The Effect of K-Distributed Clutter on Trackability

Schoenecker, Steven; Willett, Peter; Bar‐Shalom, Yaakov

doi:10.1109/tsp.2015.2478745

Cited by 7 publications

(6 citation statements)

References 33 publications

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“…When the false alarm probability is constant, the curve of relationship between the threshold factor T and shape parameter v is also obtainable, as shown in Fig. 4. As for the shape parameter v of clutter, it can be estimated using radar parameters and environmental information, so as to determine the corresponding adaptive threshold factor T .…”

Section: Relationship Between False Alarm Probability and Threshold F...mentioning

confidence: 99%

“…In order to address this problem, it is necessary to identify the characteristics of sea clutter distribution in real time. To obtain the parameters of clutter distribution model in real time, there are a series of clutter model estimation methods proposed by scholars, such as maximum likelihood estimation method [3] and moment estimation method [4] . However, they all require the parameters of the clutter distribution model to be estimated using the measurement data.…”

Section: Introductionmentioning

confidence: 99%

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CFAR Detector Based on the Identification of Sea Clutter Distribution Characteristics

Liu

et al. 2022

J. Phys.: Conf. Ser.

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To improve the capability of radar detection in a time-varying maritime environment, an algorithm was proposed and analyzed in this paper that can be applied to determine the shape and scale parameters of the K-distributed model using the radar parameters and environmental information under the context of K-distributed sea clutter, and improved the original algorithm to improve the accuracy of the parameter estimation algorithm. Furthermore, a CFAR detector capable of identifying the characteristics of sea clutter distribution was developed. It can estimate the parameters of K-distributed model for sea clutter in real time using both radar parameters and environmental perception information, thus improving the accuracy of clutter background estimation. When the false alarm rate reaches a certain level, the adaptive threshold factor can be obtained for the CFAR detector proposed in this paper, thus improving the effectiveness of control on the CFAR detector. As suggested by the simulation results, the proposed method outperforms the CFAR detector with fixed parameters in the capability of detection and robustness under the context of K-distributed clutter.

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Section: Relationship Between False Alarm Probability and Threshold F...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

CFAR Detector Based on the Identification of Sea Clutter Distribution Characteristics

Liu

et al. 2022

J. Phys.: Conf. Ser.

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“…The PMHT approach can be extended to combine features such as the intensity distribution of observations [5,30] or the spatial information obtained by arrays of acoustic receivers [31]. This method is also flexible enough to handle fluctuations of the clutter and target distribution within the TD matrix [32] and Reference [33] even offered an indicative metric to determine the conditions in which tracking is feasible. Yet, while TBD approaches often achieve good results, their main disadvantages are the sensitivity to different target's dynamic types (especially those unknown a-priori) and the high algorithmic complexity, which prevents their application in realistic scenarios (where the TD matrix might contain hundreds of thousands of elements).…”

Section: Related Workmentioning

confidence: 99%

Combining Denoising Autoencoders and Dynamic Programming for Acoustic Detection and Tracking of Underwater Moving Targets

Testolin

Diamant

2020

Sensors

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Accurate detection and tracking of moving targets in underwater environments pose significant challenges, because noise in acoustic measurements (e.g., SONAR) makes the signal highly stochastic. In continuous marine monitoring a further challenge is related to the computational complexity of the signal processing pipeline—due to energy constraints, in off-shore monitoring platforms algorithms should operate in real time with limited power consumption. In this paper, we present an innovative method that allows to accurately detect and track underwater moving targets from the reflections of an active acoustic emitter. Our system is based on a computationally- and energy-efficient pre-processing stage carried out using a deep convolutional denoising autoencoder (CDA), whose output is then fed to a probabilistic tracking method based on the Viterbi algorithm. The CDA is trained on a large database of more than 20,000 reflection patterns collected during 50 designated sea experiments. System performance is then evaluated on a controlled dataset, for which ground truth information is known, as well as on recordings collected during different sea experiments. Results show that, compared to the benchmark, our method achieves a favorable trade-off between detection and false alarm rate, as well as improved tracking accuracy.

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“…However, H-PMHT fails to track fast-moving targets. An analytic trackability framework which aim to determine the clutter/interfering conditions in which tracking is feasible was developed by Schoenecker et al in [34], [35], [36] for various types of clutter. Extension to the case of two close-by targets is given in [37].…”

Section: Related Workmentioning

confidence: 99%

An Active Acoustic Track-Before-Detect Approach for Finding Underwater Mobile Targets

Diamant

Kipnis

Bigal

et al. 2019

IEEE J. Sel. Top. Signal Process.

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We consider the challenge of tracking and estimating the size of a single submerged target in a high reverberant underwater environment using a single active acoustic transceiver. This problem is common for a multitude of applications, ranging from the security and safety needs of tracking submerged vehicles and scuba divers, to environmental research and management implications such as the monitoring of pelagic fauna. Considering that the target can be either slow (e.g., a scuba diver) or fastmoving (e.g., a shark), we avoid continuous signalling, and rely on the emission of wideband pulses whose reflection pattern are evaluated and reshaped in a time-distance matrix. As opposed to common approaches that track targets through template matching or by using tracking filters, we avoid making difficult assumptions about the target's reflection patterns or motion type, and instead perform probabilistic tracking using a constraint Viterbi algorithm, whereby detection is determined based on maximum likelihood criterion. In this process, we use the expectation-maximization (EM) approach to manage stationary reflections through distribution analysis, which otherwise may be misidentified as targets. Based on the tracked path, we then evaluate the target's size. To test our approach, we performed extensive simulations as well as eight sea experiments in different environmental settings to track both a scuba diver and a sandbar shark (Carcharhinus plumbeus). The simulation results show a tracking performance that is close to the Cramér-Rao lower bound, and the experiment results show a good trade-off between detection rate and false alarm rate for a low signal-to-clutter ratio of 5 [dB], and average tracking error of 1.5 [m] and 6.5 [m] in the detections of a scuba diver and sandbar shark, respectively. For reproducibility, we share our sea experiment data.

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The Effect of K-Distributed Clutter on Trackability

Cited by 7 publications

References 33 publications

CFAR Detector Based on the Identification of Sea Clutter Distribution Characteristics

CFAR Detector Based on the Identification of Sea Clutter Distribution Characteristics

Combining Denoising Autoencoders and Dynamic Programming for Acoustic Detection and Tracking of Underwater Moving Targets

An Active Acoustic Track-Before-Detect Approach for Finding Underwater Mobile Targets

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