Implementing digital terrain data in knowledge-aided space-time adaptive processing

Capraro, C.T.; Capraro, Gerard T.; Bradaric, I.; Weiner, D.D.; Wicks, Michael C.; Baldygo, W.

doi:10.1109/taes.2006.248199

Cited by 73 publications

(41 citation statements)

References 18 publications

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“…To compare the performance of the proposed method, we also give the results of the classical sliding window method, GIP method and KA method proposed by Capraro et al [12]. To reduce the computational complexity and the sample support required by the weight training, we adopt the factor approach [22], a reduce-dimension STAP method.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

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Robust training samples selection algorithm based on spectral similarity for space–time adaptive processing in heterogeneous interference environments

Wang

et al. 2015

IET Radar, Sonar & Navigation

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In space-time adaptive processing (STAP), the required interference covariance matrix is commonly estimated from homogeneous samples. However, the real interference environments are often heterogeneous, which is induced by the fact that the training samples do not share the same interference property with the cell under test (CUT), and thus the interference covariance matrix estimated from the training samples is mismatched with the real one. To improve the performance of STAP in heterogeneous interference environments, this paper proposes a robust training samples selection algorithm which is based on the spectral similarity of the interference. The spectrums of samples are estimated firstly, which are then utilised to calculate the similarity between the interference of the CUT and those of the training samples. Then, the samples whose interference spectrums are similar to that of the CUT are selected as the final training samples, such that the performance of the interference covariance matrix estimated from these samples can be improved significantly. Besides, to avoid the target self-nulling effect, a simple technique is adopted to exclude the samples which are contaminated by target signal. The proposed method is applied to real radar data, and experimental results demonstrate the effectiveness of the proposed method.

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Section: Experimental Results and Analysismentioning

confidence: 99%

“…In [11], the power residue algorithm is proposed, but it may result in target self-nulling. Capraro et al [12] successfully illustrate the benefits of the digital terrain data's application in STAP, i.e. the knowledge-aided STAP (KA-STAP).…”

Section: Introductionmentioning

confidence: 99%

Robust training samples selection algorithm based on spectral similarity for space–time adaptive processing in heterogeneous interference environments

Wang

et al. 2015

IET Radar, Sonar & Navigation

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“…Accurate and up-to-date roadmaps are crucial for many purposes such as navigation, target tracking [1,2], and airborne knowledge-aided space-time adaptive processing (STAP) [3,4]. Digital road maps produced by the National Imagery and Mapping Agency (NIMA) as well as the United States Geological Survey (USGS), the two most common sources of such maps, often have errors that are large compared to the resolution of the GMTI sensors [5].…”

Section: Introductionmentioning

confidence: 99%

Road Network Conflation Based on Radar Tracks

Niu¹,

Varshney²

2014

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“…It is shown that the a priori knowledge collected from the auxiliary information about the terrain feature can be incorporated into the detection scheme in two ways: on one hand, the prior knowledge can be exploited to select the high-quality secondary samples (see e.g. [6][7][8][9] and the references therein). On the other hand, the prior knowledge can be used to prewhiten the data and form the Bayesian filter [10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Robust fast maximum likelihood with assumed clutter covariance algorithm for adaptive clutter suppression

Tang

Zhang

Tang

2014

IET Radar, Sonar & Navigation

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The mismatch between the clutter and noise power of the prior knowledge and true interference covariance matrix degrades the performance of fast maximum likelihood with assumed clutter covariance (FMLACC) algorithm significantly. By introducing a scale parameter to flexibly adjust the prior power, the authors propose an algorithm which is more robust to the power mismatch than FMLACC algorithm. They also develop a more straightforward method to derive the maximum likelihood covariance matrix estimator under this scaled knowledge constraint. Moreover, they study the problem of automatically determining the scale parameter. The authors provide two parameter selection methods, the first of which is based on estimating the minimum eigenvalue of the prewhitened sample covariance matrix, and the second is based on cross validation. To reduce the computational complexity, they also develop fast implementations for the parameter selection based on cross validation. Numerical simulations demonstrate the performance enhancement of the proposed algorithm compared with FMLACC algorithm in cases of mismatched prior knowledge.

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Implementing digital terrain data in knowledge-aided space-time adaptive processing

Cited by 73 publications

References 18 publications

Robust training samples selection algorithm based on spectral similarity for space–time adaptive processing in heterogeneous interference environments

Robust training samples selection algorithm based on spectral similarity for space–time adaptive processing in heterogeneous interference environments

Road Network Conflation Based on Radar Tracks

Robust fast maximum likelihood with assumed clutter covariance algorithm for adaptive clutter suppression

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