Knowledge-aided adaptive detection in partially homogeneous clutter: Joint exploitation of persymmetry and symmetric spectrum

Foglia, Goffredo; Chen, Hao; Giunta, Gaetano; Orlando, Danilo

doi:10.1016/j.dsp.2017.04.003

Cited by 28 publications

(10 citation statements)

References 44 publications

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“…The SCM R , i.e. the covariance matrix of clutter plus noise of the bistatic MIMO radar system is

R = E false(false(bold-italicc + bold-italicn false) \cdot false(bold-italicc + bold-italicn false)^{normalH} false) .

In the STAD frame, R can be estimated as

\overset{false^}{bold-italicR} = \frac{1}{K} {false\sum}_{k = 1}^{K} false[false(c_{k} + n_{k} false) \cdot {false(c_{k} + n_{k} false)}^{normalH} false] .

To achieve more mutually independent target‐free reference data, a KA method as in [10, 11] is applied to the secondary data and we get

\begin{matrix} right leftthickmathspace.5em \\ {bold-italicz}_{1 r} (k) & = ℜ (({bold-italicc}_{k} + {bold-italicn}_{k}) + bold-italicJ ({bold-italicc}_{k} + {bold-italicn}_{k}) *), \\ {bold-italicz}_{1 i} (k) & = ℑ (({bold-italicc}_{k} + {bold-italicn}_{k}) + bold-italicJ ({bold-italicc}_{k} + {bold-italicn}_{k}) *), \\ {bold-italicz}_{2 r} (k) & = ℜ (({bold-italicc}_{k} + {bold-italicn}_{k}) - bold-italicJ ({bold-italicc}_{k} + {bold-italicn}_{k}) *), \\ {bold-italicz}_{2 i} (k \end{matrix}

…”

Section: Ka–stad Methods For Bistatic Mimo Systemmentioning

confidence: 99%

“…To achieve good target detection performance with insufficient secondary data, a knowledge‐aided (KA) transform under covariance matrix persymmetry and symmetric spectrum presented in [10] is considered and applied in STAD frame in the study. The KA transform uses the spectral symmetric property of the secondary data and the persymmetric property of the sample covariance matrix (SCM) so that the number of secondary data can increase fourfold.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Space‐time adaptive detection for bistatic MIMO system under covariance matrix persymmetry and symmetric spectrum

Yan¹,

Chen²,

Xu³

et al. 2019

J. eng.

Self Cite

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“…The SCM R , i.e. the covariance matrix of clutter plus noise of the bistatic MIMO radar system is

R = E false(false(bold-italicc + bold-italicn false) \cdot false(bold-italicc + bold-italicn false)^{normalH} false) .

In the STAD frame, R can be estimated as

\overset{false^}{bold-italicR} = \frac{1}{K} {false\sum}_{k = 1}^{K} false[false(c_{k} + n_{k} false) \cdot {false(c_{k} + n_{k} false)}^{normalH} false] .

To achieve more mutually independent target‐free reference data, a KA method as in [10, 11] is applied to the secondary data and we get

\begin{matrix} right leftthickmathspace.5em \\ {bold-italicz}_{1 r} (k) & = ℜ (({bold-italicc}_{k} + {bold-italicn}_{k}) + bold-italicJ ({bold-italicc}_{k} + {bold-italicn}_{k}) *), \\ {bold-italicz}_{1 i} (k) & = ℑ (({bold-italicc}_{k} + {bold-italicn}_{k}) + bold-italicJ ({bold-italicc}_{k} + {bold-italicn}_{k}) *), \\ {bold-italicz}_{2 r} (k) & = ℜ (({bold-italicc}_{k} + {bold-italicn}_{k}) - bold-italicJ ({bold-italicc}_{k} + {bold-italicn}_{k}) *), \\ {bold-italicz}_{2 i} (k \end{matrix}

…”

Section: Ka–stad Methods For Bistatic Mimo Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Space‐time adaptive detection for bistatic MIMO system under covariance matrix persymmetry and symmetric spectrum

