DESIGN OF DISTURBANCE DECOUPLED FILTER AND ITSAPPLICATION TO MANEUVERING TARGETS TRACKING

A novel disturbance decoupled filter (DDF) design scheme is presented. Firstly, the system with unknown input is translated into an equivalent system without unknown imputs by a simple algebraic transformation. Then, a new DDF design scheme, which is very simple, is proposed via innovations theorem. At last, the application of DDF to Maneuvering Targets Tracking is simulated and the simulation results show that DDF is suitable for high maneuvering cases.

Maneuvering target tracking using threshold interacting multiple model algorithm

To avoid missing track caused by the target maneuvers in automatic target tracking system, a new maneuvering target tracking technique called threshold interacting multiple model （TIMM） is proposed. This algorithm is based on the interacting multiple model （IMM） method and applies a threshold controller to improve tracking accuracy. It is also applicable to other advanced algorithms of IMM. In this research, we also compare the position and velocity root mean square （RMS） errors of TIMM and IMM algorithms with two different examples. Simulation results show that the TIMM algorithm is superior to the traditional IMM alzorithm in estimation accuracy.

Maneuvering target tracking of UAV based on MN-DDPG and transfer learning

Tracking maneuvering target in real time autonomously and accurately in an uncertain environment is one of the challenging missions for unmanned aerial vehicles(UAVs).In this paper,aiming to address the control problem of maneuvering target tracking and obstacle avoidance,an online path planning approach for UAV is developed based on deep reinforcement learning.Through end-to-end learning powered by neural networks,the proposed approach can achieve the perception of the environment and continuous motion output control.This proposed approach includes:(1)A deep deterministic policy gradient(DDPG)-based control framework to provide learning and autonomous decision-making capability for UAVs;(2)An improved method named MN-DDPG for introducing a type of mixed noises to assist UAV with exploring stochastic strategies for online optimal planning;and(3)An algorithm of taskdecomposition and pre-training for efficient transfer learning to improve the generalization capability of UAV’s control model built based on MN-DDPG.The experimental simulation results have verified that the proposed approach can achieve good self-adaptive adjustment of UAV’s flight attitude in the tasks of maneuvering target tracking with a significant improvement in generalization capability and training efficiency of UAV tracking controller in uncertain environments.

Multiple model efficient particle filter based track-before-detect for maneuvering weak targets

It is a tough problem to jointly detect and track a weak target, and it becomes even more challenging when the target is maneuvering. The above problem is formulated by using the Bayesian theory and a multiple model(MM) based filter is proposed. The filter presented uses the MM method to accommodate the multiple motions that a maneuvering target may travel under by adding a random variable representing the motion model to the target state. To strengthen the efficiency performance of the filter,the target existence variable is separated from the target state and the existence probability is calculated in a more efficient way. To examine the performance of the MM based approach, a typical track-before-detect(TBD) scenario with a maneuvering target is used for simulations. The simulation results indicate that the MM based filter proposed has a good performance in joint detecting and tracking of a weak and maneuvering target, and it is more efficient than the general MM method.

Maneuvering target tracking algorithm based on cubature Kalman filter with observation iterated update

Reasonable selection and optimization of a filter used in model estimation for a multiple model structure is the key to improve tracking accuracy of maneuvering target.Combining with the cubature Kalman filter with iterated observation update and the interacting multiple model method,a novel interacting multiple model algorithm based on the cubature Kalman filter with observation iterated update is proposed.Firstly,aiming to the structural features of cubature Kalman filter,the cubature Kalman filter with observation iterated update is constructed by the mechanism of iterated observation update.Secondly,the improved cubature Kalman filter is used as the model filter of interacting multiple model,and the stability and reliability of model identification and state estimation are effectively promoted by the optimization of model filtering step.In the simulations,compared with classic improved interacting multiple model algorithms,the theoretical analysis and experimental results show the feasibility and validity of the proposed algorithm.

