Specific Features of IMM Tracking Filter Design

Simeonova, Iliyana; Semerdjiev, Tzvetan

doi:10.11610/isij.0909

Cited by 12 publications

(6 citation statements)

References 4 publications

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“…The most common form of target motion models are second-order kinematic model (constant velocity, CV, model), third-order kinematic model (constant acceleration, CA, model), and non-constant speed coordinated turn model. 3,12 CV model. The second-order kinematic model, with position and velocity components in each of the three Cartesian coordinates x, y, and z, has the following state vector…”

Section: Kalman Filter Modelmentioning

confidence: 99%

“…5 Interactive multiple model (IMM) estimator is a hybrid filter that has been shown to be one of the most cost-effective hybrid state estimation schemes. 12,13 It has the ability to estimate the states of abruptly changing systems. To account for the problem in the MMAE and fast MMAE method, we include IMM in the integrated estimation and guidance strategy discussed next.…”

Section: Introductionmentioning

confidence: 99%

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An integrated estimation/guidance approach for seeker-less interceptors

Kumar

Ghose

Vengadarajan

2014

Proceedings of the Institution of Mechanical Engineers, Part G:

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This paper addresses the problem of intercepting highly maneuverable threats using seeker-less interceptors that operate in the command guidance mode. These systems are more prone to estimation errors than standard seeker-based systems. In this paper, an integrated estimation/guidance (IEG) algorithm, which combines interactive multiple model (IMM) estimator with differential game guidance law (DGL), is proposed for seeker-less interception. In this interception scenario, the target performs an evasive bang-bang maneuver, while the sensor has noisy measurements and the interceptor is subject to acceleration bound. The IMM serves as a basis for the synthesis of efficient filters for tracking maneuvering targets and reducing estimation errors. The proposed game-based guidance law for two-dimensional interception, later extended to three-dimensional interception scenarios, is used to improve the endgame performance of the command-guided seeker-less interceptor. The IMM scheme and an optimal selection of filters, to cater to various maneuvers that are expected during the endgame, are also described. Furthermore, a chatter removal algorithm is introduced, thus modifying the differential game guidance law (modified DGL). A comparison between modified DGL guidance law and conventional proportional navigation guidance law demonstrates significant improvement in miss distance in a pursuer-evader scenario. Simulation results are also presented for varying flight path angle errors. A numerical study is provided which demonstrates the performance of the combined interactive multiple model with game-based guidance law (IMM/DGL). Simulation study is also carried out for combined IMM and modified DGL (IMM/modified DGL) which exhibits the superior performance and viability of the algorithm reducing the chattering phenomenon. The results are illustrated by an extensive Monte Carlo simulation study in the presence of estimation errors.

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Section: Kalman Filter Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An integrated estimation/guidance approach for seeker-less interceptors

Kumar

Ghose

Vengadarajan

2014

Proceedings of the Institution of Mechanical Engineers, Part G:

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“…The user platform mainly based on receiver which receives the radio signals from four or more satellites provides the position and velocity information. In spite of diverse application of GPS, the flexibility of service is still limited as the GPS based tracking accuracy is decreases when it passes through poor satellite visible areas such as tunnel, mountain, forest, slope, bridge, urban or canopy areas due to signal failure and attenuation [4,6]. Different locations have affected by different atmosphere factors which vary with locations and these errors are corrected by Differential GPS.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Tracking of locomotive Using IMM-Based Robust Hybrid Control Algorithm

Patgar¹,

Shankaraiah²

2017

IJSSN

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Locomotive surveillance is the most active research topic and still faces big technical challenges in railway safety control system. An end-to-end locomotive tracking and continuous monitoring system is necessary for safety measures in satellite visible and low satellite visible environment. These smart systems aim to updates the information on location, exact detection, speed limitation and also rail track information. This paper contributes to develop an intelligent tracking and monitoring system based on Internet of Things (IoT) platform using Differential Global Positioning System (DGPS) for improved tracking accuracy of locomotive in both environments. Interacting Multiple Model (IMM) tracking algorithm based on Di-filter model is proposed for analysis that make it easy to pinpoint the location and its status of the locomotive.

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“…In particular, tracking a manoeuvering target using multiple models can provide better performance than using a single model. A number of multiple-model techniques to track a manoeuvering target have been proposed in the literature: the multiple-model algorithm [9], the interacting multiple-model (IMM) algorithm [6,8,12,13], the adaptive IMM [14][15][16], the fuzzy IMM [17,18], and others.…”

Section: Introductionmentioning

confidence: 99%

An IMM algorithm with federated information mode‐matched filters for AGV

Kim¹,

Hong²

2006

Adaptive Control & Signal

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In this paper, a tracking algorithm for autonomous navigation of automated guided vehicles (AGVs) is presented. The developed navigation algorithm is an interacting multiple-model (IMM) algorithm used to detect other AGVs using fused information from multiple sensors. In order to detect other AGVs, two kinematic models were derived: A constant-velocity model for linear motion, and a constant-speed turn model for curvilinear motion. In the constant-speed turn model, a nonlinear information filter (IF) is used in place of the extended Kalman filter (KF). Being equivalent to the KF algebraically, the IF is extended to N-sensor distributed dynamic systems. The model-matched filter used in multi-sensor environments takes the form of a federated nonlinear IF. In multi-sensor environments, the information-based filter is easier to decentralize, initialize, and fuse than a KF-based filter. In this paper, the structural features and information-sharing principle of the federated IF are discussed. The performance of the suggested algorithm using a Monte Carlo simulation is evaluated under the three navigation patterns.

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Specific Features of IMM Tracking Filter Design

Cited by 12 publications

References 4 publications

An integrated estimation/guidance approach for seeker-less interceptors

An integrated estimation/guidance approach for seeker-less interceptors

Trajectory Tracking of locomotive Using IMM-Based Robust Hybrid Control Algorithm

An IMM algorithm with federated information mode‐matched filters for AGV

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