CFAR Feature Plane: A Novel Framework for the Analysis and Design of Radar Detectors

Coluccia, Angelo; Fascista, Alessio; Ricci, Giuseppe

doi:10.1109/tsp.2020.3000952

Cited by 33 publications

(37 citation statements)

References 22 publications

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“…Note that any function of (ρ, η) yields a CFAR detector. In the sequel similarly to [18], [28] we choose to make use of the pair of random variables (β,t) (also a MIS) defined as…”

Section: A Background On the Case Of No Mismatchmentioning

confidence: 99%

“…When the SoI signature is known and the training samples have the same covariance matrix as the noise in the test vector, Bose and Steinhardt [10], [11] showed that the maximal invariant statistic (MIS) for this detection problem is bi-dimensional and thus any detector which is a function of this 2-D maximal invariant statistic enjoys the constant false alarm rate (CFAR) property. The recent paper [28] provides a very clear review of CFAR detectors, introduces the so-called CFAR feature plane constructed from the MIS and investigates the use of linear or non-linear detectors in this plane.…”

Section: Introductionmentioning

confidence: 99%

“…SoI signature v, see e.g. [7], [9], [12], [28]. Under this situation a major concern is to be able to maintain a constant false alarm rate while monitoring the trade-off between detection of slightly mismatched signals and rejection of unwanted signals.…”

Section: Introductionmentioning

confidence: 99%

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Impact of Covariance Mismatched Training Samples on Constant False Alarm Rate Detectors

Besson

2021

IEEE Trans. Signal Process.

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The framework of this paper is that of adaptive detection in Gaussian noise with unknown covariance matrix when the training samples do not share the same covariance matrix as the vector under test. We consider a class of constant false alarm rate detectors which depend on two statistics (β,t) whose distribution is parameter-free in the case of no mismatch and we analyze the impact of covariance mismatched training samples. More precisely, we provide a statistical representation of these two variables for an arbitrary mismatch. We show that covariance mismatch induces significant variations of the probability of false alarm and we investigate a way to mitigate this effect.

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“…Note that any function of (ρ, η) yields a CFAR detector. In the sequel similarly to [18], [28] we choose to make use of the pair of random variables (β,t) (also a MIS) defined as…”

Section: A Background On the Case Of No Mismatchmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Impact of Covariance Mismatched Training Samples on Constant False Alarm Rate Detectors

Besson

2021

IEEE Trans. Signal Process.

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“…Constant False Alarm Rate (CFAR) techniques are aimed at keeping constant a certain desired Probability of False Alarm (PFA) [ 44 , 45 ]. In general, CFAR detectors estimate statistics of the disturbance from radar returns and tune the detector threshold to guarantee the above-mentioned condition [ 46 ]. This threshold is expressed, in general, as the product of two terms

, where

is the estimate of the interference statistic and

is the CFAR constant expressed as a function of the desired PFA.…”

Section: Literature On Drone Detectionmentioning

confidence: 99%

Detection and Classification of Multirotor Drones in Radar Sensor Networks: A Review

Coluccia

Parisi

Fascista

2020

Sensors

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Thanks to recent technological advances, a new generation of low-cost, small, unmanned aerial vehicles (UAVs) is available. Small UAVs, often called drones, are enabling unprecedented applications but, at the same time, new threats are arising linked to their possible misuse (e.g., drug smuggling, terrorist attacks, espionage). In this paper, the main challenges related to the problem of drone identification are discussed, which include detection, possible verification, and classification. An overview of the most relevant technologies is provided, which in modern surveillance systems are composed into a network of spatially-distributed sensors to ensure full coverage of the monitored area. More specifically, the main focus is on the frequency modulated continuous wave (FMCW) radar sensor, which is a key technology also due to its low cost and capability to work at relatively long distances, as well as strong robustness to illumination and weather conditions. This paper provides a review of the existing literature on the most promising approaches adopted in the different phases of the identification process, i.e., detection of the possible presence of drones, target verification, and classification.

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“…Adopting effective detection and countermeasure techniques to face the rising threat of small drones is a great concern of security agencies and governments today. There is indeed a gap in current surveillance systems, since the small size and fast maneuvers make drones a difficult category of targets to be detected, compared to traditional aircrafts [ 7 , 8 , 9 , 10 , 11 ]. Surveillance and detection technologies based on different modalities are under investigation, with different trade-offs in terms of complexity, maximum coverage in range, and capabilities [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Drone vs. Bird Detection: Deep Learning Algorithms and Results from a Grand Challenge

Coluccia

Fascista

Schumann

et al. 2021

Sensors

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Adopting effective techniques to automatically detect and identify small drones is a very compelling need for a number of different stakeholders in both the public and private sectors. This work presents three different original approaches that competed in a grand challenge on the “Drone vs. Bird” detection problem. The goal is to detect one or more drones appearing at some time point in video sequences where birds and other distractor objects may be also present, together with motion in background or foreground. Algorithms should raise an alarm and provide a position estimate only when a drone is present, while not issuing alarms on birds, nor being confused by the rest of the scene. In particular, three original approaches based on different deep learning strategies are proposed and compared on a real-world dataset provided by a consortium of universities and research centers, under the 2020 edition of the Drone vs. Bird Detection Challenge. Results show that there is a range in difficulty among different test sequences, depending on the size and the shape visibility of the drone in the sequence, while sequences recorded by a moving camera and very distant drones are the most challenging ones. The performance comparison reveals that the different approaches perform somewhat complementary, in terms of correct detection rate, false alarm rate, and average precision.

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CFAR Feature Plane: A Novel Framework for the Analysis and Design of Radar Detectors

Cited by 33 publications

References 22 publications

Impact of Covariance Mismatched Training Samples on Constant False Alarm Rate Detectors

Impact of Covariance Mismatched Training Samples on Constant False Alarm Rate Detectors

Detection and Classification of Multirotor Drones in Radar Sensor Networks: A Review

Drone vs. Bird Detection: Deep Learning Algorithms and Results from a Grand Challenge

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