatng agnet and the seeer torqung sgnal results n the seeer preesson rate proportonal to the produt of P (t) and exp( ω t). 3 IRCCM Method usng Neural Networ Mult-layer Pereptrons (MLPs) have been suessfully used n te seres predton, however due to ther ultple layer struture; they utlze oputatonally expensve tranng algorths (suh as the BP error) and an get stu n loal na. In an attept to overoe the probles assoated wth use of MLPs, Hgh-Order NNs (HONNs) have been eployed wth great suess. HONNs ae use of nonlnear nteratons between the nputs, thus funtonally expandng the nput spae nto another spae, where lnear separablty, or reduton n the denson of the nonlnearty s possble. However, HONNs suffer fro the obnatoral exploson of the hgh-order ters and deonstrate slow learnng, when the order of the networ beoes exessvely hgh. A sple yet effent alternatve to HONNs s the P- Sga NNs (PSNNs). PSNNs are onstruted of lnear sung unts wth the output layer beng a sngle produt unt wth a nonlnear transfer funton. he weghts fro the sung unts to the produt unts are fxed at unty, whh ples that the sung unts layer s not hdden. he degree of a PSNN equals the nuber of sung unts n the frst layer [5]. Fg. 2 shows a PSNN arhteture. Fg. 2 Proposed PSNN wth ( p, q,) struture he nput s a p densonal vetor and s the th oponent of. he nputs are weghted and fed to a layer of q lnear sung unts, where q s the w desred order of the neural networ. s an adustable weght fro nput to the th sung unt and w 0 s an adustable threshold of the th sung unt. o aheve a desrable set of synapt weghts to a pre-defned networ arhteture, a tranng proess n needed. A tranng proess s generally based on an optzaton shee to adust the networ paraeters (anly, the weghts) n relaton to a set of nput-tooutput to be athed by the NN odel (supervsed learnng shee). he BP algorth based on a gradent desent tehnque has been wdely appled for general NN tranng. A BP eploys a two-pass weghted learnng algorth nown as the generalzed delta rule. In a forward pass through the networ, an error s deteted; the easured error s then propagated baward through the networ whle weghts are adusted to redue the overall error. hs teratve proess that the networ goes through n redung the overall error s nown as gradent desent. he tranng steps are provded n the followng subseton [6]. Fg. 3 Sheat dagra of proposed IRCCM ethod usng NN Mosav: Infrared Counter-Countereasure Effent ehnques usng Neural Networ, 27

4 Downloaded fro eee.ust.a.r at 23:04 IRD on Monday June 8th ranng Steps At frst, soe varables and paraeters are defned: s = he output of the th sung unt of hdden layer r = he net nternal atvty level of output neuron σ (.) = he atvaton funton of output neuron as σ ( x) = /( + e x ) y = he output of the last output neuron Δw = he adusted value of the weght w Δw = he adusted value of the threshold w 0 0 η = he learnng-rate paraeter μ = he oentu value Step : Weghts Vetor Intalzaton Set all of the synapt weghts and threshold of the networs to sall rando nubers that are unforly dstrbuted. Step 2: Forward Coputaton = p s ( ) = w w q ( ) ( ) + ( ) ; =,2, L, (5) = 0 = q r( ) = s = ( ) (6) = q y ( ) = δ [ r ( )] = δ s = ( ) (7) Step 3: Learnng Proess Δw ( + ) = η ( y( ) d ( )) y( ) 0 l = q ( y( ))( s ( )) + μδw ( ) l 0 l =, l Δw ( + ) = η ( y( ) d ( )) y( ) (8) l = q (9) ( y( ))( s ( )) ( ) + μδw ( ) l l =, l or: Δw ( + ) = ( Δw ( + ) μδw ( )) 0 0 (20) ( ) + μδw ( ) Step 4: Iteraton Inreent te by one unt and go ba to step 2. Standard supervsed BP learnng ethodology s followed n these experents wth PSNN. A subset of avalable atual data s used to onstrut tranng saples for the networ. ranng of a PSNN nvolves obtanng optal values for the learnng rate, the oentu of learnng, and the nuber of nodes n eah layer. he