Open Access The Study on the Dynamic Multi-objective Recognition and Estimation Algorithm of Infrared Imaging Based on Particle Swarms Collaboration

Transcription

1 Send Orders for Reprints to 1400 The Open Autoation and Control Systes Journal, 014, 6, Open Access The Study on the Dynaic Multi-objective Recognition and Estiation Algorith of Infrared Iaging Based on Particle Swars Collaboration Lang Zhai * and Qi Hu College of Inforation Engineering, Jilin Business and Technology College, Changchun, Jilin, 13006, China Abstract: Recently, a proble that the infrared decoy interferes infrared detection syste, cannot be solved. With the gradual application and popularity of the particle swar optiization, it is preferable to apply it to dynaic ultiobjective optiization to solve the proble of the recognition and the estiation of dynaic ulti-object in infrared iaging. In this study, the dynaic ulti-objective estiation and recognition algorith of the infrared iaging, which is based on the ulti-particle swars collaboration, ultiately estiates the otion trajectory and Pareto optial solution of the infrared iaging through the continuous iproveent and upgradation of the particle swars optiized algorith, the continuous study and inheritance as well as the cobination with the aerodynaic characteristic of the infrared decoy. The experient proves that the iproveent of particle swar algorith efficiently reduces the estiation error, which produces favorable optiized effects. The experient has great engineering significance. Keywords: DMOP, Infrared iaging l, Multiple particle swars, Pareto optial solutions. 1. INTRODUCTION The infrared iaging technology has advantages in target detection, recognition and anti-interference, thus it gradually attracts a wider attention. As the iaging technology develops, new requireents are put forward for the test and the estiation of iaging syste and its corresponding infrared detection weapon technology. As for the infrared iaging sensor, its ain function is to trace and recognize the tail flae iaging of the aircraft and to eliinate the interfered iaging of the infrared decoy eanwhile [1-4]. Hence the proble about the recognition and estiation of dynaic ulti-object can be recognized and estiated by collaborative optiization algorith. Dynaic Multi-Objective Proble denotes that the optiization of objective function, the nuber of objective function and the diension of decision space will change over tie. It also includes the ulti-objective proble of changing constraint conditions and paraeters. Dynaic Multi- Objective Proble is the cobination of dynaic optiization proble and ulti-objective optiization proble, which attracts a wider attention of doestic and overseas scholars [5-8]. In fact, Dynaic Multi-Objective Proble has a wide application background. For exaple, the ultiobjective optiization of the issile s flying trace and the control paraeter (angle of attack, elevation angle, etc.) should be processed as the target of a issile is changed. Affected by the severe weather, aircraft faults and the accidents that happen to the passengers on a flying aircraft, the factors such as the stranded tie of the passengers and the cost of operation should be taken into account to get *Address correspondence to this author at the College of Inforation Engineering, Jilin Business and Technology College, Changchun, Jilin, 13006, China; Tel: ; E-ail: livazl@16.co the aviation dispatcher re-optiized. The Dynaic Multi- Objective Proble that widely exists in the physical world desires the appropriate algorith to iprove the ability to solve this proble. There are a few research results about Dynaic Multi- Objective Proble at present. Farina et al. create the first dynaic ulti-objective function to put forward the algorith of dynaic ulti-objective optiization; Greeff et al. utilize the vector to estiate the particle swar algorith to solve the Dynaic Multi-Objective Proble with each object optiized by a particle. Enlightened by the recognition algorith of the dynaic syste ode, Talukder et al. put forward a new solution which uses a new operator to track the Pareto solution set of the decision space. ChiKiong et al. utilize the utual collaboration of ultiple populations to solve the Dynaic Multi-Objective Proble, which prove that ultiple populations collaboration can fast track the changing Pareto optial solution set and Pareto optial front. Doestic scholars such as Liu Chunan et al. create a new pattern of dynaic ulti-objective optiization algorith and iprove its convergence.. THE PERFORMANCE CHARACTERISTICS OF INFRARED DECOY Infrared iaging can be taken as the effects acted by the factors such as tie, space, and spectru of radiation energy on propagation of the infrared radiation of scene in a ediu. It can also be taken as a process of transferring and conversion of energy in detection equipent. As for the infrared detector, the priary task is to track and recognize the target aircraft. But the recognition degree of the target aircraft s tail flae and the infrared decoy is low for infrared detectors, which deterines that the infrared iaging recognition and / Bentha Open

