Anti-jamming solutions based on antenna arrays enhance the anti-jamming ability and the robustness of global navigation satellite system (GNSS) receiver remarkably. However, the performance of the receiver will deteriorate significantly in the overloaded interferences scenario. We define the overloaded interferences scenario as where the number of interferences is more than or equal to the number of antenna arrays elements. In this paper, the effect mechanism of interferences with different incident directions is found by studying the anti-jamming performance of the adaptive space filter. The theoretical analysis and conclusions, which are first validated through numerical examples, reveal the relationships between the optimal weight vector and the eigenvectors of the input signal autocorrelation matrix, the relationships between the interference cancellation ratio (ICR), the signal to interference and noise power ratio (SINR) of the adaptive space filter output and the number of interferences, the eigenvalues of the input signal autocorrelation matrix. In addition, two simulation experiments are utilized to further corroborate the theoretical findings through soft anti-jamming receiver. The simulation results match well with the theoretical analysis results, thus validating the effect mechanism of overloaded interferences. The simulation results show that, for a four elements circular array, the performance difference is up to 19 dB with different incident directions of interferences. Anti-jamming performance evaluation and jamming deployment optimization can obtain more accurate and efficient results by using the conclusions.
Polarization feature is one of the important features of radar targets, which has been used in many fields. In this paper, the grid models of some typical foreign moving targets are constructed on the simulation platform, such as glider, cruiser, fixed wing aircraft, and rotorcraft. The electromagnetic scattering characteristics of the moving platforms under the incidence of circular polarization waves are calculated. The typical polarization characteristics which the orthogonal and in-phase components have in the echoes are analyzed and proved. Based on the polarization scattering matrix (PSM) theory, from the point of view of the physical reproduction, the technical status quo that the existing technical approaches are difficult to realize the passive simulation of polarization characteristic of the target is summarized. To solve this problem, combined with the vector synthesis law, the realization mechanism of controllable polarization characteristic of target echoes is proposed, the analytical expressions of polarization control matrix and polarization ratio are deduced, and the controllability of polarization ratio feature in the case of circular polarization is verified by simulation calculation.
In airborne array synthetic aperture radar (SAR), the three-dimensional (3D) imaging performance and cross-track resolution depends on the length of the equivalent array. In this paper, Barker sequence criterion is used for sparse flight sampling of airborne array SAR, in order to obtain high cross-track resolution in as few times of flights as possible. Under each flight, the imaging algorithm of back projection (BP) and the data extraction method based on modified uniformly redundant arrays (MURAs) are utilized to obtain complex 3D image pairs. To solve the side-lobe noise in images, the interferometry between each image pair is implemented, and compressed sensing (CS) reconstruction is adopted in the frequency domain. Furthermore, to restore the geometrical relationship between each flight, the phase information corresponding to negative MURA is compensated on each single-pass image reconstructed by CS. Finally, by coherent accumulation of each complex image, the high resolution in cross-track direction is obtained. Simulations and experiments in X-band verify the availability.
To address the problem of building linear barrier coverage with the location restriction, an optimization method for deploying multistatic radars is proposed, where the location restriction splits the deployment line into two segments. By proving the characteristics of deployment patterns, an optimal deployment sequence consisting of multiple deployment patterns is proposed and exploited to cover each segment. The types and numbers of deployment patterns are determined by an algorithm that combines the integer linear programming (ILP) and exhaustive method (EM). In addition, to reduce the computation amount, a formula is introduced to calculate the upper threshold of receivers’ number in a deployment pattern. Furthermore, since the objective function is non-convex and non-analytic, the overall model is divided into two layers concerning two suboptimization problems. Subsequently, another algorithm that integrates the segments and layers is proposed to determine the deployment parameters, such as the minimum cost, parameters of the optimal deployment sequence, and the location of the split point. Simulation results demonstrate that the proposed method can effectively determine the optimal deployment parameters under the location restriction.
Phased array radar ’s measurements include two direction cosine and range measurements, which can be obtained in the direction cosine coordinates. State equation of the target is nonlinear with the measurements and in order to solve the nonlinear problem, debiased conversion measurements based target tracking with direction cosine and range measurements in direction cosine coordinates named DCMKF-PreDcos is proposed first in this paper, where the predicted information is introduced to calculate the converted measurement errors ’ statistical characteristics to eliminate the correlation between measurement noise and the converted measurement errors covariance. When range rate information can be obtained further, based on the above DCMKF-PreDcos ’ filtering result, the sequential filtering is adopted to process the additional range rate measurement and the DCMKF-PreDcos algorithm with extra range rate information is given. The predicted information is also introduced to calculate the involved statistical characteristics of converted measurements. The effectiveness of the proposed algorithms is shown in simulation results.