overall error s traed untl a na s obtaned by alterng the fore-entoned paraeters. A traned networ whh has learned the sequental nforaton n the tranng set an then be used n trang error sgnal estaton. A 5-3- arhteture wth 5 nputs, one hdden layer of 3 nodes, and one output s used. If the flare deteton odule delares the presene of a flare, the NN odule estates trang error sgnal. he CCM trang odule wll funton untl all flares ext the Feld-of-Vew (FOV). After ext of all flares fro FOV, the IRCCM ethod turns off CCM and returns to noral trang. Fg. 3 shows sheat dagra of proposed IRCCM ethod usng NN. 4 IRCCM Method usng Fuzzy Syste he gener seeer odel onssts of three odules: flare deteton, CCM trang, and noral trang. he flare deteton odule attepts to deterne whether flares exst wthn the FOV. If so, the CCM trang odule ssues tra oands n hopes of gnorng sgnals fro flares. he noral trang odule ssues tra oands based on the weghted average of all soures wthn the seeer FOV [2]. here are three ethods for flare deteton. he frst ethod oputes the reeved ntensty rato for two dfferent wavelength bands. It delares a flare f that rato exeeds user-spefed threshold. he next ethod tests the rato of the nstantaneous ntensty to the hstoral average. If ths rato s above a user-defned threshold, t delares a flare. he fnal deteton ethod ontors the seeer Lne-of-Sght (LOS) rate. hs ethod assues the flare quly separates fro the arraft thus ausng a sudden hange n the seeer LOS rate. hs ethod delares a flare when the hange n LOS rate exeeds a user-spefed threshold. Norally, the seeer eletrons adusts ts gan to always eep the target soure near the enter of ts dyna range. If a flare s present and ts ntensty s uh greater than that of the target, the seeer eletrons an redue ts gan suh that the sgnal fro the target wll fall below the nose floor of the eletrons. Autoat Gan Control (AGC) long stops ths aton. he flare ntensty ay ause the seeer eletrons to saturate; however, the target ntensty wll rean wthn the seeer s dyna range. here are four tra ethods after flare delaraton. he frst ethod s rate hold. hs tehnque adds an offset to the urrent trang rate and holds that value. he next ethod s angle hold. hs tehnque offsets and fxes the gbal angles of the seeer. For these two ethods, the offset auses the seeer to push ahead of the target. he hope s that the flare wll ext the FOV and the target wll ove fro the edge towards the enter of the seeer FOV. he next ethod s rate bas. hs tehnque uses the average of the prevous trang rate and the desred tra rate for noral trang. A user-defned value offsets ths tra rate n an attept to 28 Iranan Journal of Eletral & Eletron Engneerng, Vol. 5, No. 4, De. 2009

5 Downloaded fro eee.ust.a.r at 23:04 IRD on Monday June 8th 208 eep the seeer pontng ahead of the target. he fnal ethod s angle bas. hs tehnque uses the desred gbal angles for noral trang and offsets t by a user-spefed aount n the dreton that the target was last ovng. hs attepts to eep the seeer pontng ahead of the target. he blo dagra of proposed IRCCM ethod usng Fuzzy Syste (FS) s shown n Fg. 4. he V s used as nput fuzzy varable to ths fuzzy o Error syste. Fuzzy syste outputs are defned as K (a oeffent for ϕ angle sgnal) and K 2 (a oeffent for trang error sgnal). ϕ angle s angle between seeer axs and optal axs [7]. V, K, and K 2 are dvded nto two o Error segents for partton the rule spae. hose are fuzzed wth a sngleton ebershp funton. he ebershp funtons are defned as Fg. 5, where S and B express Sall and Bg, respetvely. wo rules n the rule base are defned as followng: Rule: If V s Sall, o Error hen K s S and K 2 s B2 Rule2 : If V o Error s Bg, hen K s B and K2 s S2 he Madan-style ethod wth produt s used for the nferene proess and the enter of area ethod s eployed for the defuzzfaton [8]. If the flare deteton odule delares the presene of a flare, the FS odule aes trang error sgnal. he CCM trang odule wll funton untl all flares ext the FOV. After ext of all flares fro FOV, the IRCCM ethod turns off CCM and returns to noral trang. 