A novel multi-sensor multiple model particle filter with correlated noises for maneuvering target tracking

Aiming at the effective realization of particle filter for maneuvering target tracking in multi-sensor measurements,a novel multi-sensor multiple model particle filtering algorithm with correlated noises is proposed.Combined with the kinetic evolution equation of target state,a multi-sensor multiple model particle filter is firstly constructed,which is also used as the basic framework of a new algorithm.In the new algorithm,in order to weaken the adverse influence from random measurement noises in the measuring process of particle weight,a weight optimization strategy is introduced to improve the reliability and stability of particle weight.In addition,considering the correlated noise existing in the practical engineering,a decoupling method of correlated noise is given by the rearrangement and transformation of the state transition equation and measurement equation.Since the weight optimization strategy and noise decoupling method adopt respectively the center fusion structure and the off-line way,it improves the adverse effect effectively on computational complexity for increasing state dimension and sensor number.Finally,the theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.

A Fuzzy Adaptive Algorithm Based on“Current”Statistical Model for Maneuvering Target Tracking

The basic"current"statistical model and adaptive Kalman filter algorithm can not track a weakly maneuvering target precisely,though it has good estimate accuracy for strongly maneuvering target.In order to solve this problem,a novel nonlinear fuzzy membership function was presented to adjust the upper and lower limit of target acceleration adaptively,and then the validity of the new algorithm for feeblish maneuvering target was proved in theory.At last,the computer simulation experiments indicated that the new algorithm has a great advantage over the basic"current"statistical model and adaptive algorithm.

Tracking maneuvering target based on neural fuzzy network with incremental neural leaning

The scheme for tracking maneuvering target based on neural fuzzy network with incremental neural learning is proposed. When tracked target maneuver occurs, the scheme can detect maneuver immediately and estimate the maneuver value accurately , then the tracking filter can be compensated correctly and duly by the estimated maneuver value. When environment changes, neural fuzzy network with incremental neural learning （INL-SONFIN） can find its optimal structure and parameters automatically to adopt to changed environment. So, it always produce estimated output very close to the true maneuver value that leads to good tracking performance and avoids misstracking. Simulation results show that the performance is superior to the traditional schemes and the scheme can fit changed dynamic environment to track maneuvering target accurately and duly.

A Maneuvering Target Tracking Filter in Mixed Coordinate System

A kalman filter in the spherical-rectangular coordinate system to track a maneuvering target is introduced. A dynamic model with extended state vector is proposed in this paper to improve the state estimation (i.e. position, velocity and acceleration) even the sensor data(i.e. range, azimuth angle and elevation angle ) is color contaminated. The Kalman filter equations are decoupled by proper coordinate transformation and using filter gain rotation algorithm. Monto Carlo simulation is performed for different kinds of target trajectories(with the same measurement noise) and the root mean square values of estimation errors are computed. Results show that there is significant improvement in tracking capability over the methods discussed by other researchers.

Maneuvering target tracking algorithm based on CDKF in observation bootstrapping strategy

The selection and optimization of model filters affect the precision of motion pattern identification and state estimation in maneuvering target tracking directly.Aiming at improving performance of model filters,a novel maneuvering target tracking algorithm based on central difference Kalman filter in observation bootstrapping strategy is proposed.The framework of interactive multiple model(IMM) is used to realize identification of motion pattern,and a central difference Kalman filter(CDKF) is selected as the model filter of IMM.Considering the advantage of multi-sensor fusion method in improving the stability and reliability of observation information,the hardware cost of the observation system for multiple sensors is adopted,meanwhile,according to the data assimilation technique in Ensemble Kalman filter(En KF),a bootstrapping observation set is constructed by integrating the latest observation and the prior information of observation noise.On that basis,these bootstrapping observations are reasonably used to optimize the filtering performance of CDKF by means of weight fusion way.The object of new algorithm is to improve the tracking precision of observed target by the multi-sensor fusion method without increasing the number of physical sensors.The theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.