This study deals with the problem of mainlobe jamming suppression for rotated array radar. The interference becomes spatially nonstationary while the radar array rotates, which causes the mismatch between the weight and the snapshots and thus the loss of target signal to noise ratio (SNR) of pulse compression. In this paper, we explore the spatial divergence of interference sources and consider the rotated array radar anti-mainlobe jamming problem as a generalized rotated array mixed signal (RAMS) model firstly. Then the corresponding algorithm improved blind source separation (BSS) using the frequency domain of robust principal component analysis (FD-RPCA-BSS) is proposed based on the established rotating model. It can eliminate the influence of the rotating parts and address the problem of loss of SNR . Finally, the measured peak-to-average power ratio (PAPR) of each separated channel is performed to identify the target echo channel among the separated channels. Simulation results show that the proposed method is practically feasible and can suppress the mainlobe jamming with lower loss of SNR.
Introducing frequency agility into a distributed multiple-input multiple-output (MIMO) radar can significantly enhance its anti-jamming ability. However, it would cause the sidelobe pedestal problem in multi-target parameter estimation. Sparse recovery is an effective way to address this problem, but it cannot be directly utilized for multi-target parameter estimation in frequency-agile distributed MIMO radars due to spatial diversity. In this paper, we propose an algorithm for multi-target parameter estimation according to the signal model of frequency-agile distributed MIMO radars, by modifying the orthogonal matching pursuit (OMP) algorithm. The effectiveness of the proposed method is then verified by simulation results.
The paper designs a peripheral maximum gray difference (PMGD) image segmentation method, a connected-component labeling (CCL) algorithm based on dynamic run length (DRL), and a real-time implementation streaming processor for DRL-CCL. And it verifies the function and performance in space target monitoring scene by the carrying experiment of Tianzhou-3 cargo spacecraft (TZ-3). The PMGD image segmentation method can segment the image into highly discrete and simple point targets quickly, which reduces the generation of equivalences greatly and improves the real-time performance for DRL-CCL. Through parallel pipeline design, the storage of the streaming processor is optimized by 55% with no need for external memory, the logic is optimized by 60%, and the energy efficiency ratio is 12 times than that of the graphics processing unit, 62 times than that of the digital signal proccessing, and 147 times than that of personal computers. Analyzing the results of 8756 images completed on-orbit, the speed is up to 5.88 FPS and the target detection rate is 100%. Our algorithm and implementation method meet the requirements of lightweight, high real-time, strong robustness, full-time, and stable operation in space irradiation environment.
Constrained by complex imaging mechanism and extraordinary visual appearance, change detection with synthetic aperture radar (SAR) images has been a difficult research topic, especially in urban areas. Although existing studies have extended from bi-temporal data pair to multi-temporal datasets to derive more plentiful information, there are still two problems to be solved in practical applications. First, change indicators constructed from incoherent feature only cannot characterize the change objects accurately. Second, the results of pixel-level methods are usually presented in the form of the noisy binary map, making the spatial change not intuitive and the temporal change of a single pixel meaningless. In this study, we propose an unsupervised man-made objects change detection framework using both coherent and incoherent features derived from multi-temporal SAR images. The coefficients of variation in time-series incoherent features and the man-made object index (MOI) defined with coherent features are first combined to identify the initial change pixels. Afterwards, an improved spatiotemporal clustering algorithm is developed based on density-based spatial clustering of applications with noise (DBSCAN) and dynamic time warping (DTW), which can transform the initial results into noiseless object-level patches, and take the cluster center as a representative of the man-made object to determine the change pattern of each patch. An experiment with a stack of 10 TerraSAR-X images in Stripmap mode demonstrated that this method is effective in urban scenes and has the potential applicability to wide area change detection.
