Search

The interrupted-sampling repeater jamming (ISRJ) is a kind of intrapulse coherent deception jamming, it produces false-target peaks, which obfuscate the real target detection and tracking. In this article, a novel recognition method is proposed to identify the false-target peaks caused by ISRJ. The proposed method is realized via the integration decomposition of pulse compression, and the intermediate data in pulse compression are extracted to identify the false-target peaks. Due to the time-sharing transmit–receive antenna of the jammer, the jamming signal is short or discontinuous pieces compared with the real echo. This intrinsic property provides important feature for jamming identification. In the proposed method, a variance value is generated from the intermediate data, to evaluate the temporal energy distribution evenness of the signal component corresponding to each target peak. Real target echo corresponds to small variance whereas the jamming signal corresponds to large variance. In this way, false-target peaks caused by jamming signal can be identified efficiently. Besides, the computational cost of the proposed method is low, thus, it is suitable for real-time applications in practical radar systems. Numerical experiments under different jamming parameters demonstrate the promising performance of the proposed false-target recognition method. Moreover, Monte Carlo simulations under different SNR levels verify the reliable classification capability of the proposed method. In summary, this article provides a new perspective for ISRJ identification, and it is also the proof-of-concept example for other potential applications concerning integration decomposition.

StochAstic Recursive grAdient algoritHm (SARAH), originally proposed for convex optimization and also proven to be effective for general nonconvex optimization, has received great attention because of its simple recursive framework for updating stochastic gradient estimates. The performance of SARAH significantly depends on the choice of the step size sequence. However, SARAH and its variants often manually select a best-tuned step size, which is time consuming in practice. Motivated by this gap, we propose a variant of the Barzilai-Borwein (BB) method, referred to as the Random Barzilai-Borwein (RBB) method, to determine the step size for SARAH in the mini-batch setting, leading to a new SARAH method: MB-SARAH-RBB. We prove that MB-SARAH-RBB converges linearly in expectation for strongly convex objective functions. Moreover, we analyze the gradient complexity of MB-SARAH-RBB and show that it is better than the original method. To further confirm the efficacy of the RBB method, we propose the MB-SARAH+-RBB method, by incorporating it into the MB-SARAH + method. Numerical experiments on standard data sets indicate that our proposed methods outperform or match state-of-the-art algorithms.

To detect highly maneuvering radar targets in low signal-to-noise ratio conditions, a hybrid long-time integration method is proposed, which combines Radon-Fourier Transform (RFT), Dynamic Programming (DP), and Binary Integration (BI), named RFT-DP-BI. A Markov model with unified range-velocity quantification is formulated to describe the maneuvering target’s motion. Based on this model, long-time hybrid integration is performed. Firstly, the whole integration time is divided into multiple time segments and coherent integration is performed in each segment via RFT. Secondly, non-coherent integration is performed in all segments via DP. Thirdly, 2/4 binary integration is performed to further improve the detection performance. Finally, the detection results are exported together with target range and velocity trajectories. The proposed method can perform the long-time integration of highly maneuvering targets with arbitrary forms of motion. Additionally, it has a low computational cost that is linear to the integration time. Both simulated and real radar data demonstrate that it offers good detection and estimation performances.

Parameter estimation of chirp signals plays an important role in the field of radar countermeasures. Compressed sensing (CS) based sub-Nyquist sampling and parameter estimation methods alleviates the pressure on hardware systems to acquire and process chirp signals with large time-bandwidths. In this paper, a framework based on the fractional Fourier transform (FrFT) and alternating direction method of multipliers network (ADMM-Net) is proposed to realize chirp signal parameter estimation under sub-Nyquist sampling. The whole framework is composed of multiple parallel ADMM-Nets, where each ADMM-Net is defined over a data flow graph, which is derived from the iterative procedures of the ADMM algorithm for optimizing a CS-based $p$ -order FrFT spectral estimation model. The chirp rate and central frequency of chirp signals are obtained through a two-dimensional search on the spectrum image output by the network group. Experiments demonstrate that the proposed ADMM-Net-based method can achieve higher estimation accuracy and computational efficiency at lower signal-to-noise ratios and sampling ratios than traditional CS methods. We also demonstrate that the proposed ADMM-Net-based framework has strong generalization ability for multi-component chirp signals. Furthermore, we further generalize ADMM-Net to GADMM-Net, in which the activation function is data-driven instead of model-driven. Experiments demonstrate that GADMM-Net significantly improves on the basic ADMM-Net and achieves higher spectral resolution with faster computation speed.

Intra-pulse modulation phase calibration is necessary in inverse synthetic aperture radar (ISAR) imaging of high-speed targets. Traditional intra-pulse phase error compensation strategies rarely handle the high-order and slow-time-variant phase components induced during the coherent processing interval. In this paper, a novel intra-pulse modulation phase calibration with a two-dimensional (2-D) parametric phase model is proposed. It models the intra-pulse phase errors as a 2-D time-variant polynomial with accommodation of both fast-time and slow-time modulation. Entropy minimization of high-resolution range profiles (HRRPs) is developed to retrieve the phase error parameters. Improved coordinate descent optimization solver is established by Levenberg-Marquardt (LM) algorithm in order to find the global optimum of entropy efficiently. Comparative experiments using both simulated and real measured data are performed to demonstrate the enhancements of the proposed algorithm.