Ant colony optimization for bearings-only maneuvering target tracking in sensors network

In this paper, the problem of bearings-only maneuvering target tracking in sensors network is investigated. Two objectives are proposed and optimized by the ant colony optimization （ACO）, then two kinds of node searching strategies of the ACO algorithm are presented. On the basis of the nodes determined by the ACO algorithm, the interacting multiple models extended Kalman filter （IMMEKF） for the multi-sensor bearings-only maneuvering target tracking is introduced. Simulation results indicate that the proposed ACO algorithm performs better than the Closest Nodes method. Furthermore, the Strategy 2 of the two given strategies is preferred in terms of the requirement of real time.

Application of Adaptive Reduced Sigma Points Unscented Kalman Filter to the Tracking of Maneuvering Target

Based on the principle of statistical linear regression, a set of n ＋ 2 sigma points instead of 2n ＋ 1 sigma points used in the unscented Kalman filter （UKF）, is constructed to approximate the system state. And filter accuracy is second order. Real-time of modified UKF is improved. In order to describe accurately the maneuvering target, the ＂current＂ statistical model is used. And the equation of acceleration error covariance is modified at every sample time of the filter. The modified adaptive UKF is presented for estimating the position, velocity and acceleration of maneuvering target. Monte Carlo simulations show the modified adaptive UKF acquires good performance for tracking position of maneuvering target. The modified adaptive UKF has better computational efficiency than UKF.

ALGORITHMS FOR TRACKING MANEUVERING TARGET WITH PHASED ARRAY RADAR

Several typical algorithms for tracking maneuvering target with phased array radar are studied in this paper. The constant gain filter with multiple models is analyzed. A typical method for adaptively controlling the sampling interval is modified. The performance of the single model and multiple model estimator with uniform and variable sampling interval are evaluated and compared. It is shown by the simulation results that it is necessary to apply the adaptive sampling policy based on the multiple model method when the maneuvering targets are tracked by the phased array radar since saving radar resources is more important. The adaptive algorithms of variable sampling interval are better than the algorithms of variable model. The adaptive policy to determine the sampling interval based on multiple model are superior than those based on the single model filter, because IMM estimator can adapt to the maneuver more quickly and the prediction covariance of IMM is the more sensitive and more reliable index than residual to determine the sampling interval. With IMM based method, lower sampling interval is required for a certain accuracy.

A combination algorithm of Chaos optimization and genetic algorithm and its application in maneuvering multiple targets data association

The most important problem in targets tracking is data association which may be represented as a sort of constraint combinational optimization problem. Chaos optimization and adaptive genetic algorithm were used to deal with the problem of multi-targets data association separately. Based on the analysis of the limitation of chaos optimization and genetic algorithm, a new chaos genetic optimization combination algorithm was presented. This new algorithm first applied the "rough" search of chaos optimization to initialize the population of GA, then optimized the population by real-coded adaptive GA. In this way, GA can not only jump out of the "trap" of local optimal results easily but also increase the rate of convergence. And the new method can also avoid the complexity and time-consumed limitation of conventional way. The simulation results show that the combination algorithm can obtain higher correct association percent and the effect of association is obviously superior to chaos optimization or genetic algorithm separately. This method has better convergence property as well as time property than the conventional ones.

Adaptive Maneuvering Frequency Method of Current Statistical Model

Current statistical model(CSM) has a good performance in maneuvering target tracking. However, the fixed maneuvering frequency will deteriorate the tracking results, such as a serious dynamic delay, a slowly converging speedy and a limited precision when using Kalman filter(KF) algorithm. In this study, a new current statistical model and a new Kalman filter are proposed to improve the performance of maneuvering target tracking. The new model which employs innovation dominated subjection function to adaptively adjust maneuvering frequency has a better performance in step maneuvering target tracking, while a fluctuant phenomenon appears. As far as this problem is concerned, a new adaptive fading Kalman filter is proposed as well. In the new Kalman filter, the prediction values are amended in time by setting judgment and amendment rules,so that tracking precision and fluctuant phenomenon of the new current statistical model are improved. The results of simulation indicate the effectiveness of the new algorithm and the practical guiding significance.