Yan¹,

Chen²,

Xu³

et al. 2019

J. eng.

Self Cite

View full text Add to dashboard Cite

“…In order to alleviate the sample-starvation effect, a priori knowledge of the clutter covariance matrix is a possible way to improve covariance matrix estimation accuracy [17]. The knowledge-aided (KA) technology, as an important part of a cognitive radar system [18], has been widely used in the secondary data selection [19], detector design [20,21,22], and covariance matrix estimation [23,24,25]. The essence of KA method is to use the prior information to reduce the DOF of covariance matrix estimation, and hence obtain a more accurate result in small sample case [26].…”

Section: Introductionmentioning

confidence: 99%

“…The KA covariance estimation methods can fall loosely into three categories based on the difference of knowledge. The first category of knowledge is the structure information of covariance matrices, such as symmetric spectrum [21,27], persymmetry structure [20,22,28] and identity matrix [29,30,31]. The second category of knowledge is the prior statistical distribution knowledge of the data of covariance matrix, such as the Wishart or inverse Wishart distributed [32,33,34].…”

Section: Introductionmentioning

confidence: 99%

Knowledge-Aided Structured Covariance Matrix Estimator Applied for Radar Sensor Signal Detection

Kang

Shang

Du³

2019

Sensors

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This study deals with the problem of covariance matrix estimation for radar sensor signal detection applications with insufficient secondary data in non-Gaussian clutter. According to the Euclidean mean, the authors combined an available prior covariance matrix with the persymmetric structure covariance estimator, symmetric structure covariance estimator, and Toeplitz structure covariance estimator, respectively, to derive three knowledge-aided structured covariance estimators. At the analysis stage, the authors assess the performance of the proposed estimators in estimation accuracy and detection probability. The analysis is conducted both on the simulated data and real sea clutter data collected by the IPIX radar sensor system. The results show that the knowledge-aided Toeplitz structure covariance estimator (KA-T) has the best performance both in estimation and detection, and the knowledge-aided persymmetric structure covariance estimator (KA-P) has similar performance with the knowledge-aided symmetric structure covariance estimator (KA-S). Moreover, compared with existing knowledge-aided estimator, the proposed estimators can obtain better performance when secondary data are insufficient.

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“…The utilisation of the persymmetric structure can be traced back to the application of the CCM maximum likelihood (ML) estimator for Gaussian environment [15]. Following that, several adaptive detectors taking into account the persymmetric property have been proposed for point‐like targets scenarios in [18–40]. In particular, this property is applied to the multi‐band signal detection in Gaussian noise [18].…”

Section: Introductionmentioning

confidence: 99%

Persymmetric generalised adaptive matched filter for range‐spread targets in homogeneous environment

Jian

Wang

et al. 2019

IET Radar, Sonar & Navigation

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This study deals with the problem of adaptively detecting range-spread targets in Gaussian environment, by assuming a persymmetric structure of covariance matrix. A persymmetric detector based on the two-step generalised likelihood ratio test design scheme is devised to reduce secondary data requirement, which ensures the constant false alarm rate property. Moreover, the general closed-form expressions of the probabilities of the false alarm and detection for the proposed detector are derived, in both cases of deterministic and fluctuating scatter models. Furthermore, the compact versions of forenamed expressions are presented in a special case when the joint space-time dimension of the received signal is odd. Finally, the numerical results of these derived statistical expressions are confirmed with those of Monte Carlo trials.

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Knowledge-aided adaptive detection in partially homogeneous clutter: Joint exploitation of persymmetry and symmetric spectrum

Cited by 28 publications

References 44 publications

Space‐time adaptive detection for bistatic MIMO system under covariance matrix persymmetry and symmetric spectrum

Space‐time adaptive detection for bistatic MIMO system under covariance matrix persymmetry and symmetric spectrum

Knowledge-Aided Structured Covariance Matrix Estimator Applied for Radar Sensor Signal Detection

Persymmetric generalised adaptive matched filter for range‐spread targets in homogeneous environment

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