SDDRE versus IMM based Estimation of 3D Coordinated Turn Target Maneuvers

Previous papers (1997, 1999, 2000) have described a tracking approach which utilized a Combined Kalman Filter (CKF), adaptive tracking for maneuver tracking, and the JVC association algorithm for reports to tracks, for use in Airborne Early Warning (AEW) applications. In this paper we present our incorporation of Interacting Multiple Model (IMM) tracking. First our previous AEW tracking approach is briefly reviewed as most of this approach is still utilized and forms our baseline. The new IMM approach and equations are then described. Then the two IMM tracking approaches used are discussed. One involves a two model IMM containing two constant velocity models, one a low process noise and the other a high process noise model. The other approach involves three IMM filter models, a coordinated turn filter model, and the same two constant velocity filter models as in the two model IMM approach. Results for both IMM approaches and the baseline tracker are shown. The results presented involve a 120 target scenario with two second update time with simulated radar data. In addition computer timing results are presented. These results indicate that while the three model IMM approach provides the best tracking results, it does so at a substantial computational cost. The two model IMM provides comparable tracking improvement but at a far less computational cost.

This paper presents a simple yet efficient adaptive Kalman filter for tracking targets expected to perform varying turn maneuvers. The process noise covariance level of a second order Kalman filter is adjusted at each time step according to the calculated turn rate. The turning rate is estimated from the magnitude of the calculated acceleration divided by the estimated speed of the target. At each scan the previous and current velocity estimates are used to calculate the acceleration. The comparison of the performance of the proposed algorithm is made with that of an interacting multiple model (IMM) algorithm, employing three models with different levels of process noise covariance and also to that of a second order Kalman filter. Two different assumptions have been made for selecting the process noise values for the the IMM and Kalman filter algorithms, in the first case it was assumed that there was no prior information about the target motion whereas in the second case it was assumed that the largest turn rate that the target of interest could perform was known. The IMM algorithm utilizing three models gives slightly better estimates during the nonmaneuvering periods, but the proposed algorithm is superior to the IMM algorithm during maneuvering periods in terms of estimation errors. Also the proposed algorithm requires 78% less computation, almost the same number of calculations required by a single fixed process noise Kalman filter, than the IMM algorithm.

The conventional interacting multiple models (IMM) approach for a hybrid system under the Gaussian assumption is limited for most real applications due to the noisy measurements often being in the presence of outliers. This paper aims at accommodating the IMM approach to the non‐Gaussian cases where outliers exist. In the proposed IMM algorithm, the Student‐t distribution is used to model the non‐Gaussian measurement noise. At the interaction step, the mixed statistics of the noise parameter under a Bayesian framework are obtained via a Gamma approximation and a recently reported moments matching method. To address the state noise‐coupled intractability in Bayesian filtering, a variational Bayesian method is utilized to approximate the posterior distributions of the noise and state recursively. The proposed algorithm is tested with a maneuvering target tracking example and is shown to be robust to the outliers.

For multi-target tracking, the IMM (interactive multiple model) algorithm has been proposed. The IMM is expected to reduce tracking errors for both nonmaneuvering and maneuvering targets. However, the IMM requires a heavy computational burden, because it utilizes multiple Kalman filters in parallel. On the other hand, the Kalman filter with a turning acceleration estimator, which can adapt a maneuvering target, has been proposed. The Kalman filter with a turning acceleration estimator is a single two-stage type filter and has a problem of setting threshold for the maneuver detector. In this paper, we propose the hybrid filter with a constant-velocity (CV) filter and a turning acceleration estimation filter. The proposed filter does not require a maneuver detector, because it integrates the outputs of two filters using the likelihood of each filter. And its computational requirement is smaller than the IMM, since it consists of only two Kalman based filters. The proposed method can prevent deteriorating tracking accuracy by reducing the risk of maneuver misdetection when a target maneuvers. We evaluate the performance of the proposed filter by computer simulation, and show the effectiveness of the proposed filter, comparing with the conventional Kalman filter and the two-stage Kalman filter.

The paper compares the performance of two different configurations of the interacting multiple model (IMM) algorithm in tracking a civil aircraft. The first configuration of the IMM algorithm uses one straight line motion model and different constant turn rate models whereas the other configuration also employs a straight line motion model and a single turn rate model which uses an estimate of the turn rate. In the first algorithm, results have been obtained using different process noise values and different numbers of models. From these results, recommendations are made regarding the choice of both these variable parameters.

Aiming at improving the performance of tracking unmanned aerial vehicle in the battlefield, this paper focuses on the algorithm involving tracking the maneuvering target with a distributed sensor network under non-Gaussian measurement noise. A novel distributed maximum correntropy cubature information filtering based on interactive multiple model is proposed. Taking advantage of the correntropy, we design a maximum correntropy cubature information filtering for each node to estimate the target state under non-Gaussian measurement noise. Then, distributed information fusion based on weighted average consensus is conducted to improve the stability of the sensor network. After that, the information pair is changed, so that a distributed state estimation algorithm based on interactive multiple model is developed to increase the accuracy of maneuvering target tracking. Simulation results and comparison with other algorithms in three typical non-Gaussian measurement noise scenarios are given to evaluate the effectiveness of the proposed algorithm.

