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In order to study the waveform selection of radar seeker in the terminal guidance and tracking phase, this paper proposes a waveform selection algorithm in the guidance and tracking process of cognitive radar seeker. Firstly, we establish the processing flow of radar seeker waveform selection by combining the seeker guidance and observation filtering models; Secondly, we use the interactive multiple model unscented Kalman filter (IMMUKF) algorithm to solve the nonlinear tracking problem. We minimize tracking error by searching for waveform parameters based on the maximum mutual information criterion. The simulation results show that the waveform selection algorithm can effectively improve the seeker tracking accuracy and provide stable guidance overload. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Under the high repetition rate pulse system, the ground clutter of the side looking multiple input multiple output (MIMO) array bistatic radar system has serious range dependence and clutter spectrum broadening problem. At the same time, the multi-dimensional clutter structure of bistatic radar faces the difficulty of obtaining sufficient training samples and high complexity of adaptive weighting algorithms. In this paper, a range ambiguous clutter suppression method for frequency diversity array (FDA)-MIMO bistatic radar based on transmitting main lobe correction is proposed. This method solves the problem of the main lobe illumination angle movement by correcting the main lobe of the equivalent emission pattern at the receiving end. Through the correction and compensation of the equivalent emission pattern main lobe and the reverse compensation after the received signal, the echo has a phase term of range coupling, which realizes the effective distinction of clutter at different range areas, solves the problem of clutter range ambiguity, and completes clutter suppression. The three-dimensional search is spit into the form of "2 + 1". Firstly, the clutter is suppressed in the emission angle frequency domain and the Doppler domain, and the target parameter search is performed. Then, construct a dimensionality reduction matrix according to the obtained target transmitting angle and Doppler information, and perform dimensionality reduction search in the receiving angle frequency domain, which solves the problem of insufficient clutter samples and large amount of calculation. Finally, this method solves the problem of ground range clutter ambiguity in the large scene of bistatic early warning radar. Simulation experiments verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Low probability of intercept (LPI) radar is a new type of radar with strong anti-jamming ability and low interception characteristics. Accurate and efficient recognition of it has become a difficult problem in waveform recognition of radar confrontation. Aiming at the structural intelligent optimization problem of the mainstream classifier convolution neural network (CNN) in this direction, a waveform recognition algorithm based on particle swarm optimization (PSO)-CNN is proposed. The algorithm uses the optimization characteristics of PSO to automatically build CNN structure with indefinite layers, layer types and layer parameters in a large range, evaluate and iteratively optimize it. The algorithm uses a combination of recognition accuracy and network complexity to measure indicators, the proportion of the two can be adjusted according to requirements to achieve the choice of accuracy and lightness. The CNN structure obtained by the algorithm achieves better LPI radar waveform recognition effect than nine classic CNN structures. And the algorithm avoids the lack of intelligence and objectivity of artificially selecting CNN hyper parameters during radar waveform recognition, and improves the adaptability and efficiency of the selecting CNN structure. [ABSTRACT FROM AUTHOR]

Aiming at solving the problem of difficulty in extracting features of radar emitter signals and low recognition accuracy under the condition of low signal to noise ratio, a radar emitter signal based on sliced recurrent neural networks (SRNN), attention mechanism and convolutional neural networks (CNN) is proposed. Batch normalization layer is introduced into CNN to further improve the recognition ability of the network. Taking the amplitude sequence of radar emitter signal as input, the signal characteristic is extracted automatically and the recognition result of radar emitter signal is output. Compared with gated recurrent unit (GRU), the experimental results show that the training speed of SRNN is greatly improved, and the attention mechanism and batch normalization layer can effectively improve the recognition accuracy. In the experiments with eight common radar emitter signals, the proposed method still has a high recognition accuracy under the condition of low signal to noise ratio. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In view of the problem of radar anti-jamming performance evaluation, a method of radar anti-jamming performance evaluation based on grey correlation fuzzy comprehensive evaluation is proposed. Based on the theory of fuzzy mathematics and correlation function, the set of fuzzy comprehensive evaluation factors of the evaluation index for reference is used firstly. Through scoring by the interval number, the weight of the evaluation index is determined based on the grey correlation method. Then, the fuzzy comprehensive evaluation results are calculated by establishing the evaluation matrix by the membership function, and the anti-jamming capability level of radar is determined according to the membership degree. Finally, the fuzzy value linear order evaluation model is established to determine the order of radar anti-jamming performance. The example shows that the proposed method can not only determine the evaluation level of radar anti-jamming performance, but also further determine the order of radar anti-jamming performance, which is more objective and reliable. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To solve the problem that the classification network is difficult to effectively expand the number of classifications, a radar signal recognition method based on deep residual network and triplet loss is proposed. This method firstly takes the radar signal as the input of the deep residual network, maps the radar signal to 128-dimensional Euclidean space through one-dimensional convolution, and obtains the signal's eigenvector; then uses the triplet loss function to adjust the network parameters so that the Euclidean distance of feature vectors between homogeneous signals decreases and the distance between different types of signals increases; finally, the classification of the signals is realized through a sample library-based recognition algorithm. Experimental results show that compared with traditional classification networks, this method ensures the accuracy of recognition while enabling the model to effectively expand the number of classifications. [ABSTRACT FROM AUTHOR]