Ground-based interferometric synthetic aperture radar (GB-InSAR) can take deformation measurement with a high accuracy. Partition of the GB-InSAR deformation map benefits analyzing the deformation state of the monitoring scene better. Existing partition methods rely on labelled datasets or single deformation feature, and they cannot be effectively utilized in GB-InSAR applications. This paper proposes an improved partition method of the GB-InSAR deformation map based on dynamic time warping (DTW) and k-means. The DTW similarities between a reference point and all the measurement points are calculated based on their time-series deformations. Then the DTW similarity and cumulative deformation are taken as two partition features. With the k-means algorithm and the score based on multi evaluation indexes, a deformation map can be partitioned into an appropriate number of classes. Experimental datasets of West Copper Mine are processed to validate the effectiveness of the proposed method, whose measurement points are divided into seven classes with a score of 0.315 1.
With the continuous improvement of radar intelligence, it is difficult for traditional countermeasures to achieve ideal results. In order to deal with complex, changeable, and unknown threat signals in the complex electromagnetic environment, a waveform intelligent optimization model based on intelligent optimization algorithm is proposed. By virtue of the universality and fast running speed of the intelligent optimization algorithm, the model can optimize the parameters used to synthesize the countermeasure waveform according to different external signals, so as to improve the countermeasure performance. Genetic algorithm (GA) and particle swarm optimization (PSO) are used to simulate the intelligent optimization of interrupted-sampling and phase-modulation repeater waveform. The experimental results under different radar signal conditions show that the scheme is feasible. The performance comparison between the algorithms and some problems in the experimental results also provide a certain reference for the follow-up work.
With the rapid development of digital and intelligent information systems, display of radar situation interface has become an important challenge in the field of human-computer interaction. We propose a method for the optimization of radar situation interface from error-cognition through the mapping of information characteristics. A mapping method of matrix description is adopted to analyze the association properties between error-cognition sets and design information sets. Based on the mapping relationship between the domain of error-cognition and the domain of design information, a cross-correlational analysis is carried out between error-cognition and design information. We obtain the relationship matrix between the error-cognition of correlation between design information and the degree of importance among design information. Taking the task interface of a warfare navigation display as an example, error factors and the features of design information are extracted. Based on the results, we also propose an optimization design scheme for the radar situation interface.
In recent years, ground-based micro-deformation monitoring radar has attracted much attention due to its excellent monitoring capability. By controlling the repeated campaigns of the radar antenna on a fixed track, ground-based micro-deformation monitoring radar can accomplish repeat-pass interferometry without a space baseline and thus obtain high-precision deformation data of a large scene at one time. However, it is difficult to guarantee absolute stable installation position in every campaign. If the installation position is unstable, the stability of the radar track will be affected randomly, resulting in time-varying baseline error. In this study, a correction method for this error is developed by analyzing the error distribution law while the spatial baseline is unknown. In practice, the error data are first identified by frequency components, then the data of each one-dimensional array (in azimuth direction or range direction) are grouped based on numerical distribution period, and finally the error is corrected by the nonlinear model established with each group. This method is verified with measured data from a slope in southern China, and the results show that the method can effectively correct the time-varying baseline error caused by rail instability and effectively improve the monitoring data accuracy of ground-based micro-deformation radar in short term and long term.
This paper concentrates on super-resolution imaging of the ship target under the sparse aperture situation. Firstly, a multi-static configuration is utilized to solve the coherent processing interval (CPI) problem caused by the slow-speed motion of ship targets. Then, we realize signal restoration and image reconstruction with the alternating direction method of multipliers (ADMM). Furthermore, we adopt the interferometric technique to produce the three-dimensional (3D) images of ship targets, namely interferometric inverse synthetic aperture radar (InISAR) imaging. Experiments based on the simulated data are utilized to verify the validity of the proposed method.
Interference suppression is a challenge for radar researchers, especially when mainlobe and sidelobe interference coexist. We present a comprehensive anti-interference approach based on a cognitive bistatic airborne radar. The risk of interception is reduced by lowering the launch energy of the radar transmitting terminal in the direction of interference; mainlobe and sidelobe interferences are suppressed via cooperation between the two radars. The interference received by a single radar is extracted from the overall radar signal using multiple signal classification (MUSIC), and the interference is cross-located using two different azimuthal angles. Neural networks allowing good, non-linear non-parametric approximations are used to predict the location of interference, and this information is then used to preset the transmitting notch antenna to reduce the likelihood of interception. To simultaneously suppress mainlobe and sidelobe interferences, a blocking matrix is used to mask mainlobe interference based on azimuthal information, and an adaptive process is used to suppress sidelobe interference. Mainlobe interference is eliminated using the data received by the two radars. Simulation verifies the performance of the model.