2 The Study on the Dynaic Multi-objective Recognition The Open Autoation and Control Systes Journal, 014, Volue estiation give rise to the great difficulty and coplexity in calculation. Thus, it has a great significance to analyze the dynaic characteristics of the infrared decoy and its trace estiation. The infrared decoy is generally divided into rising tie period, lasting tie period and falling tie period fro eission to burning-up and disappearance. When the eission starts, the radiation intensity of burning will increase rapidly fro 0. And as the radiation intensity reaches the axiu extent, it begins to aintain the burning intensity to for the infrared iage which is sae as the attacking target aircraft tail flae. Along with the gradual exhaustion of the fuel, the radiation intensity decreases till it falls and burns up. Cut off the aircraft, the infrared decoy will do parabolakind oveent under the dual action of aerodynaic drag and gravity of projectile bodies. Above all, the drag acceleration to the infrared decoy is calculated as the following forula: α d ( t) ( ) ρ ( ) W( t) ρ gv t gv t C A = = β a f a f d ref In the above, is the atospheric density; is the gravity acceleration; is the speed of the infrared decoy; β is the trajectory coefficient; is the quality of the infrared decoy; is the resistance coefficient; is the referenced area of resistance coefficient. ( ) ( ) ( ) α ( +Δ ) = ( ) + ( ) Δ + α Δ ( ) ( ) ( ) ( α ) xf t+δ t = xf t + vfx t Δ t+ dx Δt / yf t t yf t vfy t t dy t / zf t+δ t = zf t + vfz t Δ t+ g+ dz Δt Therefore, the infrared testing targets such as the infrared decoy and the tail flae of the target aircraft can be seen as particle swars in the process of recognizing the infrared decoy. The otion trace of the infrared decoy is estiated after the ulti-saple analysis of particle swars and the cobination of its own radiation characteristics and aerodynaic characteristics, which ultiately gives feedback to the detector to get a quick and exact estiation. 3. DYNAMIC MULTI-OBJECTIVE PROBLEM AND ITS RELEVANT CONCEPTS Dynaic Multi-Objective Proble is deonstrated as the following forula: In the forula above, is the decision vector of the decision space X, is the target function set, is the nuber of the targets, t is the function of tie, g, h is the corresponding inequality and the equality constraint function. Soe iportant definition of dynaic ulti-objective optiization can be directly extended in the definition of the static ulti-objective optiization. / ( ) = { 1( ) ( ) L ( ) } x X in x, f x,, x,, f x,,, (1) () (3) and Definition 1: Pareto doinance deotes that if Thus we can get: doinates f deonstrated as f1 p f. Definition : the optial front of dynaic Pareto {, /, p,,, } POF = f f f f F * * * t i t j t i t j t (4), which can be Definition 3: the optial solution of dynaic Pareto. The coon point of dynaic ulti-objective optiization and dynaic single-objective optiization is that the present optial solution will change at the next oent. The key difference is that the forer is a ulti-diensional target fitness space F. But the latter is just the single-objective optiization. Hence, the Pareto optial front and Pareto optial non-doinated solution set of Dynaic Multi- Objective Proble can possibly change over tie. 4. HE DYNAMIC MULTI-OBJECTIVE RECOGNI- TION AND ESTIMATION ALGORITHM BASED ON PARTICLE SWARMS COLLABORATION At present, ultiple population that utilizes various ethods to collaborate in the fraework of collaboration helps to iprove the optiization efficiency of the algorith. Potter illustrates in his doctoral dissertation in detail that the introduction of ecological odels and the collaborative fraework effectively iprove the efficiency of classic evolutionary algorith [9-1]. Major scholars apply the strategy of the paralleled search of ultiple populations to respond to the changing environent. However, the ultiple population parallel optiizations is not equal to the ultiple population