Generation and estimation of randomly maneuvering target trajectories towards metacognitive tracking radar applications

Sreekantamurthy, Veena; DeVault, Jordan A.; Martone, Anthony F.; Kirk, Benjamin H.; Narayanan, Ram M.

doi:10.1117/12.2622407

Cited by 2 publications

(4 citation statements)

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“…Moreover, in real-time tracking situations, where the physics of the trajectory path and the sequence of maneuvers are not so clearly defined, additional variables to represent the random motion are appropriate to improve target tracking accuracy. In such situations, approaches involving cognitive and metacognitive approaches will be needed [7], [13]- [16].…”

Section: Discussionmentioning

confidence: 99%

“…While the IMM algorithm provides better state estimates over the basic Kalman filter, only marginal improvements occur during large maneuvers and results are dependent on the weighting parameters used to tune the estimator [9]. The IMM also uses Gaussian noise assumptions and requires more computation time as state estimates are calculated from multiple filter models at each time step [6], [7].…”

Section: Introductionmentioning

confidence: 99%

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Combined Kalman and Kalman-Levy filter for maneuvering target tracking

Sreekantamurthy,

Narayanan,

Martone

2024

Radar Sensor Technology XXVIII

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Common target tracking algorithms, such as the Kalman Filter, assume Gaussian estimates of process and measurement noises. This Gaussian assumption does not fully support practical maneuvering target tracking. Rather, when target motion is highly dynamic, sudden maneuvers are better described by non-Gaussian noise distributions. A Kalman-Levy filter has been proposed as an improvement to the maneuvering target tracking problem. This filter models process and measurement noises using Levy distributions. While an improvement in maneuver estimation is demonstrated with the Kalman-Levy filter, it requires significant computation time and occasionally provides poor estimates of simple, linear maneuvers that the Kalman filter can otherwise provide. This paper seeks to improve maneuvering target tracking without sacrificing computation time by proposing the use of a moving-average filter in the tracking process. A Moving-Average filter is used to track the position root-mean-square error (RMSE) and switch from the Kalman filter to the Kalman-Levy filter when this error becomes large. The Kalman filter, the Kalman-Levy filter, and the switching algorithm based on the Moving-Average filter are demonstrated on two tracking problems. Simulation results show that switching between the filters improves maneuvering target state estimation accuracy while being computationally efficient.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Combined Kalman and Kalman-Levy filter for maneuvering target tracking

Sreekantamurthy,

Narayanan,

Martone

2024

Radar Sensor Technology XXVIII

Self Cite

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“…For the purposes of this tracking scenario, the motion model was assumed to have Constant Velocity (CV). A 2-dimensional state space representation of a target undergoing CV motion in terms of position and velocity is expressed in Cartesian coordinates as 2 :…”

Section: Tracking Algorithm Outlinementioning

confidence: 99%

“…Sophisticated modern radar systems detect not only targets and their range, but also track and classify targets while suppressing strong interferences, discriminating clutter, and avoiding jamming 1 . Tracking is a basic radar function where the positions of one or multiple targets of interest in an environment are followed 1,2 . Tracking radars are expanding in their capabilities by the use of advanced software and hardware to cover a wide range of applications such as military and civilian tracking, satellite tracking and space navigation, tracking in dense terrain, air traffic collision avoidance, and surveillance and monitoring 1 .…”

Section: Introductionmentioning

confidence: 99%

A proposed concept for metacognitive configuration switching for tracking radar systems

Sreekantamurthy

Ridder

Martone

et al. 2023

Radar Sensor Technology XXVII

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Target tracking has become more complicated as radar operating environments have become increasingly congested with clutter, interference, and countermeasures for jamming of radio frequency (RF) signals. As a result, the accuracy and performance of target tracking radars are further degraded. Three configurations of radar systems, namely, monostatic radar, bistatic radar, and passive radar, are commonly used today. The most conventional one is the monostatic radar defined by a co-located transmitter and receiver. In the bistatic radar system, the radar transmitter and receiver are physically separated by a large distance. A passive radar system is a derivative of the bistatic radar system, wherein radar functions are performed without the use of one's own transmitter. Instead, existing signals-of-opportunity (SOP) within the RF environment are opportunistically exploited to perform the radar functions. This paper presents a concept based on metacognition which entails dynamically switching the mode of operation between the three radar configurations to optimize target tracking accuracy. The paper provides an overview of the three radar configurations followed by the description of an approach for switching radar configuration among the three radars. Modeling and simulation of a passive radar system for target tracking in MATLAB is presented and followed by analysis and discussion of its performance.

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Generation and estimation of randomly maneuvering target trajectories towards metacognitive tracking radar applications

Cited by 2 publications

References 0 publications

Combined Kalman and Kalman-Levy filter for maneuvering target tracking

Combined Kalman and Kalman-Levy filter for maneuvering target tracking

A proposed concept for metacognitive configuration switching for tracking radar systems

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