智能卡尔曼滤波概述.doc

Intelligent Kalman lter for tracking a manoeuvringtargetKalman filter is an ideal random linear adaptive filter. It needs to clearly define the Mathematical model to describe the relationship between output and input ,it is the model of the controller of Mathematical model which works well while the function between output and input is determined.To the unknown environment,controller of Mathematical model usually deal with it by Probability statistics(Conditional mean, covariance),the linear or Markov model are often used in practice. but there are some problems: （1）It is hard to describe the Uncertainty clearly by classical date model. （2）It is hard to add the knowledge from domain experts to the system ,it can only use it to estimate conditions of the initial state and covariance. So,when manoeuvring to turn or taking evasive action, the standard KF cannot be applied,because the unknown target acceleration during the manoeuvre appears as extensive process noise on the target model, and the original process noise variance cannot cover it.The traditional research method and its shortcomings at present are as follows:1：Detecting the manoeuvre and then to cope with it effectively.Examples of this approachinclude input estimation (IE) techniques , the variable dimension (VD) lter, the two-stage Kalman estimator. shortcoming：these techniques require additional effort such as the estimation and detection of acceleration, and the compensation of the state estimate or the transition between the non-manoeuvring lter and the manoeuvring lter in order to deal with the unknown target manoeuvres.2：describeing the motion of a target using multiple sub-lters. The generalised pseudo-Bayesian (GPB) method, the interacting multiple model (IMM) method ,and the adaptive interacting multiple model (AIMM) method 10 are included in this approach. shortcoming：These techniques also need extra effort such as predening multiple sub-lters and updating the model transition probability, in addition to the large computational load imposed by using multiple sub-filters.The author of this paper propose a IKF of optimized fuzzy system based on GA method.Advantages are as follows：（1） Unlike the IE technique and the similar methods, the proposed IKF requires no additional effort for estimation and detection of the target manoeuvre, and compensation of the state estimate or the transition between the non-manoeuvring lter and the manoeuvring lter to treat the target manoeuvre.（2）Unlike multiple model methods, no extra effort such as the predenition of multiple sub-lters and the updating of the model transition probability is needed.（3）Since the proposed lter can effectively track a manoeuvring target with only a lter and an offline optimised fuzzy system, it is possible to reduce the computational load and to establish a simpler tracking algorithm for a manoeuvring target. Real-time tracking systems generally use radar or other equipment to detect altitude and azimuth between targets and plane of the equipment Controlling the detection direction of the equipment by two motors, by continuously adjusting the speed of two motors to maintain the tracking of targets. the control of the height and azimuth can use the same algorithm.Linear discrete-time maneuvering target model can be expressed as: where is the overall process noise with time-varyinvariance : From the fact that in the presence of a target manoeuvre, the residual of the KF increases in proportion to its magnitude, the variance can be determined from the residual and its variation at every sampling instant, and so we can treat the target manoeuvre by adjusting this process noise varianceThe time-varying overall process noise variance for the IKF is inferred by a double-input single-output (DISO)fuzzy system, for which the jth fuzzy IFTHEN rule is represented bywhere two premise variables w1 and w2 are the lter residual and its variation, respectively, and a consequence variable y is the process noise variance qj,Aij, are fuzzy sets i1,2 and j1,2,M, and throughout this paper, it has the Gaussian membership function with centre cij and standard deviation sij as follows:By using a singleton fuzzier, product inference and centre-average defuzzier, is approximated in the following form: Offline optimization of fussy system： fussy system used in IKF2： Since the design parameters of fuzzy controller touch a lot (mainly the credibility of fuzzy control rules and regulations, input / output variable scale factor, input and output variables of the fuzzy set membership function), and the nonlinear performance and other reasons, it still lacks an effective general method for fuzzy system design and adjustment, and designers mainly rely on experience and repeated debuging.So this problem need for further research .GA proposed by Holland from the United States in 1962 ,it is a optimization method imitating the biological evolution process ,advantages including no require for gradient, simple to obtain the global optimal solution algorithm , supporting parallel processing ,it mainly used for optimizing fuzzy system input and output scaling factors, membership functions and control rules.the fuzzy system should be designed such that the following objective function is minimised:： J=The GA and the DNA coding method represent the search variables of the given optimisation problem as a chromosome containing one or more substrings. In this case, the search variables are the centre cij and the standard deviation of a Gaussian membership function of the fuzzy set Aij,and the singleton output qj: A convenient way to convey the search variables into a chromosome is to gather all search variables associated with the jth fuzzy rule into a string andto concatenate the strings asSince the rules of original fuzzy system are too much ,considering optimizing the number of the rule set,we use a tness function of the formWhere is a positive scalar to adjust the weight between the objective function and the rule number. By comparing the simulation results of GA-based IKF and the IMM,AIMM method,we find that he normalised position and velocity errors of the GA-based IKF are reduced by 8.538:62% and 36.1638:35%; respectively, compared with the IMM method, and by 7.687:78% and 28.2729:97%; respectively, compared with the AIMM method in the average sense.This implies that the GA-based IKF provides smaller position errors and velocity errors at almost every scan time, specially during manoeuvring time intervals,than the IMM and the AIMM methods. This is because,although the properties of the manoeuvres are unknown, the time-varying variance of the overall process noise can be well approximated via the fuzzy system, whereas the IMM and the AIMM methods cannot effectively deal with the complex properties of the manoeuvring target. Moreover, since the proposed method utilises a lter and a fuzzy system for tracking a manoeuvring target, the CPU time of the GA-based IKF over 200 runs is reduced by 47:58% and 73:49%; respectively, compared with IMM3 and IMM5 and by 55:52% and 75:83%; respectively, compared with AIMM3 and AIMM5.The membership function parameters of GA-based fuzzy optimization system are as follows: In this paper , the authors propose a GA-based fuzzy systems with off-line optimization . the using of genetic algorithm optimization is a research focus in the application of fuzzy systems in recent years , It is mainly used for the optimization of scaling factors of input and output, membership functions and control rules,Since the completing process of conventional genetic algorithm need some time , the optimization process still remains in the offline ways.There are some improved genetic algorithm speeding up the convergence rate, but still can not reach the on-line optimization, so to a large extent limited the improvement of the fuzzy system performance.I think that if there is a dynamic optimization fuzzy system based on GA method .I raised some points for improvements:（1） Conventional GA-based optimization method is to optimize the parameters together,evolution of a large number of complicated operations and processing will take some time combined with calculation of the fitness function also takes time.Therefore, conventional optimization algorithm can only work in offline way, which will affect effects of its control process,The new proposed genetic algorithm based dynamic optimization of fuzzy systems each only activate the current Correction values of the parameters of the fuzzy system，Achieve the line optimization of