This study proposes a novel approach to suppress noise jamming and smart jamming. The traditional method of using auxiliary channels to cancel interference requires pure interference samples to ...calculate weights, which is almost impossible for pulsed interference signals. In this work, to avoid the difficulty of choosing suitable interference samples, we construct the parameterized expected signal according to the time-delay relation between target reflecting echo and transmitted signal. The objective function is established in the form of the minimum mean square error between the recovered signal and the expected signal. The optimization problem is solved by an alternating iteration method. Simulation results demonstrate that the proposed method achieves excellent performance for suppressing noise jamming and smart jamming and is not sensitive to signal-to-noise ratio and jamming-to-noise ratio. The processing results of the measured data show that the method has a certain practical application value.
We propose anew antenna array design approach for a multiple-input and multiple-output (MIMO) radar, which has closed-form expressions for the sensor locations and the number of achievable degrees of ...freedom (DOFs). This new approach utilizes the nested array as transmitting and receiving arrays. We employ the difference coarray of the sum coarray (DCSC) of the MIMO radar to obtain more DOFs for direction-of-arrival (DOA) estimation. Via properly designing the interelement spacings of the transmitting and receiving arrays, we can obtain a hole-free DCSC. The characteristics of array geometries are analyzed and the optimal numbers of sensors in transmitting/receiving antenna array are derived when given the total number of physical sensors. Simulations are conducted to demonstrate the advantages of the proposed array in terms of the number of DOFs, the number of resolvable sources, and the DOA estimation performance over the coprime MIMO array.
This paper addresses the recognition problem of velocity gate pull-off (VGPO) jamming from the target echo signal for the velocity-based tracking system. The discrete chirp-Fourier transform (DCFT) ...is studied in this paper to jointly estimate the chirp rates and frequencies of the target and jamming signals. Firstly, the scaling characteristic of the DCFT algorithm is explored. Then, we focus on the quantitative effect of the VGPO jamming signal by analyzing the jointly estimated results in each pulse. The quantitative effect indicates that, as long as the estimated frequency is unchanged, the relationship between the estimated chirp rate and the pulse numbers is similar to the relationship between the frequency offset of VGPO jamming and the time. Finally, by utilizing the joint estimated results in each pulse repetition interval and calculating the mean square to variance ratio (MSVR) of the normalized estimated chirp rate, the VGPO jamming can be recognized. Simulation results show that the jamming signal and the target echo become distinguishable with the proposed feature. Comparing to the existing works, the proposed method can correctly recognize the jamming signal with lower jamming-to-noise ratio (JNR) 5 dB with less data needed, which means it can work effectively in the early stage of interference implementation and shows great potential in practical applications.
With the development of electronic warfare, anti‐jamming measure becomes more and more complex. There have been certain research results on jamming strategies, but only a few research materials on ...anti‐jamming strategies. It is difficult to simulate the real jamming environment, and there is no appropriate anti‐jamming decision‐making model for research. Cognitive radar can perceive the environment and receive feedback, which provides the possibility to solve the problem of anti‐jamming decision‐making. This article regards the anti‐jamming measure as a kind of interaction behaviour and establishes the cognitive radar antagonistic environment model and uses the reinforcement learning algorithm to solve the problem of anti‐jamming decision‐making. Finally, this article verifies the feasibility of applying reinforcement learning theory on making anti‐jamming decision in the radar antagonistic environment model. The performance of different reinforcement learning algorithms is compared, and their advantages and disadvantages are discussed.
This work proposes an intelligent anti‐jamming decision‐making scheme designed for the cognitive jamming to an anti‐jamming interaction mode. This scheme is suitable for scenarios with varying jamming types and the capability to predict jamming behaviour.
This paper proposes a time difference of arrival (TDOA) passive positioning sensor selection method based on tabu search to balance the relationship between the positioning accuracy of the sensor ...network and system consumption. First, the passive time difference positioning model, taking into account the sensor position errors, is considered. Then, an approximate closed-form constrained total least-squares (CTLS) solution and a covariance matrix of the positioning error are provided. By introducing a Boolean selection vector, the sensor selection problem is transformed into an optimization problem that minimizes the trace of the positioning error covariance matrix. Thereafter, the tabu search method is employed to solve the transformed sensor selection problem. The simulation results show that the performance of the proposed sensor optimization method considerably approximates that of the exhaustive search method. Moreover, it can significantly reduce the running time and improve the timeliness of the algorithm.
When the elevation of targets is smaller than beamwidth, the coherent multi-path signals will significantly degrade the direction of arrival (DOA) estimation accuracy of existing methods for a ...very-high-frequency (VHF) radar system. Through detailed theoretical analysis, we demonstrate that the phase distortion is the key factor of degrading the accuracy of DOA estimation. Hence, a novel phase enhancement model based on supervised convolutional neural network (CNN) for coherent DOA estimation is proposed to mitigate the phase distortion and improve estimation accuracy. The results of simulation experiments and real data have demonstrated the superiority of proposed method in DOA estimation accuracy and resolution compared to classic physics-driven methods. Moreover, the proposed scheme is suitable for the coherent DOA estimation compared with existing data-driven methods.
Multistatic radar has the advantage of spatial diversity, but its detection performance is decreased in dense multipath scattering environments. This problem can be solved by using the time reversal ...(TR) technique to take advantage of the multipath effect to improve target detection; however, this usually requires a stationary channel response that is difficult to achieve in practice. This article studies a TR detection algorithm for a multistatic radar system in a varying multipath channel environment. We establish a varying channel response model and derive a time reversal likelihood ratio test (TR–LRT) detector to utilise the characteristics of multiple paths when the multipath environment is changing during the detection process. We use Monte Carlo simulations and theoretical analysis to show the superior performance of the proposed TR detector compared with a conventional detector. Our proposed TR–LRT detector shows good robustness to environmental variations. Simulation results show that better detection results are achieved in a more severe multipath scattering environment.
Array imperfections severely degrade the performance of most physics-driven direction-of-arrival (DOA) methods. Deep learning-based methods do not rely on any assumptions, can learn the latent data ...features of a given dataset, and are expected to adapt better to array imperfections compared with existing physics-driven methods. Hence, an improved DOA estimation method based on long short-term memory (LSTM) neural networks for situations with array imperfections is proposed in this paper. Various analyses given by this paper demonstrate that the phase features are the key to DOA estimation. Considering the sequential characteristics of the moving target and the correlation of multi-frame data features, the LSTM neural networks are used to learn and enhance the phase features of sampled data. The DOA estimation accuracy and generalization capability are improved by mitigating the phase distortion using LSTM. Numerical simulations and statistical results show that the proposed method is satisfactory in terms of both the generalization capability and imperfection adaptability compared with state-of-the-art physics-driven and data-driven methods.
Polarisation diversity radar enhances the performance of target detection. Time reversal exploits the multipath effect by adaptively matching propagation channels to improve the signal‐to‐noise ratio ...(SNR) and realise high detectability. This work combines polarisation diversity and time‐reversal technology to propose a time‐reversal detector suitable for a polarisation array radar. The maximum likelihood estimate is carried out for the time‐varying channel response to solve the channel mismatch problem, which may cause the degradation of time‐reversal performance. In addition, a conventional polarisation detector is developed under the same varying channel scenario to benchmark the time‐reversal detector, and furthermore, the SNR gain of the time‐reversal detector over the conventional detector is derived. Numerical simulations demonstrate the superiority of the time‐reversal detector. Both the polarisation detectors proposed can take advantage of the channel variations to improve the detectability, and the greater the energy brought by the channel changes, the better the detection performance achieved.
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