In order to solve the problem that the traditional space jamming countermeasure cannot deal with the mainlobe self-protecting jammings, a polarization-space joint mainlobe jamming countermeasure technique based on divided dimensions is proposed. Specifically, the digital beam of each row and column is firstly formed by using dual polarization digital receiving in multi-channel. Then, the polarization-space joint cancellation in both azimuth and elevation dimensions is carried out based on the polarization-space joint difference between the target echo and the jamming, as well as the divided dimension feature of the row and column beams. Finally, the sum and difference beams of the full array in the elevation or azimuth dimension are formed by the beams after jamming cancelling, and the monopulse angle measurement is further employed to obtain target angles. The effectiveness of the proposed technique is verified by simulations, indicating that the self-protecting jamming and multiple mainlobe following jammings can be both cancelled simultaneously with the angle measurement unchanged.
It is always a challenging issue for radar systems to estimate the height of a low-angle target in the multipath propagation environment. The highly deterministic maximum likelihood estimator has a high accuracy, but the errors of the ground reflection coefficient and the reflecting surface height have serious influence on the method. In this paper, a robust estimation method with less computation burden is proposed based on the compound reflection coefficient multipath model for low-angle targets. The compound reflection coefficient is estimated from the received data of the array and then a one-dimension generalized steering vector is constructed to estimate the target height. The algorithm is robust to the reflecting surface height error and the ground reflection coefficient error. Finally, the experiment and simulation results demonstrate the validity of the proposed method.
This paper proposes a method to improve the spurious-free dynamic ranges (SFDRs) of 1-bit sampled signals greatly, which is very beneficial to multi-tone signals detection. Firstly, the relationship between the fundamental component and the third harmonic component of 1-bit sampled signals is analyzed for determining four contiguous special frequency bands, which do not contain any third harmonics inside and cover 77.8% of the whole Nyquist sampling frequency band. Then, we present a special 4-channel monobit receiver model, where appropriate filter banks are used to obtain four desired pass bands before 1-bit quantization and each channel can sample and process sampled data independently to achieve a good instantaneous dynamic range without sacrificing the real-time performance or computing resources. The simulation results show that the proposed method effectively eliminates the effect of the most harmonics on SFDRs and the mean SFDR is increased to to 20 dB. Besides, the multi-signals simulation results indicate that the maximum amplitude separation (dynamic range) of two signals in each channel is 12 dB while the proposed monobit receiver can deal with up to eight simultaneous arrival signals. In general, the designing method proposed in this paper has a potential engineering value.
This paper dwells upon optimizing the azimuth sampling interval of digital surface maps used to model radar ground clutter. The resulting equations can be used to find the digital map sampling interval for the required calculation error and modeled power of the simulated signal, which determines the resulting distribution of backscatter intensity. The paper further showcases how the sampling interval could be increased by preprocessing the map.
Because of the range-angle dependency in random log frequency diverse array (RD-log-FDA) radar, a method for designing beamspace transformation matrix in angle and range based on the receive signal has been proposed. It is demonstrated that the designed beamspace transformation matrix basically meets the requirements of beam gain. However, there are some problems in the transformation matrix designed, such as unstable beam gain and high sidelobe. Hence, we propose an optimization method by adjusting array element spacing and random number in frequency offset to get the optimum beam gain. Therefore, particle swarm optimization (PSO) is used to find the optimal solution. The beam gain comparison before and after the optimization is obtained by simulation, and the results show that the optimized array after beamspace preprocessing has more stable beam gain, lower sidelobe, and higher resolution in parameter estimation. In conclusion, the RD-log-FDA is capable of forming desired beam gain in angle and distance through beamspace preprocessing, and suppressing interference signals in other areas.
Monopulse radar is widely used in military. Jamming monopulse radar has always been a research hotspot in electronic warfare (EW). Cross-eye jamming has always been considered as the most effective measures to jam with monopulse radar. In this paper, we propose a multi-group three-tuple cross-eye jamming structure where each group contains three antenna elements with a definite phase and an amplitude relationship. Then, based on the principle of monopulse angle measurement, the error angle is deduced theoretically. Simulations show that such a multi-group three-tuple cross-eye jamming structure performs better than the multi-element cross-eye jamming structure previously proposed, and the analysis of the centroid shows that the centroid of the structure proposed in this paper is more widely distributed in space.
