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Abstract 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.

Abstract Chaff is widely used in electronic countermeasures as an effective passive jamming method. This study proposes a chaff identification method that integrates the distribution of distance, Doppler frequency, and power in Range‐Doppler imaging. The traditional radar signal processing method estimates the distance and Doppler frequency, and the mean‐shift algorithm is combined to complete the clustering after the constant false alarm detection. Then, the target and chaff cloud models are modelled in three dimensions: radar cross‐section, range, and Doppler frequency. Using the Neyman–Pearson criterion, three chaff likelihood ratio test detectors are designed based on these assumptions. The three detectors can effectively identify the chaff, and then the theoretical detection probability and influencing factors are analysed. At the same time, the Kullback–Leibler divergence is used to describe the distribution difference between the data to be detected and the theoretical target and chaff. Besides, the different classifiers are used to identify the features of the differences in Kullback–Leibler divergence in three dimensions. Finally, the real‐life chaff datasets verified the excellent recognition rate of the authors’ method for the chaff.

Abstract 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.

Abstract 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.

Abstract 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.

Abstract The increasing demand for low‐cost and low‐power radars has led to the emergence of 1‐bit sampling technology. 1‐bit sampling technology is introduced in a multiple‐input and multiple‐output (MIMO) radar that has many advantages such as a high degree of freedom and flexible transmit beam pattern. The authors’ theoretical derivation shows that after 1‐bit quantisation, the received signal can be expressed as a linear combination of infinite components, which include the original component and high‐order components that can be regarded as interference terms. On this basis, the signal‐to‐noise‐ratio (SNR) loss after 1‐bit sampling at a low SNR is 1.96 dB. Furthermore, the Cramér‐Rao bound (CRB) of a MIMO radar with 1‐bit sampling is derived. In the derivation, the concept of 1‐bit equivalent noise is proposed. At low SNRs, the equivalent noise power is π/2 (1.96 dB) times the original noise power, which is consistent with a previous derivation. Numerical simulations showed the feasibility of traditional algorithms for a MIMO radar with 1‐bit sampling and the correctness of the authors’ derivation.

Thermochemical processes are considered as a promising technology for sewage sludge management, which could achieve volume reduction, energy and resource recovery, and effective destruction of pathogens. However, there are still many technology limitations and challenges of the thermochemical processes toward industrialization and commercialization. This review first briefly discusses the impact of sewage sludge on environmental sustainability and its current treatment and disposal methods. Typical thermochemical conversion technologies i.e., incineration/combustion, pyrolysis, gasification, and hydrothermal liquefaction for energy and resource recovery from sewage sludge are then comprehensively summarized. Subsequently, the technical challenges of thermochemical conversion of sewage sludge and the solutions that have been and/or are being developed to address the challenges are in-depth analyzed. Meanwhile, the prospects and future directions of the thermochemical technologies are outlined. In addition, the economic analysis and life cycle assessment of thermochemical conversion technologies are evaluated. Finally, the conclusions are put forward.

Abstract 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.

Abstract We address the suppressing problem of smeared spectrum (SMSP) jamming from the mixed signal received by the radar. To this end, we resort to the time-frequency analysis algorithm and give the concrete expression formula of the short-time Fourier transform (STFT) of SMSP jamming. The proposed method is to estimate the parameters of the jamming signal based on the STFT result, and then the jamming signal is reconstructed and suppressed form the mixed signal. In order to perform effective interference even after the pulse compression process, the jamming-to-target ratio (JSR) is always positive. Based on this assumption, the estimation steps for different parameters of SMSP jamming are given, including the number of sub-waveforms, the amplitude, and the time delay. The influence of choosing different window is also considered in this paper. The estimated performance of this method is verified by Monte Carlo simulation, and the results show the effectiveness of the proposed method.