To solve the problem that the traditional space-time adaptive and processing method cannot effectively suppress the heterogeneous clutter due to lack of enough independent identically distributed training samples, an iteration-separable clutter suppression algorithm for airborne planar array radar is proposed. Firstly, the clutter representation basis matrix is constructed based on some priori information such as radar system and aircraft platform motion parameters. Then, the spatial and temporal clutter power spectrum of cell under test (CUT) are separately estimated by the covariance matching estimation criterion. Next, the spatial and temporal partial weights are calculated through the linearly constrained minimum variance criterion. Finally, combined with the clutter parameterized model and the obtained spatial and temporal partial weight vectors, the space-time filtering weight vector is calculated to process the CUT echo data. The proposed method is hyperparame-ter-free, which is global convergent and does not need the training samples. In addition, the proposed method can effectively suppress the heterogeneous clutter of echo data of airborne planar array radar even if range ambiguity exists. Simulation results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

For multiple-input multiple-output over-the-horizon radar( MIMO-OTHR),the minimum variance distortionless response( MVDR) is usually used to solve the impact of the ionospheric multimode clutter in the received echo on the detection of the target. The output signal-to-clutter-plus-noise ratio( SCNR) of traditional MVDR algorithm is low due to the omnidirectional emission of MIMO-OTHR,the inaccurate estimation of interference-noise covariance matrix,and a few training samples within a finite coherent accumulation time. Therefore,the MVDR algorithm is improved to enhance the multimode clutter suppression ability of MIMO-OTHR. A transmit beamspace preprocessing is realized by a second order cone programming( SOCP) to focus the energy on the principal mode angle,which is the transmit angle of the target path reflected by the ionosphere. The echo signal received by array is spatially smoothed to suppress the expected signal of principal mode and improve the estimation accuracy of interference-noise covariance matrix. A bi-iterative MVDR beamforming algorithm based on the beamspace preprocessing is proposed to reduce the computational load and the requirement of training samples. Simulated results show that the improved MVDR algorithm can be used to effectively suppress multi-mode clutter,and obtain higher output SCNR with fewer training samples and less computational load. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Beijing University of Technology is the property of Journal of Beijing University of Technology, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Adaptive waveform optimization technology is one of the key technologies of cognitive radar， which can improve the performance of radar systems. For the target detection， an adaptive waveform design method based on dual mutual information (DMI) criterion was proposed in the clutter environment. The mutual information (MI) was maximized by the proposed method between the received signal and the target impulse response while the mutual information could be minimized between the received signal and clutter impulse response. With the transmitted signal energy constraint， the optimization model for optimal waveform was established， and the optimal waveform was solved by the maximum marginal allocation algorithm (MMA). The simulation results demonstrate the validation of the proposed method. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

When the interference enters from the direction of the main-lobe of the radar, adopting the adaptive beamforming algorithm will lead to problems such as beam distortion and migration of the main-lobe. Aiming at the problem of interference entering from the radar main-lobe, this paper proposed an anti-jamming method based on the design of the transmitted waveform. When the signal encoded by Frank is transmitted, it constructed the matched filter and the blocking filter by using the orthogonality of the Frank code, and passed the received data through the two filters to obtain two signals. Then it suppressed the interference by the adaptive cancellation method, thus suppressed the main-lobe interference. Finally, simulation results show that the method of waveform design can effectively suppress interference from main-lobe entry. [ABSTRACT FROM AUTHOR]

In view of the limitation of the range factor on analyzing low probability of intercept (LPI) performance of MIMO radar systems, a performance parameter is derived and applied for quantifying the LPI performance of distributed MIMO radar systems. A series of contour charts have been given with different values of geometrical parameter. By plotting and analyzing these charts, the LPI performance can be obtained intuitively. Simulation results show that LPI performance of distributed MIMO radar systems depends on both radar parameter and system geometry. It is shown that the spatial diversity gain plays an important role in designing a distributed MIMO radar system with robust LPI performance. [ABSTRACT FROM AUTHOR]

Compared with conventional radar system, MIMO radar system has huge computation because of the large dimensions of echo data in receiving beamforming using MVDR or LCMV. Considering that the transmitted waveforms of MIMO radar is orthogonal to each other, the full-dimensional MIMO radar can be thought a combination of multiple SIMO radars in receiving beamforming after matched filter. This kind of equivalent not only reduces the dimensions and number of data snapshot, but also cuts down the computation when the covariance matrix and its inverse operation were calculated. In addition, the iterative solution is not required compared with the existing algorithm. Simulation results validates that the new method has a similar performance with the full-dimensional algorithm when the number of data snapshot is large enough, and keeps good performance as the number of data snapshot is fewer, meanwhile, the computation of the new method is greatly decreased compared to the full-dimensional method. [ABSTRACT FROM AUTHOR]

The Multiple-Input Multiple-Output (MIMO) radar was proposed in recent years. Using MIMO radar to detect the location of multi-target, a lot of algorithms are failure when the multi-target echo signals are coherent. Concerning this problem, a localization algorithm of coherent multi-target is presented in bistatic MIMO radar. The algorithm constructs virtual array for both the transmit and receiver array to de-correlate the coherent multi-target echo signals, and then the location of multi-target can be obtained. The estimated parameters are automatically paired without additional paring computation. The proposed method avoids the array aperture loss and the performance of angle estimation is improved. Theoretical analysis and simulation results verify the effectiveness of this method. [ABSTRACT FROM AUTHOR]