Adaptive Detection of Point-Like Targets in Spectrally Symmetric Interference

Abstract-The adaptive classification of the interference covariance matrix structure for radar signal processing applications is addressed in this paper. This represents a key issue because many detection architectures are synthesized assuming a specific covariance structure which may not necessarily coincide with the actual one due to the joint action of the system and environment uncertainties. The considered classification problem is cast in terms of a multiple hypotheses test with some nested alternatives and the theory of Model Order Selection (MOS) is exploited to devise suitable decision rules. Several MOS techniques, such as the Akaike, Takeuchi, and Bayesian information criteria are adopted and the corresponding merits and drawbacks are discussed. At the analysis stage, illustrating examples for the probability of correct model selection are presented showing the effectiveness of the proposed rules.

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“…When H 2 is assumed, the ICM is unstructured and real. Thus, following the lead of [5], we use the following estimates…”

Section: A Mos Decision Rules Using the Entire Data Matrixmentioning

confidence: 99%

“…For each of the mentioned scenarios, there exist examples of adaptive detectors in the literature [4], [5], [31]. The knowledge about the environment as well as the structure of the ICM can guide the system operator towards the most appropriate decision scheme.…”

Section: Introduction Motivation and Problem Formulationmentioning

confidence: 99%

Model Order Selection Rules for Covariance Structure Classification in Radar

Carotenuto

Maio

Orlando

et al. 2017

IEEE Trans. Signal Process.

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“…These algorithms allow us to reduce the number of training samples required for the ICM estimation [16]- [23], [27], [28] by almost a half while maintaining a satisfactory detection performance. Further examples are provided in [24], [25], where it is shown that the spectral symmetry of the clutter can be used for instance to obtain gains of about 3 dB (in SINR, namely Signal-to-Interference plus Noise Ratio), for a Probability of Detection P d = 0.9 and Probability of False Alarm P f a = 10 −4 , in comparison to conventional detectors.…”

Section: Introductionmentioning

confidence: 99%

New ECCM Techniques Against Noiselike and/or Coherent Interferers

Yan

Addabbo

Chen

et al. 2020

IEEE Trans. Aerosp. Electron. Syst.

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Multiple-stage adaptive architectures are conceived to face with the problem of target detection buried in noise, clutter, and intentional interference. First, a scenario where the radar system is under the electronic attack of noise-like interferers is considered. In this context, two sets of training samples are jointly exploited to devise a novel two-step estimation procedure of the interference covariance matrix. Then, this estimate is plugged in the adaptive matched filter to mitigate the deleterious effects of the noise-like jammers on radar sensitivity. Besides, a second scenario, which extends the former by including the presence of coherent jammers, is addressed. Specifically, the sparse nature of data is brought to light and the compressive sensing paradigm is applied to estimate target response and coherent jammers amplitudes. The likelihood ratio test, where the unknown parameters are replaced by previous estimates, is designed and assessed. Remarkably, the sparse approach allows for echo classification and estimation of both angles of arrival and number of the interfering sources. The performance analysis, conducted resorting to simulated data, highlights the effectiveness of the newly proposed architectures also in comparison with suitable competing architectures (when they exist).

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“…Nevertheless, the reflectivity change with grazing angles and the inaccurate registering of the terrain data would degrade its performance. Additionally, the persymmetric structure of the interference covariance matrix and the symmetric property of power spectral density of the clutter can also be used as priori knowledges to improve the detection performances in training-limited scenarios [17][18][19][20]. And some STAP detection methods, which allow one to identify the degree of accuracy of the prior knowledge and combine the prior information with the secondary data in an appropriate way, were proposed in [21,22].…”

Section: Introductionmentioning

confidence: 99%

Deterministic-aided single dataset STAP method based on sparse recovery in heterogeneous clutter environments

Wang

Zou

Wang

et al. 2018

EURASIP J. Adv. Signal Process.

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Traditional space-time adaptive processing (STAP) usually needs many independent and identically distributed (i.i.d) training datasets for estimating clutter covariance matrix (CCM). But this requirement is hardly satisfied in the heterogeneous clutter environments, which lead to an inaccurate estimation of CCM and accordingly degrade the performance of STAP significantly. To improve the performance of STAP in heterogeneous environments, a novel deterministic-aided (DA) single dataset STAP method based on sparse recovery technique (SR) is proposed in this paper. This presented algorithm exploits the property that the clutter components of side-looking airborne or spaceborne radar are distributed along the clutter ridge to estimate the CCM of the cell under test (CUT) without any secondary training data. The new method only uses a single CUT data to acquire a high-resolution angle-Doppler power spectrum using sparse recovery (SR) approach and then employs a new adaptive deterministic-aided generalized inner product (GIP) algorithm to recognize and select the clutter components in the CUT angle-Doppler power spectrum automatically. Subsequently, the CCM, which is used to construct the weights of STAP filter, can be effectively estimated by the selected clutter components to fulfill the final STAP filter processing. Simulation results verify the effectiveness of the proposed detection method.

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Adaptive Detection of Point-Like Targets in Spectrally Symmetric Interference

Cited by 102 publications

References 39 publications

Model Order Selection Rules for Covariance Structure Classification in Radar

Model Order Selection Rules for Covariance Structure Classification in Radar

New ECCM Techniques Against Noiselike and/or Coherent Interferers

Deterministic-aided single dataset STAP method based on sparse recovery in heterogeneous clutter environments

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