The feasibility of one-bit quantization in coast-ship bistatic high-frequency surface-wave radar (HFSWR) is mainly discussed in order to enjoy the advantages of simplicity, low cost and low power consumption. Our theoretical derivation shows that the received signal after one-bit quantization can be expressed as a linear combination of infinite components. These components include the original component and the high-order components that can be regarded as the interference terms. Then, the variation of the magnitude of each component with the signal-to-noise ratio (SNR) is given. To reduce the negative impact of these high-order components on the radar system, the detailed signal forms are analyzed. Furthermore, some comparative simulations of one-bit quantization and traditional high-precision quantization are provided to verify the correctness of our theoretical analysis. Both theories and simulation results indicate that the high-order components can be ignored in coast-ship bistatic HFSWR and that one-bit quantization is effective. Finally, real data analysis also validates the efficiency of one-bit quantization in practice.

A multistatic radar system can effectively improve the detection performance owing to its spatial diversity property. However, the detection performance will degrade when there exist multipaths. Time reversal has been proved to be able to transform the impact of the multipath into a favorable factor by matching propagation channels to achieve space-time focusing in a monostatic radar system. Therefore, we study the time reversal detection problem in a multistatic radar system with multipath environment in this paper. We divide the detection problems into two scenarios according to whether the channel response is known or not. In both scenarios, the time reversal detector and the conventional detector are derived respectively. Monte-Carlo experiments are used to examine the performance of the four detectors. The simulation results demonstrate that the time reversal detectors have a significant performance enhancement over the conventional detectors for the multistatic radar in a multipath environment, and more multipaths lead to better detection performance for the time reversal detectors. In addition, we also show that the detection probability improves with the increase of the number of radar transceivers.

Abstract Sparse frequency waveform is widely used to suppress the narrowband interferences. The performance that can be achieved with a single waveform is limited. The authors study the approach to design a pair of matching waveforms with low autocorrelation sidelobes and arbitrary frequency stopbands. Two algorithms based on the minimum mean square error (MMSE) between the actual waveform characteristics and the desired waveform characteristics are proposed to deal with this problem. The constrained optimization problem is solved by an alternating iteration method. Numerical experiments show that the proposed algorithms can provide better performance than the existing methods.

Abstract We propose a new array configuration composed of multi-scale scalar arrays and a single triangular spatially spread electromagnetic-vector-sensor (SS-EMVS) for high-accuracy two-dimensional (2D) direction-of-arrival (DOA) estimation. Two scalar arrays are placed along x-axis and y-axis, respectively, each array consists of two uniform linear arrays (ULAs), and these two ULAs have different inter-element spacings. In this manner, these two scalar arrays form a multi-scale L-shaped array. The two arms of this L-shaped scalar array are connected by a six-component SS-EMVS, which is composed of a spatially spread dipole-triad plus a spatially spread loop-triad. All the inter-element spacings in our proposed array can be larger than a half-wavelength of the incident source, thus to form a sparse array to mitigate the mutual coupling across antennas. In the proposed DOA estimation algorithm, we perform the vector-cross-product algorithm to the SS-EMVS to obtain a set of low-accuracy but unambiguous direction cosine estimation as a reference; we then impose estimation of signal parameters via rotation invariant technique (ESPRIT) algorithm to the two scalar arrays to get two sets of high-accuracy but cyclically ambiguous direction cosine estimations. Finally, the coarse estimation is used to disambiguate the fine but ambiguous estimations progressively and therefore a multiple-order disambiguation algorithm is developed. The proposed array enjoys the superiority of low redundancy and low mutual coupling. Moreover, the thresholds of the inter-sensor spacings utilized in the proposed array are also analyzed. Simulation results validate the performance of the proposed array geometry.

In low-altitude target situation, the multi-path signals cause the amplitude-phase distortion of direct signal from targets and degrade the performance of existing methods. Hence, in this paper, we propose a phase enhancement method for low-angle estimation using supervised deep neural network (DNN) to mitigate the phase distortion, thus to improve direction of arrival (DOA) estimation accuracy. The mapping relationship between the original phase difference distribution of the received signal and desired phase difference distribution is learned by DNN during training. The phase of test data is enhanced by trained DNN, and the enhanced phase is used for DOA estimation. We explain the significance of enhancing phase instead of amplitude by discussing the sensitivity of amplitude and phase on DOA estimation. Moreover, we prove the effectiveness and superiority of the proposed method by simulation experiments. The results demonstrate that the proposed technique has a better performance in terms of estimation error and goodness of fit (GoF) than the physics-driven DOA estimation methods and state-of-the-art methods including feature reversal and the support vector regression (SVR).

