Tracking algorithm with radar and infrared sensors using a novel adaptive grid interacting multiple model

Target detection and tracking is important in military as well as in civilian applications. In order to detect and track high-speed incoming threats, modern surveillance systems are equipped with multiple sensors to overcome the limitations of single-sensor based tracking systems. This research proposes the use of information from RADAR and Infrared sensors (IR) for tracking and estimating target state dynamics. A new technique is developed for information fusion of the two sensors in a way that enhances performance of the data association algorithm. The measurement acquisition and processing time of these sensors is not the same; consequently the fusion center measurements arrive out of sequence. To ensure the practicality of system, proposed algorithm compensates the Out of Sequence Measurements (OOSMs) in cluttered environment. This is achieved by a novel algorithm which incorporates a retrodiction based approach to compensate the effects of OOSMs in a modified Bayesian technique. The proposed modification includes a new gating strategy to fuse and select measurements from two sensors which originate from the same target. The state estimation performance is evaluated in terms of Root Mean Squared Error (RMSE) for both position and velocity, whereas, track retention statistics are evaluated to gauge the performance of the proposed tracking algorithm. The results clearly show that the proposed technique improves track retention and and false track discrimination (FTD).

Section: Introductionmentioning

confidence: 99%

“…An IR sensor is passive in nature, hence it is immune to electromagnetic interference [2][3][4]. However, it is quite sensitive to atmospheric conditions, which, limits its range but the angular measurements are more precise as compared to RADAR.…”

Section: Introductionmentioning

confidence: 99%

A Modified Bayesian Framework for Multi-Sensor Target Tracking with Out-of-Sequence-Measurements

Shi

Qayyum

Memon

et al. 2020

“…Multisensor data fusion is a promising approach to combine the data of multiple sensors having similar or complementary characteristics [ 2 ]. Considerable research and development have been carried out towards algorithm development for target dynamics estimation based on information fusion from multiple sensor platforms [ 2 , 3 , 4 , 5 ]. Data fusion not only proves useful in terms of estimation accuracy but also reduces susceptibility to Electronic Counter Measures (ECM) by utilizing the benefits of different measurement principles.…”

Section: Introductionmentioning

confidence: 99%

Multisensor-Based Target-Tracking Algorithm with Out-of-Sequence-Measurements in Cluttered Environments

Ullah

Qureshi

Khan

et al. 2018

A localization and tracking algorithm for an early-warning tracking system based on the information fusion of Infrared (IR) sensor and Laser Detection and Ranging (LADAR) is proposed. The proposed Kalman filter scheme incorporates Out-of-Sequence Measurements (OOSMs) to address long-range, high-speed incoming targets to be tracked by networked Remote Observation Sites (ROS) in cluttered environments. The Rauch–Tung–Striebel (RTS) fixed lag smoothing algorithm is employed in the proposed technique to further improve tracking accuracy, which, in turn, is used for target profiling and efficient filter initialization at the targeted platform. This efficient initialization increases the probability of target engagement by increasing the distance at which it can be effectively engaged. The increased target engagement range also reduces risk of any damage from debris of the engaged target. Performance of the proposed target localization algorithm with OOSM and RTS smoothing is evaluated in terms of root mean square error (RMSE) for both position and velocity, which accurately depicts the improved performance of the proposed algorithm in comparison with existing retrodiction-based OOSM filtering algorithms. The effects of assisted target state initialization at the targeted platform are also evaluated in terms of Time to Impact (TTI) and true track retention, which also depict the advantage of the proposed strategy.

“…A distributed flow of information fusion for radar/IR compound seeker was established in [6], and the federated filter was used to track the target. An adaptive grid IMM based on modified iterated extended Kalman filter for tracking a maneuvering target using radar/ IR sensors was proposed in [7]. …”

Section: Introductionmentioning

confidence: 99%

Heterogeneous Multiple Sensors Joint Tracking of Maneuvering Target in Clutter

Kong

et al. 2015

Self Cite

To solve the problem of tracking maneuvering airborne targets in the presence of clutter, an improved interacting multiple model probability data association algorithm (IMMPDA-MDCM) using radar/IR sensors fusion is proposed. Under the architecture of the proposed algorithm, the radar/IR centralized fusion tracking scheme of IMMPDA-MDCM is designed to guarantee the observability of the target state. The interacting multiple model (IMM) deals with the model switching. The modified debiased converted measurement (MDCM) filter accounts for non-linearity in the dynamic system models, and reduces the effect of measurement noise on the covariance effectively. The probability data association (PDA) handles data association and measurement uncertainties in clutter. The simulation results show that the proposed algorithm can improve the tracking precision for maneuvering target in clutters, and has higher tracking precision than the traditional IMMPDA based on EKF and IMMPDA based on DCM algorithm.