5 IRCCM Method usng Kalan Flter he Kalan Flter (KF) s a versatle proedure for obnng nosy sensor outputs to estate the state of a syste wth unertan dynas. hs flter onssts of three fators, whh are predton, observaton, and estaton. he dyna odel desrbes the behavor of state vetor, whle the observaton odel establshes the relatonshp between easureents and the state vetor. Both odels are assoated wth statstal propertes to desrbe the auray of the odels. Fg. 5 Mebershp funtons: (a) V, (b) K, and () oerror K 2 he KF s brefly desrbed n ths seton. Assue that the rando proess s odeled n the for: S [ n + ] = A[ S[ + w[ (2) where S [ n +] s the proess state vetor at te n +, A [ s a atrx relatng S [ to S [ n +] wth the absene of a forng funton and w [ s whte nose wth nown ovarane struture. he easureent of the proess s assued to our at dstane ponts wth the followng relatonshp: X[ = H[ S[ + γ [ (22) where X [ s the easureent vetor at n, H [ s a atrx gvng noseless onneton between the easureent and state vetor at n and γ [ s the easureent error assued to be whte wth a nown ovarane struture. he followng KF equatons for predton are then derved as followng [9]: Step : Intal estaton for Ŝ [0] and P [0] Step 2: Coputaton of Kalan gan K [ = P [ H[ ( H[ P [ H [ + R[ ) (23) Fg. 4 Blo dagra of proposed IRCCM ethod usng FS Step 3: Updatng the estaton wth easureent X [ Sˆ [ = Sˆ [ + K[ ( X[ H[ Sˆ [ ) (24) Mosav: Infrared Counter-Countereasure Effent ehnques usng Neural Networ, 29

6 Step 4: Coputaton of error ovarane for updated estate P[ = ( I K[ H[ ) P [ (25) Downloaded fro eee.ust.a.r at 23:04 IRD on Monday June 8th 208 Step 5: Updatng the state transton atrx 0 0 A [ = 0 0 (26) a a a n n2 n3 where oeffents a, a, and a are oputed n n2 n3 by fttng a three order te-varyng Auto-Regressve (AR) odel [0]. Step 6: Predton S ˆ [ n + ] = A[ Sˆ[ P [ n + ] = A[ P[ A [ + Q[ (27) (28) Step 7: Iteraton Inreent te n by one unt and go ba to step 2. In Eq. (23) and Eq. (28), R [ and Q [ are obtaned as: Q ; = E[ w w = ] (29) 0 ; R ; = E[ v v = ] (30) 0 ; E [ w v ] = 0 ; for all and (3) Equatons (23)-(28) are used to perfor Kalan flterng. A te seres of N saple s provded. hen the oeffents of AR odel are oputed. AR s a well-nown odel used n dsrete-te stohast proesses. he odel update equatons autoatally provde one step predton. he odel updatng ontnues for eah predton. If the flare deteton odule delares the presene of a flare, the KF odule estates trang error sgnal. he CCM trang odule wll funton untl all flares ext the FOV. After ext of all flares fro FOV, the IRCCM ethod turns off CCM and returns to noral trang. Fg. 6 desrbes the proposed IRCCM ethod usng KF. 6 Experental Results Perforane of the proposed IRCCM ethods was assessed wth experents on IR retle seeer based on a DSP proessor. Fg. 7 shows ang deteton syste output. In ths fgure, Ch and Ch2 present nforaton sgnal and ang deteton syste output, respetvely. As shown n Fg. 7, output of ang deteton syste beoe fro +5V to -5V, after ang ourrene. Fg. 6 Proposed IRCCM ethod desrpton usng KF Fg. 7 Output of deteton syste, before and after ang ourrene (Ch: Inforaton sgnal and Ch2: Deteton syste output). Fgs. 8, 9, and 0 show proposed IRCCM ethods outputs usng NN, FS, and KF, respetvely. As shown n these fgures, the proposed IRCCM ethods an 220 Iranan Journal of Eletral & Eletron Engneerng, Vol. 5, No. 4, De. 2009

