Detection and tracking of multiple targets using dual-frequency interferometric radar

Ishtiaq, Saima; Wang, X.; Hassan, Shahid

doi:10.1049/icp.2021.0646

Cited by 3 publications

(2 citation statements)

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“…Based on the carrier frequency

, the lengths of the two orthogonal baselines were set as

m. The detection and extraction of radial velocity measurements were performed at a higher carrier frequency

, whereas the azimuth and elevation angular velocity measurements were obtained at a lower carrier frequency

, thereby suppressing the intermodulation terms in the interferometric response. The interferometric frequencies’ information was preserved by increasing the baseline lengths

and

simultaneously [ 28 ]. First of all, the radar observes the region of interest (ROI) operating at carrier frequency

and performs 2D-FFT on the received data for the detection of the potential targets by obtaining the range–radial velocity map.…”

Section: Design Of the Proposed 3d Mtt Algorithmmentioning

confidence: 99%

“…Furthermore, the MTT algorithm using a phased array antenna requires the number of receiving elements to be greater than the number of targets in the sensor’s FoV. To resolve this issue, in our previous work [ 28 , 29 ], we presented an algorithm for multiple targets’ detection and tracking in the 2D Cartesian space by measuring their range and 2D velocity (radial velocity and angular velocity) measurements using a dual-frequency frequency-modulated continuous wave (DF-FMCW) interferometric radar. Now, we propose to extend this concept to MTT in the 3D Cartesian space based on 3D velocity (radial velocity, azimuth angular velocity, and elevation angular velocity) measurements using a dual-orthogonal baseline DF-FMCW interferometric radar.…”

Section: Introductionmentioning

confidence: 99%

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Three-Dimensional Multi-Target Tracking Using Dual-Orthogonal Baseline Interferometric Radar

Ishtiaq

Wang

Hassan

et al. 2022

Sensors

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Multi-target tracking (MTT) generally needs either a Doppler radar network with spatially separated receivers or a single radar equipped with costly phased array antennas. However, Doppler radar networks have high computational complexity, attributed to the multiple receivers in the network. Moreover, array signal processing techniques for phased array radar also increase the computational burden on the processing unit. To resolve this issue, this paper investigates the problem of the detection and tracking of multiple targets in a three-dimensional (3D) Cartesian space based on range and 3D velocity measurements extracted from dual-orthogonal baseline interferometric radar. The contribution of this paper is twofold. First, a nonlinear 3D velocity measurement function, defining the relationship between the state of the target and 3D velocity measurements, is derived. Based on this measurement function, the design of the proposed algorithm includes the global nearest neighbor (GNN) technique for data association, an interacting multiple model estimator with a square-root cubature Kalman filter (IMM-SCKF) for state estimation, and a rule-based M/N logic for track management. Second, Monte Carlo simulation results for different multi-target scenarios are presented to demonstrate the performance of the algorithm in terms of track accuracy, computational complexity, and IMM mean model probabilities.

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“…Based on the carrier frequency

, the lengths of the two orthogonal baselines were set as

m. The detection and extraction of radial velocity measurements were performed at a higher carrier frequency

, whereas the azimuth and elevation angular velocity measurements were obtained at a lower carrier frequency

, thereby suppressing the intermodulation terms in the interferometric response. The interferometric frequencies’ information was preserved by increasing the baseline lengths

and

simultaneously [ 28 ]. First of all, the radar observes the region of interest (ROI) operating at carrier frequency

and performs 2D-FFT on the received data for the detection of the potential targets by obtaining the range–radial velocity map.…”

Section: Design Of the Proposed 3d Mtt Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Multi-Target Tracking Using Dual-Orthogonal Baseline Interferometric Radar

Ishtiaq

Wang

Hassan

et al. 2022

Sensors

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Multi-Target Tracking Algorithm Based on 2-D Velocity Measurements Using Dual-Frequency Interferometric Radar

Ishtiaq¹,

Wang²,

Hassan³

2021

Electronics

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Multi-target tracking (MTT) generally requires either a network of Doppler radar receivers distributed at different locations or a phased array radar. The targets moving with small/no radial velocity or angular velocity only cannot be detected and localized completely by deploying Doppler radar without antenna arrays or multiple receivers. To resolve this issue, we present a new MTT algorithm based on 2-D velocity measurements, namely, radial and angular velocities, using dual-frequency interferometric radar. The contributions of the proposed research are twofold: First, we introduce the mathematical model and implementation of the proposed algorithm by explicitly establishing the relationship between 2-D velocity measurements and kinematic state of the target in terms of Cartesian coordinates. Based on 2-D velocity measurement function, the proposed MTT algorithm comprises the following steps: (i) data association using global nearest neighbor (GNN) method (ii) target state estimation using interacting multiple model (IMM) estimator combined with square-root cubature Kalman filter (SCKF) (iii) track management using rule-based M/N logic. Second, performance of the proposed algorithm is evaluated in terms of tracking accuracy, computational complexity and IMM mean model probabilities. Simulation results for different scenarios with multiple targets moving in different tracks have been presented to verify the effectiveness of the proposed algorithm.

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Human Activity Classification Based on Dual Micro-Motion Signatures Using Interferometric Radar

et al. 2023

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Micro-Doppler signatures obtained from the Doppler radar are generally used for human activity classification. However, if the angle between the direction of motion and radar antenna broadside is greater than 60°, the micro-Doppler signatures generated by the radial motion of human body reduce significantly, thereby degrading the performance of the classification algorithm. For the accurate classification of different human activities irrespective of trajectory, we propose a new algorithm based on dual micro-motion signatures, namely, the micro-Doppler and interferometric micro-motion signatures, using an interferometric radar. First, the motion of different parts of the human body is simulated using motion capture (MOCAP) data, which is further utilized for radar echo signal generation. Second, time-varying Doppler and interferometric spectrograms obtained from time-frequency analysis of a single Doppler receiver and interferometric output data, respectively, are fed as input to the deep convolutional neural network (DCNN) for feature extraction and the training/testing process. The performance of the proposed algorithm is analyzed and compared with a micro-Doppler signatures-based classifier. Results show that a dual micro-motion-based DCNN classifier using an interferometric radar is capable of classifying different human activities with an accuracy level of 98%, where Doppler signatures diminish considerably, providing insufficient information for classification. Verification of the proposed classification algorithm based on dual micro-motion signatures is also performed using a real radar test dataset of different human walking patterns, and a classification accuracy level of approximately 90% is achieved.

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Detection and tracking of multiple targets using dual-frequency interferometric radar

Cited by 3 publications

References 0 publications

Three-Dimensional Multi-Target Tracking Using Dual-Orthogonal Baseline Interferometric Radar

Three-Dimensional Multi-Target Tracking Using Dual-Orthogonal Baseline Interferometric Radar

Multi-Target Tracking Algorithm Based on 2-D Velocity Measurements Using Dual-Frequency Interferometric Radar

Human Activity Classification Based on Dual Micro-Motion Signatures Using Interferometric Radar

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