collaborative optiization, whose key difference is that the collaborative evolution has unique evolutionary dynaics echanics, naely, ultiple sub-population has utual effects at individual level. And the ultiple sub-populations becoe the selection pressure of each one of the and drive each other to iprove the characteristics and coplexity of each one to achieve the coevolution. The ultiple population collaborative optiizations is used to solve the coplex proble, whose first step is to decopose the proble. Next is the collaboration of the population. The particle swar collaborative odel can be divided into two types according to the existing docuents; one is based on space decoposing and the other is based on proble decoposing. As for the collaborative algorith, it is not sufficient to apply the copetitive echanis and the collaborative echanis. There in no copetition between each particle swar algorith but the collaboration, because of the fact that the algorith easily converges and gets into a local extreu. If the genetic algorith has only selection F { / (, ) p (, ), (, ) } POS = x f x t f x t f x t F * * * t i j i j M (5) (6)

3 140 The Open Autoation and Control Systes Journal, 014, Volue 6 Zhai and Hu Fig. (1). The coparison diagra of PSO. Fig. (). The coparison diagra of PSO. operator instead of utation operator, the algorith is easy to converge. Thus the search precision is greatly iproved. In the collaborative odule, ultiple sub-population is ainly applied to do the collaborative search in search space. The decoposition approach of the search space applied is display space decoposition, which eans that each sub-population search on one diension or ultiple diensions of the solution space. After that, ultiple subpopulations collaborate to for a coplete solution vector while calculating the individual fitness value. This kind of collaborative approach is proved effective in any docuents, whose exact search on local area is adaptive. Based on the above notion, this paper introduces the ultiple particle swar collaborative optiization algorith which utilizes the ixed copetition and the collaborative echanis of particle swar algorith when solving the dynaic ulti-objective optiization proble. 5. THE EXPERIMENTAL RESULTS 5.1. The Generation of Spread Spectru Waterarking Firstly, PSO is initialized into a group of rando particles (rando solution). Next is to search for the optial solution through iteration. During each ties of iteration, a particle updates its own speed and location through individual extreu value and neighborhood extreu: ( ) ( ) v = w v + c f p x + c f l x i i 1 rand i i rand i i xi = xi + vi Aong the above, inertia weight is used to control the effects on the present iteration speed which is acted by the speed of previous iteration. The global search features of the (7) (8)

4 The Study on the Dynaic Multi-objective Recognition The Open Autoation and Control Systes Journal, 014, Volue Fig. (3). The coparison diagra of PSO. particle swar optiization algorith are realized by rando initialization. The general inertia weight is w [ 0,1]. is the individual extreu and is the neighborhood extreu. is a rando nuber fro 0 to 1. The forulas (1) and () are the speed displaceent updating forulas which are the cores of the traditional PSO algorith. In the forulas, this experient sets up the learning factor: c1 = c =. Suppose that the nuber of siultaneously releasing the infrared decoy is 9. Adding up with the target aircraft tail flae, the nuber of the object which is going to be recognized by the infrared seeker is 10, which eans that the space diension where particles exist is N = 10. Initially, set 50 particles. Then ake ties iteration and circulate 100 ties to get the optial solution. During the process of 100 ties search of optiization, ten of the find real attacking targets. The interference of infrared decoy is successfully excluded. To prove the effectiveness of the algorith in this paper, the iproved particle swar algorith put forward here is synthetically copared with the traditional particle swar optiization algorith. This experient set three control groups which use the original particle swar optiization algorith, whose nuber of particles is 50, 100 and 00 respectively. Make sapling of optial solution as each group copletes relevant calculating aount and record the results. Then draw a coparison diagra as Figs. (1-3). In this experient, the changing curve of the optiization results has obvious convergence effects. And the speed of convergence is not affected by the changing nuber