Nitrogen oxides released from the combustion of fossil fuels are one of the main air pollutants. Selective catalytic reduction technology is the most widely used nitrogen oxide removal technology in the industry. With the development of nanomaterials science, more and more novel nanomaterials are being used as catalysts for the selective reduction of nitrogen oxides. In recent years, metal-organic frameworks (MOFs), with large specific surface areas and abundant acid and metal sites, have been extensively studied in the selective catalytic reduction of nitrogen oxides. This review summarizes recent progress in monometallic MOFs, bimetallic MOFs, and MOF-derived catalysts for the selective catalytic reduction of nitrogen oxides and compares the reaction mechanisms of different catalysts. This article also suggests the advantages and disadvantages of MOF-based catalysts compared with traditional catalysts and points out promising research directions in this field.

In this paper, a multiscale sparse array, which is composed of spatially-spread electromagnetic-vector-sensors (SS-EMVSs), is proposed to estimate the direction-of-arrivals (DOA) and polarizations of multiple sources. The SS-EMVS is composed of three orthogonally oriented but spatially noncollocated dipoles to measure the electric field and three orthogonally oriented but spatially noncollocated loops to measure the magnetic field, simultaneously. In this paper, an array of SS-EMVSs is placed along the y-axis, and this sparse array is composed of two sub-arrays, i.e., the first n1 SS-EMVSs with inter-sensor spacing D1, and the last n2 SS-EMVSs with inter-sensor spacing D2, with D2 = mD1; m 1 is an integer and D1 is larger than a half-wavelength of the incident signal. Thereby, a multiscale sparse array is constructed, which is capable of providing high accuracy estimates of DOA and polarizations of multiple sources. The vector-cross-product algorithm is used to obtain the unambiguous but low-accuracy estimations of direction cosines, and the different inter-sensor spacings are used to estimate high-accuracy but ambiguous estimations of direction cosines. Following this, a multiscale disambiguation algorithm is developed to obtain highaccuracy and unambiguous estimations of direction cosines, thus the elevation angles, azimuth angles, as well as the polarization parameters of multiple sources. Simulation results verify the superior performance of the proposed multiscale SS-EMVS array.

In this paper, we propose a unified array geometry, dubbed generalized nested subarray (GNSA), for the underdetermined direction-of-arrival estimation. The GNSA is composed of multiple, identical subarrays, which can be a minimum redundancy array (MRA), a (super) nested array, a uniform linear array (ULA), or any other linear arrays with hole-free difference coarrays (DCAs). By properly design the spacings between subarrays, the resulting DCA of the GNSA can also be a hole-free (filled) ULA. When the subarray is an MRA and meanwhile its sensors' positions also follow an MRA configuration, a nested MRA (NMRA) is constructed. This NMRA can provide up to O(M2N2) degrees of freedom (DOFs) using only MN physical sensors. In order to fully utilize the increased DOF, we develop a new DOA estimation algorithm, which consists of a dimensional reduction matrix to exploit the data of all virtual elements, a Toeplitz matrix to decorrelate the equivalent coherent sources, and a root-MUSIC method to mitigate the computational workload. This new algorithm can achieve better DOA estimation performance than traditional spatial smoothing MUSIC algorithm with lower computational complexity. Numerical simulation results demonstrate the superiorities of the proposed array geometry in resolving more sources than sensors, DOA estimation performance, and the angular resolution.

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.