This paper presents a novel method for fast calculation of radar echo in near-field regions after the equivalent source has been computed by method of moments (MoM). An easy-to-access near-field database (NFDB) is established, which is built on the auxiliary tetrahedral meshes surrounding the near-field regions of interest. The near-fields calculation (NFC) of arbitrary observation points can be expressed explicitly via the NFDB. An efficient matrix compression scheme named random sampling-based butterfly factorization (RS-BF) is proposed to speed up the construction of NFDB. With this approach, each group of O(N) elements in the database can be calculated through one fast matrix-vector multiplication operation that has a computational complexity below O(Nlog2N). The proposed method can avoid time-consuming point-by-point NFC of the traditional methods. Several numerical examples are presented to demonstrate the accuracy and efficiency of this method. In particular, the echo simulation of a missile-target encounter exam-ple is presented to illustrate its capability for practical applications.
This paper deals with subspace detection for range-spread target in non-homogeneous clutter with unknown covariance matrix where structured interference is presented in the received data. Through exploiting the persymmetry of the clutter covariance matrix, we propose two adaptive target detectors, which are referred to as persymmetric subspace Rao to suppress interference and persymmetric subspace Wald to suppress interference (“PS-Rao-I” and “PS-Wald-I”), respectively. The persymmetry-based design brings in the advantage of easy implementation for small training sample support. The signal flow analysis of the two detectors shows that the PS-Rao-I rejects interference and integrates signals successively through separated matrix projection, while the PS-Wald-I jointly achieves interference elimination and signal combination via oblique projection. In addition, both detectors are shown to be constant false alarm rate detectors, significantly improving the detection performance with other competing detectors under the condition of limited training.
Collocated multiple input multiple output (MIMO) radar, which has agile multi-beam working mode, can offer enhanced multiple targets tracking (MTT) ability. In detail, it can illuminate different targets simultaneously with multi-beam or one wide beam among multi-beam, providing greater degree of freedom in system resource control. An adaptive time-space resource and waveform control optimization model for the collocated MIMO radar with simultaneous multi-beam is proposed in this paper. The aim of the proposed scheme is to improve the overall tracking accuracy and meanwhile minimize the resource consumption under the guarantee of effective targets detection. A resource and waveform control algorithm which integrates the genetic algorithm (GA) is proposed to solve the optimization problem. The optimal transmitting waveform parameters, system sampling period, sub-array number, binary radar tracking parameter $ \chi _i\left( {{t_k}} \right) $ , transmitting energy and multi-beam direction vector combination are chosen adaptively, where the first one realizes the waveform control and the latter five realize the time-space resource allocation. Simulation results demonstrate the effectiveness of the proposed control method.
The spaceborne synthetic aperture radar (SAR) sparse flight 3-D imaging technology through multiple observations of the cross-track direction is designed to form the cross-track equivalent aperture, and achieve the third dimensionality recognition. In this paper, combined with the actual triple star orbits, a sparse flight spaceborne SAR 3-D imaging method based on the sparse spectrum of interferometry and the principal component analysis (PCA) is presented. Firstly, interferometric processing is utilized to reach an effective sparse representation of radar images in the frequency domain. Secondly, as a method with simple principle and fast calculation, the PCA is introduced to extract the main features of the image spectrum according to its principal characteristics. Finally, the 3-D image can be obtained by inverse transformation of the reconstructed spectrum by the PCA. The simulation results of 4.84 km equivalent cross-track aperture and corresponding 1.78 m cross-track resolution verify the effective suppression of this method on high-frequency sidelobe noise introduced by sparse flight with a sparsity of 49% and random noise introduced by the receiver. Meanwhile, due to the influence of orbit distribution of the actual triple star orbits, the simulation results of the sparse flight with the 7-bit Barker code orbits are given as a comparison and reference to illuminate the significance of orbit distribution for this reconstruction results. This method has prospects for sparse flight 3-D imaging in high latitude areas for its short revisit period.