The paper considers the problem of tracking multiple maneuvering targets in the presence of clutter using switching multiple target motion models. A novel suboptimal filtering algorithm is developed by applying the basic interacting multiple model (IMM) approach and the joint probabilistic data association (JPDA) technique. Unlike the standard single-scan JPDA approach, the authors exploit a multiscan joint probabilistic data association (mscan-JPDA) approach to solve the data association problem. The algorithm is illustrated via a simulation example involving tracking of four maneuvering targets and a multiscan data window of length two

In many applications, the interacting multiple model (IMM) estimator provides better performance over the single model Kalman filter (KF). However, there are two high frequency surface wave radar (HFSWR) factors which seem to make the IMM estimator less attractive: Firstly, the radar has a very long integration time, up to a few hundred seconds for ship detection; Secondly, the radar has very limited detection capability of maneuvering targets. It is unknown whether the IMM estimator will perform better over an EKF for real time HFSWR. The purpose of this paper is to compare the EKF with the IMM estimator. An optimal noise level and critical value have been found for HFSWR EKF design. The results demonstrated that the IMM estimator does not yield improved performance.

This paper presents mathematical procedures to calculate the Cramer-Rao lower bound (CRLB) for a target track obtained by fusing two tracks given by two interactive multiple model (IMM) trackers filtering the plots received by two radars. The target under tracking is a ballistic target (BT) in reentry phase and it is assumed that the two sensors could have a detection probabilities Pd less than 1. Accurate BT tracking is required for: (i) impact point prediction, (ii) support to the kill assessment, and (iii) threat classification. Note that, to improve the performance, the method can be extended to the fusion of more than two sensor tracks. The paper is organized as follows: the problem is introduced in section 1; section 2 briefly recalls the models of BT dynamic and of the radar measures. Section 3 presents the tracking algorithm based on the IMM approach; section 4 contains the description of the CRLB evaluation problem and section 5 the results achieved in comparing the fusion of two BT tracks (each track is obtained from the IMM algorithm described in section 4) with the CRLB; Monte Carlo simulations is also used to quantify the fused track accuracies. Section 6 is pertinent to conclusions and follows on of the activity while references are reported in section 7.

A systematic approach to interactive multiple model (IMM) filter design is described, using evolution strategies (ESs), a global optimization technique inherited from the artificial intelligence field. The methodology defined is based on the definition of test scenarios and related performance goals, which are then assumed as design constraints. Then, ESs are used to search a set of filter parameters for the chosen IMM structure, trying to fulfill all design constraints, and if not possible, gracefully relaxing those constraints. The method has been successfully applied to the design of an IMM monoradar tracker for an air traffic control (ATC) application, comparing the performance of several IMM structures. The resulting filters are compared with previous designs for similar applications, with ad-hoc parameters, and with other classical optimization methods. The proposed method enables improved filter tuning in a comprehensive and robust way.

Distributed asynchronous multiple sensor fusion with nonlinear multiple models

Interacting multiple model (IMM) filtering and multiple hypothesis tracking (MHT) represent the most accurate methods currently available for tracking multiple maneuvering targets in cluttered environments. Although these methods are complex, modern computational capabilities make their combined implementation feasible for modern tracking systems. This paper discusses alternative approaches for developing a combined IMM/MHT tracking system and describes specific implementations that have been developed for radar and IRST applications. Simulation results for both radar and IRST systems are presented. Track maintenance and accuracy performance for the IMM/MHT system is compared with that obtained from an MHT tracker using a conventional filtering approach. Results indicate that the improvements in data association derived from the use of IMM filtering with MHT may be comparable to the well known IMM improvements in track accuracy. The addition of multiple filter models to MHT data association has the potential to significantly increase computational requirements. Thus, several compromises, described in this paper, have been developed in order to assure computational feasibility. Preliminary estimates of computational requirements are given in order to demonstrate implementation feasibility.© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

To improve the performance of strapdown inertial navigation system (SINS)/ Doppler velocity logs (DVL) integrated navigation system, a new Interacting Multiple Model Feedback Particle Filter (IMMFPF) approach is proposed under the condition of complex and changeable underwater environment. In this scheme, the Interacting Multiple Model uses to deal with the model switch, while FPF approach takes each particle as a controllable stochastic system. By finding the solution about the Euler-Lagrange boundary value, the optimal feedback control law of Kullback-Leibler distance is obtained, and then the state of the particle itself is corrected and updated. Both simulations and field test illustrate that proposed IMMFPF approach can achieve a more accurate solution and make a more correct and effective response for SINS/DVL integrated navigation system.

To increase the probability of destroying a maneuvering target (e.g. ballistic missile), a framework of multi-missiles interception is presented in this paper. Each intercepting missile is equipped with an IR Image seeker which can provide excellent stealth ability during its course of tracking the ballistic missile. Such intelligent ranging system integrates the Interacting Multiple Model (IMM) technique and the concept of reachable set to find the optimal interception results by minimizing the energy of pursuing the maneuvering target. The proposed guidance law of every missile interceptor is designed based on pursuit and evasion game theory while considering the motion of the target in 3-D space such that the distance between the missiles and the target is minimized. Finally, extensive computer simulations have been conducted to validate the performance of the proposed system.

The interacting multiple model (IMM) and probabilistic data association (PDA) filter (IMMPDAF), is developed to support the air surveillance of the Track While Scan (TWS) radar systems. Take advantage of both, IMM and PDA algorithms to increase the tracking efficiency of fast maneuvers targets. The known algorithm was implemented in a radar tracking application for the purpose of modernizing the ‘L’ band radar, as a replacement for the conventional NN (Nearest Neighbor) algorithm which proved to be ineffective in situations with airborne targets in the air. In extensive experiments with complex, target maneuver and non-maneuver scenarios has been shown that proposed algorithm to have robust tracking characteristics with small position and velocity error.