7 Downloaded fro eee.ust.a.r at 23:04 IRD on Monday June 8th 208 properly estate trang error sgnal after ang ourrene. Fg. shows a saple fro runnng of developed IRCCM sulator progra by paper author. able opares perforane of the proposed IRCCM ethods. As shown n ths table, FS ethod s ore aurate than other ethods. able Suessful perentage results of proposed IRCCM ethods IRCCM Present NN KF FS Method Suessful Perentage Algorth Fg. 8 Outputs of IRCCM ethod usng NN (Ch: NN output, Ch2: rang error sgnal, Ch3: Inforaton sgnal, and Ch4: Deteton syste output). Fg. 9 Outputs of IRCCM ethod usng FS (Ch: rang error sgnal, Ch2: Deteton syste output, Ch3: Inforaton sgnal, and Ch4: FS output) Fg. 0 Outputs of IRCCM ethod usng KF (Ch: Inforaton sgnal, Ch2: KF output, and Ch3: Deteton syste output) Fg. A saple fro runnng of developed IRCCM sulator progra 7 Conlusons In a target trang syste, an IRCCM algorth s requred for target effent trang under IRCM suh as IR flares. he funton of atve IR ang s to ause the seeer to ss ts ntended target by dsturbng the seeer trang proess. he atve IRCM ats n suh a way as to ause ether a oplete loss of target trang or to degrade target trang n suh a anner that the gudane of the seeer s affeted adversely. hs paper has presented desgn and pleentaton of three new IRCCM effent ethods usng NN, FS, and KF. An experental test setup was desgned and pleented for perforane evaluaton of the proposed ethods. he ethods valdty was verfed wth experents on IR seeer retle based on a DSP proessor. he pratal results ephaszed that the proposed algorths were hghly effetve and ould redue the ang effets. he experental results obtaned strongly supported the potental of the ethod usng FS to elnate the IRCM effet 83%. Mosav: Infrared Counter-Countereasure Effent ehnques usng Neural Networ, 22

8 Downloaded fro eee.ust.a.r at 23:04 IRD on Monday June 8th 208 Referenes [] Cohen R., Forra D. and Maer J., A ool for Infrared Countereasures Assessent, IEEE Conferene on Aerospae and Eletrons, pp.0-7, Otober [2] Forra D. P. and Maer J. J., Gener Models n the Advaned IRCM Assessent Model, Proeedng of the 200 Wnter Sulaton Conferene, pp , 200. [3] Mosav M. R., Asadpour M. and Kall R., Coparng Perforane of wo Infrared Ant- Jang Methods usng Fuzzy Syste and Neural Networ, 2007 Congress on Intellgent and Fuzzy Systes, Ferdows Unversty of Mashhad, Iran, August [4] Mosav M. R., Asadpour M., and Aer H. A., Desgn and Sulaton of an Infrared Jaer Soure for an Infrared Seeer, IEEE Conferene on Sgnal Proessng, Counatons, and Networng, Inda, January [5] Shn Y. and Ghosh J., he P-Sga Networ: An Effent Hgh-Order Neural Networ for Pattern Classfaton and Funton Approxaton, IEEE Conferene on Neural Networs, pp.3-8, 99. [6] Mosav M. R., A Pratal Approah for Aurate Postonng wth L GPS Reevers usng Neural Networs, Journal of Intellgent and Fuzzy Systes, Vol. 7, No. 2, pp.59-72, Marh [7] Cu S. H. and Zhu C. Q., Applaton of Kalan Flter to Bearng-Only arget rang Syste, IEEE Conferene on Sgnal Proessng, pp , 996. [8] Mosav M. R., Fuzzy Pont Averagng of the GPS Poston Coponents, Internatonal Conferene on Geographal Inforaton ehnology and Applatons, Chna, August [9] Mosav M. R., Coparng DGPS Corretons Predton usng Neural Networ, Fuzzy Neural Networ, and Kalan Flter, Internatonal Journal of GPS Solutons, Vol. 0, No. 2, pp.97-07, May [0] Mosav M. R., Mohaad K., and Refan M. H., e varyng ARMA Proessng on GPS Data to Iprove Postonng Auray, he Asan GPS Conferene 2002, pp.25-28, Otober Mohaad-Reza Mosav reeved hs B.S., M.S., and Ph.D. degrees n Eletron Engneerng fro Iran Unversty of Sene and ehnology (IUS), ehran, Iran n 997, 998, and 2004, respetvely. He s urrently faulty eber of IUS as assoate professor. Hs researh nterests nlude Artfal Intellgent Systes, Global Postonng Systes, Geograph Inforaton Systes and Reote Sensng. 222 Iranan Journal of Eletral & Eletron Engneerng, Vol. 5, No. 4, De. 2009