Sparse planar arrays, such as the billboard array, the open box array, and the two-dimensional nested array, have drawn lots of interest owing to their ability of two-dimensional angle estimation. Unfortunately, these arrays often suffer from mutual-coupling problems due to the large number of sensor pairs with small spacing d (usually equal to a half wavelength), which will degrade the performance of direction of arrival (DOA) estimation. Recently, the two-dimensional half-open box array and the hourglass array are proposed to reduce the mutual coupling. But both of them still have many sensor pairs with small spacing d, which implies that the reduction of mutual coupling is still limited. In this paper, we propose a new sparse planar array which has fewer number of sensor pairs with small spacing d. It is named as the thermos array because its shape seems like a thermos. Although the resulting difference coarray (DCA) of the thermos array is not hole-free, a large filled rectangular part in the DCA can be facilitated to perform spatial-smoothing-based DOA estimation. Moreover, it enjoys closed-form expressions for the sensor locations and the number of available degrees of freedom. Simulations show that the thermos array can achieve better DOA estimation performance than the hourglass array in the presence of mutual coupling, which indicates that our thermos array is more robust to the mutual-coupling array.

The heavily congested radio frequency environment severely limits the signal bandwidth of the high frequency surface wave radar (HFSWR). Based on the concept of multiple-input multiple-output (MIMO) radar, we propose a MIMO sparse frequency HFSWR system to synthesize an equivalent large bandwidth waveform in the congested HF band. The utilized spectrum of the proposed system is discontinuous and irregularly distributed between different transmitting sensors. We investigate the sparse frequency modulated continuous wave (FMCW) signal and the corresponding deramping based receiver and signal processor specially. A general processing framework is presented for the proposed system. The crucial step is the range-azimuth processing and the sparsity of the carrier frequency causes the two-dimensional periodogram to fail when applied here. Therefore, we introduce the iterative adaptive approach (IAA) in the range-azimuth imaging. Based on the initial 1D IAA algorithm, we propose a modified 2D IAA which particularly fits the deramping processing based range-azimuth model. The proposed processing framework for MIMO sparse frequency FMCW HFSWR with the modified 2D IAA applied is shown to have a high resolution and be able to provide an accurate and clear range-azimuth image which benefits the following detection process.

For improving the performance of multiple-target detection in a colocated multiple-input multiple-output (MIMO) radar system, a constant-modulus-waveform design method is presented in this paper. The proposed method consists of two steps: simultaneous multiple-transmit-beam design and constant-modulus-waveform design. In the first step, each transmit beam is controlled by an ideal orthogonal waveform and a weight vector. We optimized the weight vectors to maximize the detection probabilities of all targets or minimize the transmit power for the purpose of low intercept probability in the case of predefined worst detection probabilities. Various targets’ radar cross-section (RCS) fluctuation models were also considered in two optimization problems. Then, the optimal weight vectors multiplied by ideal orthogonal waveforms were a set of transmitted waveforms. However, those transmitted waveforms were not constant-modulus waveforms. In the second step, the transmitted waveforms obtained in the first step were mapped to constant-modulus waveforms by cyclic algorithm. Numerical examples are provided to show that the proposed constant-waveform design method could effectively achieve the desired transmit-beam pattern, and that the transmit-beam pattern could be adaptively adjusted according to prior information.

Discontinuous spectrum signal which has separate subbands distributed over a wide spectrum band is a solution to synthesize a wideband waveform in a highly congested spectrum environment. In this paper, we present a general range-velocity processing scheme for the discontinuous spectrum-frequency modulated continuous wave (DS-FMCW) signal specifically. In range domain, we propose a simple time rearrangement operation which converts the range transform problem of the DS-FMCW signal to a general spectral estimation problem of nonuniformly sampled data. Conventional periodogram results in a dirty range spectrum with high sidelobes which cannot be suppressed by traditional spectral weighting. In this paper, we introduce the iterative adaptive approach (IAA) in the estimation of the range spectrum. IAA is shown to have the ability to provide a clean range spectrum. On the other hand, the discontinuity of the signal spectrum has little impact on the velocity processing. However, with the range resolution improved, the influence of the target motion becomes nonnegligible. We present a velocity compensation strategy which includes the intersweep compensation and in-sweep compensation. Our processing scheme with the velocity compensation is shown to provide an accurate and clean range-velocity image which benefits the following detection process.