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A method of robust clutter suppression with space time adaptive processing (STAP) for airborne radar in heterogeneous environments is proposed, which is based on multi frames and the similarity between the cell under test and each training sample. The proposed method deals with the problem of covariance matrix estimation for radar signal processing, and it provides a solution to overcome the performance degradation of STAP in heterogeneous environments and training samples limitation. Firstly, the method expands the set of training samples by selecting training frames from past frames. Secondly, initial training samples are selected from the expended training samples set, which is composed by the samples of current frame and past frames. Thirdly, general inner product method is adopted to discard heterogeneous samples. Fourthly, the similarities between the cell under test and the remaining training samples are estimated, and training samples which are more similar with the cell under test take higher weight in the estimation of clutter covariance matrix. The accuracy of the estimated clutter character is improved significantly, and thus the performance of clutter suppression is improved. Experimental results based on measured data demonstrate the performance of the proposed method.

The distribution of traffic in the WiNoC system is variable in time and space. Based on this feature, researchers have proposed a kind of scheme which is dynamic MAC mechanism to adaptively allocate bandwidth and improve data communication efficiency. However, this kind of scheme has a large power consumption overhead. Therefore, this paper designs a dynamic and finegrained method based on different wireless usage rates. The fine-grained distributed power gate control scheme dynamically switches the power gated working state according to different mechanisms of the MAC mechanism, and effectively reduces the data communication power consumption in the network with a small area overhead. Experiments show that: although this scheme increases the area overhead by 6.42%, it reduces the power consumption overhead by 26.4%

In order to adapt to China's energy transformation, construction ideas of the new generation control system clearly pointed out the need to improve the optimal combination of transmission and transformation equipment, the economy of power cut plan and the intelligence of auxiliary decision making. On the basis of this, this paper constructs the decision model of annual power cut plan, and proposes an improved DMODA method. On the basis of the original multi-objective Dragonfly algorithm, the external archiving maintenance strategy and the niche sharing mechanism under the crowded distance are added to improve the overall Bureau and the distribution uniformity of the original algorithm, so that the annual blackout plan is economical, reliable, and distributed all. Both the balance and reserve are all optimal. Finally, the simulation results show that the proposed method can meet the needs of actual power grid outage plan effectively and effectively.

As training samples are not always identically distributed with the clutter in the cell under test (CUT) in heterogeneous environments, the estimated clutter covariance matrix for space-time adaptive processing (STAP) is not accurate, which degrades the performance of STAP. To improve the performance of STAP in heterogeneous environments, this letter proposes a novel training sample selection algorithm to estimate the covariance matrix. Based on the subaperture smoothing techniques, subapertures' covariance matrices are estimated, which are used to measure the similarities between the clutter covariance matrix of the CUT and the clutter covariance matrices of the training samples. Training samples whose clutter covariance matrices are similar to that of the CUT are selected, leading to a better estimation of the clutter covariance matrix, and the performance of STAP improves. Experimental results confirm the performance of the proposed algorithm.

In this letter, a novel polarimetric target decomposition (PTD) method based on the attributed scattering center (ASC) model is proposed for man-made targets in synthetic aperture radar (SAR) images. By extracting attributed parameters, polarimetric characteristics of targets can be exploited by performing PTD on the extracting parameters of ASCs instead of pixels in conventional PTD algorithms. As a result, the integrity of target components is enhanced, leading to a reliable analysis on the polarimetric scattering mechanisms of SAR targets. In the proposal, an attributed parameters extraction method based on joint exploitation of multiple polarimetric channels and a target discriminating method based on a constant-false-alarm threshold are developed to improve its robustness in strong noise scenarios. Experimental results confirm the effectiveness of the proposed algorithm.

For non-sidelooking airborne radar operating in medium pulse repetition frequency mode, the space–time adaptive processing (STAP) technique suffers from a significant performance loss due to the range dependence of clutter. Here, an efficient clutter suppression approach is presented to improve STAP performance. Utilising the characteristics of clutter, the near-range clutter, the major reason for the performance degradation, is extracted from raw returns using a specially designed oblique projector firstly, and then is mitigated by an adaptive beamformer estimated from the extracted components. Subsequently, an azimuth-Doppler STAP beamformer is employed to further suppress the residual clutter. To enhance the robustness of the approach, a simple steering vector calibration technique is taken into account. The proposed approach can eliminate the near-range clutter effectively, and therefore alleviate the clutter range dependence greatly. The STAP performance can be improved significantly. Simulation results demonstrate the effectiveness of the presented method.

Since training samples are not always identically distributed with the clutter in the cell under test (CUT) in heterogeneous clutter environments, which lead to an inaccurate estimate of clutter covariance matrix (CCM), the performance of space time adaptive processing (STAP) degrade significantly. In order to improve the performance of STAP, we propose a robust weighted knowledge-aided (KA) STAP algorithm. Unlike the same weights used for sample covariance matrix estimation, the presented algorithm uses different weights on training samples for CCM, where the weights are developed based on the similarities between the a priori covariance matrix of the CUT and that of the training data. The algorithm is capable of measuring the effect of heterogeneous samples on CCM more properly, and then we obtain a covariance matrix which reflects the clutter property of CUT more accurately. As a result, the performance of weighted KASTAP is better than conventional STAP in clutter suppression. Experimental results using the real data verify the effectiveness of the proposed method.

In this paper, a novel signal reconstructing method for radar targets is proposed based on the attributed scattering center model. By extracting the attributed parameters, the large amount of target data can be represented by small amounts of attributed parameters. In this way, the data amount has been compressed sharply, which releases the computer memory for storage. After extraction, a target discriminating method is presented by applying a CFAR threshold to the energy of extracted attributed scattering centers, by which, weak distributed scattering centers with relatively high energy in total can be discriminated from noise under low SNRs. Experimental results validate the effectiveness of the signal reconstructing capability of the proposal.