Realistic extended target model for track before detect in maritime surveillance

Errasti-Alcala, Borja; Fuscaldo, Walter; Braca, Paolo; Vivone, Gemine

doi:10.1109/oceans-genova.2015.7271624

Cited by 17 publications

(8 citation statements)

References 8 publications

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“…The radar has an antenna mounted on a rotor with variable rotating speed and the possibility to lock and hold the position towards a specific direction with a 0.1 • accuracy. The range resolution is approximately 1 m, while the angular resolution is 0.172 • [101]. More details about the radar system, the signal processing chain, and the data collection are available in [97], [101]- [103].…”

Section: Discussionmentioning

confidence: 99%

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Statistical Hypothesis Testing Based on Machine Learning: Large Deviations Analysis

Braca¹,

Millefiori²,

Aubry³

et al. 2022

Preprint

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We study the performance-and specifically the rate at which the error probability converges to zero-of Machine Learning (ML) classification techniques. Leveraging the theory of large deviations, we provide the mathematical conditions for a ML classifier to exhibit error probabilities that vanish exponentially, say ∼ exp (−n I + o(n)), where n is the number of informative observations available for testing (or another relevant parameter, such as the size of the target in an image) and I is the error rate. Such conditions depend on the Fenchel-Legendre transform of the cumulant-generating function of the Data-Driven Decision Function (D3F, i.e., what is thresholded before the final binary decision is made) learned in the training phase. As such, the D3F and, consequently, the related error rate I, depend on the given training set, which is assumed of finite size. Interestingly, these conditions can be verified and tested numerically exploiting the available dataset, or a synthetic dataset, generated according to the available information on the underlying statistical model. In other words, the classification error probability convergence to zero and its rate can be computed on a portion of the dataset available for training. Coherently with the large deviations theory, we can also establish the convergence, for n large enough, of the normalized D3F statistic to a Gaussian distribution. This property is exploited to set a desired asymptotic false alarm probability, which empirically turns out to be accurate even for quite realistic values of n. Furthermore, approximate error probability curves ∼ ζn exp (−n I) are provided, thanks to the refined asymptotic derivation (often referred to as exact asymptotics), where ζn represents the most representative sub-exponential terms of the error probabilities. Leveraging the refined asymptotic, we are able to compute an analytically accurate approximation of the classification performance for both the regimes of small and large values of n. Theoretical findings are corroborated by extensive numerical simulations. Real-world data, acquired by an X-band maritime radar system for surveillance, are exploited to assess the validity of the proposed theory.

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Section: Discussionmentioning

confidence: 99%

“…The range resolution is approximately 1 m, while the angular resolution is 0.172 • [101]. More details about the radar system, the signal processing chain, and the data collection are available in [97], [101]- [103].…”

Section: Discussionmentioning

confidence: 99%

Statistical Hypothesis Testing Based on Machine Learning: Large Deviations Analysis

Braca¹,

Millefiori²,

Aubry³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…It assumes that M b (r, a) also follows the Rayleigh distribution in (3). Meanwhile, the effects of shadowing in [27] are also considered. Once the wavelength of an electromagnetic wave is much smaller than the dimension of the objects involved in the propagation, the scattering phenomena are mainly governed by the laws of geometrical optics [28].…”

Section: Measurement Modelmentioning

confidence: 99%

Track‐before‐detect algorithm based on dynamic programming for multi‐extended‐targets detection

Yan

et al. 2017

IET signal process.

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In recent years, multi-extended-targets detection in sea clutter has gained a special interest. Dynamic programming based track-before-detect (DP-TBD) algorithm is used to detect extended targets in video data of high resolution radars. Two innovations are presented in this work. First one is a novel partition method to cluster targets into well separate groups for the problem of high-dimensional maximisation. Second one is a novel merit function specifically designed for extended targets for the problem of target extended. Both the principle of this novel DP-TBD method specifically for extended targets and its detail implementation are presented. Then, both the real data and simulated data are performed. The comparison of the results shows that compared with particle filter based track before detect algorithm, the proposed method obtains better performance in detection rate, position error and false alarm rate. Meanwhile, far less calculation is spent with the novel partition method. Therefore, it can safely conclude that the proposed method is very practical under various sea conditions in the real world.

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“…In this paper, we address a multiple M-EOTs Based on track-before-detect (TBD) approach, which is an efficient way to track low observable M-EOTs from ISAR images. Among the popular EOT approaches [1], [4][5][6][7][8][9][10], the random matrix model (RMM) approach appears promising since it is capable to jointly estimate the kinematic state and extension state without data association.…”

Section: Introductionmentioning

confidence: 99%

Skewness Multi-Bernoulli-TBD filter for tracking multiple maneuvering extended objects from ISAR Images

Barbary¹,

Hamed²,

Kernec³

2022

International Conference on Radar Systems (RADAR 2022)

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Realistic extended target model for track before detect in maritime surveillance

Cited by 17 publications

References 8 publications

Statistical Hypothesis Testing Based on Machine Learning: Large Deviations Analysis

Statistical Hypothesis Testing Based on Machine Learning: Large Deviations Analysis

Track‐before‐detect algorithm based on dynamic programming for multi‐extended‐targets detection

Skewness Multi-Bernoulli-TBD filter for tracking multiple maneuvering extended objects from ISAR Images

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