Recent advances in electronics have increased the complexity of radar signal modulation. The quasi-linear frequency modulation (quasi-LFM) radar waveforms (LFM, Frank code, P1?P4 code) have similar time-frequency distributions, and it is difficult to identify such signals using traditional time-frequency analysis methods. To solve this problem, this paper proposes an algorithm for automatic recognition of quasi-LFM radar waveforms based on fractional Fourier transform and time-frequency analysis. First of all, fractional Fourier transform and the Wigner-Ville distribution (WVD) are used to determine the number of main ridgelines and the tilt angle of the target component in WVD. Next, the standard deviation of the target component's width in the signal's WVD is calculated. Finally, an assembled classifier using neural network is built to recognize different waveforms by automatically combining the three features. Simulation results show that the overall recognition rate of the proposed algorithm reaches 94.17% under 0 dB. When the training data set and the test data set are mixed with noise, the recognition rate reaches 89.93%. The best recognition accuracy is achieved when the size of the training set is taken as 400. The algorithm complexity can meet the requirements of real-time recognition.
An adaptive dwell scheduling algorithm for phased array radar (PAR) is proposed in this paper. The concept of online dynamic template is introduced, based on which a general pulse interleaving technique for PAR is put forward. The pulse interleaving condition of the novel pulse interleaving is more intuitive and general. The traditional adaptive dwell scheduling algorithm combined with the general novel pulse interleaving technique results in the online adaptive dwell scheduling based on dynamic template for PAR is given. The proposed algorithm is suitable for radar tasks with multiple pulse repetition intervals (PRIs), which can be utilized in the actual radar system. For the purpose of further improving the scheduling efficiency, an efficient version is proposed. Simulation results demonstrate the effectiveness of the proposed algorithm and the efficient one. The proposed efficient algorithm can improve the time utilization ratio (TUR) by 9%, the hit value ratio (HVR) by 3.5%, and reduce the task drop ratio (TDR) by 6% in comparison with existing dwell scheduling algorithms considering pulse interleaving in PAR and the proposed efficient one.
A miniaturized periodic element for constructing bandpass frequency selective surface (FSS) independent of incident angles and polarizations is presented. An interdigital resonator (IR) with one extending finger to connect the two separate parts of the interdigital capacitor is explored to achieve parallel resonance. The equivalent circuit model (ECM) and electric field distributions are introduced to explain frequency performance of FSS. The whole structure has only one layer and possesses a low profile (a thickness of 0.001 5 $\lambda $ , where $\lambda $ represents the resonant wavelength in free space) as well as a small size (0.03 $\lambda $ ×0.03 $\lambda $ ). This FSS performs as a spatial bandpass filter which exhibits a great angular stability with incident angles ranging from 0° to 80° for both transverse electric (TE) and transverse magnetic (TM) polarizations. As an example, a prototype of one proposed FSS is fabricated and tested. The measured results show a good angular stability.
Modern radar signals mostly use low probability of intercept (LPI) waveforms, which have short pulses in the time domain, multicomponent properties, frequency hopping, combined modulation waveforms and other characteristics, making the detection and estimation of LPI radar signals extremely difficult, and leading to highly required significant research on perception technology in the battlefield environment. This paper proposes a visibility graphs (VG)-based multicomponent signals detection method and a modulation waveforms parameter estimation algorithm based on the time-frequency representation (TFR). On the one hand, the frequency domain VG is used to set the dynamic threshold for detecting the multicomponent LPI radar waveforms. On the other hand, the signal is projected into the time and frequency domains by the TFR method for estimating its symbol width and instantaneous frequency (IF). Simulation performance shows that, compared with the most advanced methods, the algorithm proposed in this paper has a valuable advantage. Meanwhile, the calculation cost of the algorithm is quite low, and it is achievable in the future battlefield.
Adaptive digital self-interference cancellation (ADSIC) is a significant method to suppress self-interference and improve the performance of the linear frequency modulated continuous wave (LFMCW) radar. Due to efficient implementation structure, the conventional method based on least mean square (LMS) is widely used, but its performance is not sufficient for LFMCW radar. To achieve a better self-interference cancellation (SIC) result and more optimal radar performance, we present an ADSIC method based on fractional order LMS (FOLMS), which utilizes the multi-path cancellation structure and adaptively updates the weight coefficients of the cancellation system. First, we derive the iterative expression of the weight coefficients by using the fractional order derivative and short-term memory principle. Then, to solve the problem that it is difficult to select the parameters of the proposed method due to the non-stationary characteristics of radar transmitted signals, we construct the performance evaluation model of LFMCW radar, and analyze the relationship between the mean square deviation and the parameters of FOLMS. Finally, the theoretical analysis and simulation results show that the proposed method has a better SIC performance than the conventional methods.