A New Particle Filter for Nonlinear Tracking Problems

A new particle filter is presented for nonlinear tracking problems. Inpractice, maneuvering target-tracking systems are usually nonlinear and incompletely observed, andthe main difficulty of maneuvering target-tracking problem lies in the fact that the maneuverabilityat every step is of high uncertainties. Here a new smoothing particle filter algorithm is proposed,which combines the particle filter to tackle the non-linear and non-Gaussian peculiarities of theproblem, together with smoothing of the PDF of system modes and thus settles the estimate problem ofthe target maneuverability. The simulation comparison with the auxiliary particle filters showsthat the approach has superiority and yields performance improvements in solving nonlinear trackingproblems.

Maneuvering target state estimation based on separate model-ing of target trajectory shape and dynamic characteristics

The state estimation of a maneuvering target,of which the trajectory shape is independent on dynamic characteristics,is studied.The conventional motion models in Cartesian coordinates imply that the trajectory of a target is completely determined by its dynamic characteristics.However,this is not true in the applications of road-target,sea-route-target or flight route-target tracking,where target trajectory shape is uncoupled with target velocity properties.In this paper,a new estimation algorithm based on separate modeling of target trajectory shape and dynamic characteristics is proposed.The trajectory of a target over a sliding window is described by a linear function of the arc length.To determine the unknown target trajectory,an augmented system is derived by denoting the unknown coefficients of the function as states in mileage coordinates.At every estimation cycle except the first one,the interaction(mixing)stage of the proposed algorithm starts from the latest estimated base state and a recalculated parameter vector,which is determined by the least squares(LS).Numerical experiments are conducted to assess the performance of the proposed algorithm.Simulation results show that the proposed algorithm can achieve better performance than the conventional coupled model-based algorithms in the presence of target maneuvers.

Adaptive tracking algorithm based on 3D variable turn model

Satisfactory results cannot be obtained when three-dimensional (3D) targets with complex maneuvering characteristics are tracked by the commonly used two-dimensional coordinated turn (2DCT) model. To address the problem of 3D target tracking with strong maneuverability, on the basis of the modified three-dimensional variable turn (3DVT) model, an adaptive tracking algorithm is proposed by combining with the cubature Kalman filter (CKF) in this paper. Through ideology of real-time identification, the parameters of the model are changed to adjust the state transition matrix and the state noise covariance matrix. Therefore, states of the target are matched in real-time to achieve the purpose of adaptive tracking. Finally, four simulations are analyzed in different settings by the Monte Carlo method. All results show that the proposed algorithm can update parameters of the model and identify motion characteristics in real-time when targets tracking also has a better tracking accuracy.

ADAPTIVE MULTIPLE MODEL FILTER USING IMM AND STF

In fault identification, the Strong Tracking Filter (STF) has strong ability to track the change of some parameters by whitening filtering innovation. In this paper, the authors give out a modified STF by searching the fading factor based on the Least Squared Estimation. In hybrid estimation, the well known Interacting Multiple Model (IMM) Technique can model the change of the system modes. So one can design a new adaptive filter — SIMM. In this filter, our modified STF is a parameter adaptive part and IMM is a mode adaptive part. The benefit of the new filter is that the number of models can be reduced considerably. The simulations show that SIMM greatly improves accuracy of velocity and acceleration compared with the standard IMM to track the maneuvering target when 2 model conditional estimators are used in both filters. And the computation burden of SIMM increases only 6% compared with IMM.

An Algorithm of the Adaptive Grid and Fuzzy Interacting Multiple Model

This paper studies the algorithm of the adaptive grid and fuzzy interacting multiple model （AGFIMM） for maneuvering target tracking, while focusing on the problems of the fixed structure multiple model （FSMM） algorithm＇s cost-efficiency ratio being not high and the Markov transition probability of the interacting multiple model （IMM） algorithm being difficult to determine exactly. This algorithm realizes the adaptive model set by adaptive grid adjustment, and obtains each model matching degree in the model set by fuzzy logic inference. The simulation results show that the AGFIMM algorithm can effectively improve the accuracy and cost-efficiency ratio of the multiple model algorithm, and as a result is suitable for enineering apolications.