of particles. In the eantie, the above diagra deonstrates that the target function is greatly affected by particles when applying the original PSO, which is represented as: if the nuber of particles is too few, the effects of searching optiization are ade poor though the speed of convergence is fast. It can be hardly ensured that the optial solution can be found (50 particles and 100 particles); If the nuber of particles is high, the speed of convergence is slow though better solutions can be found (00 particles). But MPSO can well coproise the converging speed and the searching optiization to guarantee not only the converging speed but also better optiization results. In ters of the ultiate optiization results, the optiization result of iproved algorith is slightly better than that of the particle swar algorith. CONCLUSION This paper utilizes the Dynaic Multi-Objective Proble to recognize and estiate the detection iage of the infrared iaging and to estiate and judge the trace of aerodynaic characteristics of the infrared decoy through the theory of ultiple particle collaborative optiizations. This study designs the strategy of ultiple particle collaborate repair and overcoes the shortcoings that the optiization results is greatly affected by the nuber of particles in standard algorith. The strategy not only guarantees the optiization results but also the converging speed. This experient deonstrates that the approach put forward in this paper can well solve the proble that the infrared seeker can recognize and estiate the inference of infrared decoy, which has great engineering significance. CONFLICT OF INTEREST The authors confir that this article content has no conflict of interest. ACKNOWLEDGEMENTS Declared none. REFERENCES [1] K. Subbarao, and B. M. Shippey, Hybrid genetic algorith collocation ethod for trajectory optiization, Journal of Guidance, Control, and Dynaics, vol. 3, no. 4, pp , 009. [] F. Bergh, and A.P. Engelbrecht, Training product unit networks using cooperative particle swar optiizers, In: Proceedings of International Joint Conference On Neural Networks, Washington. vol. 1, pp , 001. [3] H. Yoshida, K. Kawata, and F. Yoshikazu, A Particle swar optiization for reactive power and voltage control considering voltage security assessent, IEEE Transactions on Power Syste, vol. 15, no. 4, pp , 000. [4] P. Willias, Herite-Legendre-Causs-Lobatto direct transcription in trajectory optiization, Journal of Gudance, Control and Dynaics, vol. 3, no. 4, pp , 009.

5 1404 The Open Autoation and Control Systes Journal, 014, Volue 6 Zhai and Hu [5] A. Wuerl, T. Crain, and E. Braden, Genetic algorith and calculus of variations based trajectory optiization technique, Journal of Spacecraft and Rockets, vol. 40, no. 6, pp , 003. [6] M. Pontani, and B. A. Conway, Particle swar optiization applied to space trajectories, Journal of Guidance, Control, and Dynaics, vol. 33, no. 5, pp , 010. [7] L. Gao, and H. B. Gao, Particle swar optiization based algorith for cutting paraeters selection, In: Proceedings of IEEE World Congress on Intelligent Control and Autoation. Hangahou, 004, vol. 4, pp [8] G. T. Huntington, and A. V. Rao, Coparison of global and lo cal collocation ethods for optial control, Journal of Guidance, Control, and Dynaics, vol. 31, no., pp , 008. [9] J. S. Alsuait, J. K. Sykulski, and A. Al-Othan, A hybrid GA- PS-5QP ethod to solve power syste valve-point econoic dispatch probles, Applied Energy, vol. 87, no. 5, pp , 010. [10] K. E. Parsopoulos, and M. N. Vrahatis, Recent approaches to global optiization probles through particle swar optiization, Natural Coputing, vol. 1, no.1, pp , 00. [11] A. Lopez, C.A.C. Coello, A. Oyaa, and K. Fujii, An alternative preference relation to deal with any-objective optiization probles, In: R.C. Purshouse, P.J. Fleing, C.M. Fonseca, S. Greco, J. Shaw, Eds. In: Proceedings of the 3 rd International Conference on Evolutionary Multi Criterion Optiization, Berlin, pp , 013. [1] L.S. Batista, F. Capelo, F.G. Guiaraes, and J.A. Rairez, A coparison of doinance criteria in any-objective optiization probles, In: Proceedings of the IEEE Congress on Evolutionary Coputation, Piscataway: IEEE Service Center, pp , 011. Received: Septeber 16, 014 Revised: Deceber 3, 014 Accepted: Deceber 31, 014 Zhai and Hu; Licensee Bentha Open. This is an open access article licensed under the ters of the Creative Coons Attribution Non-Coercial License ( which perits unrestricted, non-coercial use, distribution and reproduction in any ediu, provided the work is properly cited.