Adaptive Phased-Array Tracking in ECM using Negative Information

Blanding, Wayne; Koch, Wolfgang; Nickel, Ulrich

doi:10.1109/taes.2009.4805270

Cited by 24 publications

(18 citation statements)

References 13 publications

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“…Therefore, the fusion process is simplified by considering the bearing and range dimensions independently, and fusing the resulting likelihoods sequentially as measurements using an existing Gaussian mixture filtering method [18]- [21]. Note that fusing a GMM likelihood, such as (16), results in a mixture model posterior, where the number of mixands grows exponentially with each negative measurement update. Therefore, the negative measurement posterior must be compressed to a manageable number of mixands using existing methods, such as those presented in [18]- [21].…”

Section: A Negative Measurementsmentioning

confidence: 99%

“…The task at hand is then to model a Gaussian CDF, (20), with an unnormalized Gaussian mixture, (16), for which a closed-form solution is available. Specifically, the range likelihood is then written as…”

Section: A Negative Measurementsmentioning

confidence: 99%

“…The concept of negative information has been successfully leveraged within the estimation framework [2], [7]- [16]. Human observation has been fused as sensor data for static target search applications in [8] and [9].…”

Section: Introductionmentioning

confidence: 99%

“…Negative information has also been used to address known sensor limitations, such as finite resolution, Doppler blindness, and jamming [7], [15], [16]. Refined sensor models are developed for a variety of applications by inferring information from unique properties of each sensor.…”

Section: Introductionmentioning

confidence: 99%

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Negative Information for Occlusion Reasoning in Dynamic Extended Multiobject Tracking

Wyffels

Campbell

2015

IEEE Trans. Robot.

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A novel approach to utilize negative information to improve the precision and accuracy of extended multiobject tracking is presented. The parameterized probability density of object tracks undetected in sensor data is updated via inferences about the conditions necessary to result in occlusion of the undetected object. Negative information is also leveraged to inform track existence and data association, both of which contribute to a more sensible belief of the local dynamic scene. Simulation and experimental results are presented from autonomous driving scenarios, demonstrating that the use of negative information leads to a more complete, accurate, precise, and intuitive belief of the local scene, enabling high-level tasks that would otherwise be impractical.

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Section: A Negative Measurementsmentioning

confidence: 99%

Section: A Negative Measurementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Negative Information for Occlusion Reasoning in Dynamic Extended Multiobject Tracking

Wyffels

Campbell

2015

IEEE Trans. Robot.

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“…The monopulse angle estimation in presence of main lobe interference is discussed in [6], and a four-channel monopulse framework is proposed. The monopulse angle tracking problem in electronic countermeasures or jamming is investigated in [7]. The performance of monopulse angle tracking in noise or noise jamming is analyzed in [8].…”

Section: Introductionmentioning

confidence: 99%

Robust adaptive monopulse algorithm based on main lobe constraints and subspace tracking

Qiu

Sheng

et al. 2017

EURASIP J. Adv. Signal Process.

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In continuous wave (CW) radar and high pulse repetition frequency pulse-Doppler (HPRF-PD) radar, the interference plus noise sample snapshots are hard to be obtained. The desired signal in the received snapshots makes the LCMV-based adaptive monopulse algorithm sensitive to pattern look direction error. A linearly constrained subarray robust adaptive monopulse algorithm based on main lobe maintenance constraint and subspace tracking is developed in this paper. The constraint of main lobe maintenance is obtained by signal subspace projection. The bi-iterative least-square (Bi-LS) subspace tracking is used to update the signal subspace, and a power-associated method is developed to determine the dimension of the projection subspace automatically. The proposed robust adaptive monopulse algorithm can achieve high-angle estimation accuracy and good robustness to look direction error while expending only one additional degree of freedom compared to conventional LCMV-based method.Keywords: Robust adaptive monopulse algorithm, Main lobe maintenance, Subspace tracking, Dimension estimation BackgroundThe monopulse technique is utilized to perform high precision angle estimation for tracking radars. The target's angle is estimated using the ratio of difference to sum beam outputs, called monopulse ratio. Because of the linearity of monopulse ratio in 3dB beamwidth, the target's angle can be estimated bywhere θ 0 and θ s are the angle of the pattern look direction and the target direction, respectively, g (·) is the monopulse ratio, and K θ is the slope of the monopulse ratio.The adaptive array processing is first applied to monopulse technique by Davis [1]. The adaptive monopulse uses adaptive beamforming to form the sum and difference beams and suppress the spatial interferences. For the large-scale adaptive array, computation load and high data rate are two main bottlenecks for realization of the adaptive monopulse method. Subarrray *Correspondence: maxiaofeng@njust.edu.cn School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, China adaptive monopulse method can effectively alleviate such pressures and ensure angle estimation performance when limited number of interferences exist. An example of the subarray geometry is illustrated in Fig. 1a, in which regular and irregular subarray geometries are comprised. Nickel extends the adaptive monopulse technique to subarray and arbitrary array geometries and provides modified formulas of monopulse ratio. Then he systematically analyzes the performance of the adaptive monopulse technique and extends it to space-time processing [2][3][4][5].

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