This paper addresses the problem of estimating the parameters of constant-amplitude chirp signals that have single or multiple components and are embedded in noise. Chirp signals are widely employed in applications such as radar and telecommunications, and it is a key task in countermeasure techniques to estimate their parameters without prior information. Hence, a parameter estimation processor based on a complex-valued deep neural network (CV DNN) is proposed to perform this task efficiently. The CV DNN, which is designed for regression, consists of a function fitter and a predictor. The function fitter acts like a eigenfunction mapping: it maps the one-dimensional input into a two-dimensional feature map suitable for subsequent network learning. As a special feature extraction tool, the predictor extracts local features from the feature map and estimates parameters. Simulation results indicate that the CV DNN outperforms conventional processors. Moreover, it is more accurate than the Wigner-Hough transform while being several orders of magnitude faster, which will enable real-time signal processing with fewer computational resources. Furthermore, we demonstrate that the CV DNN shows strong robustness to changes in modulation parameters and the number of components of a chirp signal. This study shows the advantages of deep learning systems for signal parameter estimation.

In the inverse synthetic aperture radar imaging field, the increasing demand of characterizing small targets drives researchers to seek a higher resolution. One attractive way to improve the resolution is sparse subband imaging. In this paper, we propose a novel framework for sparse subband imaging. First, the fine imaging of each subband is performed, through which the high-order phase errors between subband data are compensated. Then, the Keystone transform is performed to unify the image scales of subbands, and sparse subband returns are coherently processed. Finally, the cross-range compressed subband data are interpolated based on the autoregressive model and then used to reconstruct the high-resolution range profile (HRRP) via the smoothed $\ell^{0}$ algorithm. In the proposed framework, the cross-range compression, which is performed before HRRP reconstruction, enhances the sparsity of the returns and achieves a considerable SNR gain. Consequently, it paves the way for HRRP reconstruction. Furthermore, the proposed framework needs no filling of the band gap between subbands, and it is robust against the band gap. The real data experimental results demonstrate this framework’s ability to achieve a high-resolution fusion image accurately, even under low SNR conditions.

Our work aims to address the problem of estimating the parameters of constant-amplitude, time-unsynchronized linear frequency-modulated (LFM) signals that have single or multiple components, which is a crucial task in electronic countermeasure techniques. A method for estimating the parameters, center frequency f0, and chirp rate μ of an LFM signal is proposed; the method is referred to as the Wigner–Ville distribution complex-valued convolutional neural network (WVD-CV-CNN). The method can be regarded as an application of neural networks for extracting parameter features from the signal spectrogram, wherein the CV-CNN is the main body of the network, which takes a complex-valued WVD matrix as the input and outputs several sets of estimated parameters. A performance analysis shows that the estimation accuracy and computational efficiency of the proposed method are significantly improved compared with those of the conventional methods. Further, the proposed method shows strong robustness to changes in modulation parameters. We apply the CV-CNN to other spectrograms and prove compatibility of the WVD and CV-CNN by comparison. We also demonstrate that the estimation accuracy of the proposed method is robust against cross interference on the WVD. Our study shows the advantages of using deep learning systems in signal parameter estimation.

This paper considers the coherent integration problem for moving target detection using frequency agile (FA) radar, involving range cell migration (RCM) and the nonuniform phase fluctuations among different pulses caused by range-agile frequency (R-AF) coupling and velocity-time-agile frequency (V-T-AF) coupling. After the analysis of the term corresponding to the phase fluctuation caused by V-T-AF coupling, the term can be regarded as related to an equivalent non-uniform slow time, and nonuniform fast Fourier transform (NUFFT) could be the solution. So a fast coherent integration method combining Radon Fourier transform (RFT) and NUFFT based on low-rank approximation, i.e., Radon-NUFFT, is proposed. In this method, the RCM is solved by Radon algorithm via target trajectory searching, the non-uniform phase fluctuation caused by R-AF coupling is compensated by constructing a compensation item corresponding to the range and agile frequency. In addition, the compensation of the non-uniform phase fluctuation caused by V-T-AF coupling is converted into a problem of spectral analysis of non-uniform sampling complex-valued signal, which is solved by the NUFFT based on low rank approximation. Compared with the existing methods, the proposed method can realize the coherent integration for FA radar accurately and quickly. The effectiveness of the proposed method is verified by simulation experiments.

Synchronous Nyquist folding receiver (SNYFR) is an improved novel broadband receiver architecture that is possible to implement in the engineering. The output signal of SNYFR is usually a hybrid modulated signal, so corresponding signal detection methods need to be investigated, which is the premise of precise parameter estimation. In this paper, a signal detection method based on recursive autocorrelation and mn method for SNYFR is proposed. First, autocorrelation with a fixed length for SNYFR output signal is acquired. Then, in order to determine the existence of the signal, constant false alarm rate (CFAR) detector is introduced into the method to generate an adaptive threshold. Finally, the position where the signal exists is determined by mn method and the signal data are sent to back-end processor for parameter estimation. The proposed algorithm can not only adapt to low signal-to-noise ratio (SNR), but also has a high measurement accuracy with low computational complexity. The simulation demonstrates the viability and validity of the proposed method.

The problem of direction-of-arrival (DOA) estimation is investigated for co-prime linear array, where the co-prime linear array incorporates two uniform linear arrays (ULAs) with adjacent spacing larger than half wavelength. In this paper, we propose an ESPRIT-like DOA estimation method based on gradient descent for co-prime linear array. First, we decompose the entire co-prime array into several subarrays regarding each sensor as a subarray and derive the property of rotational invariance between manifold matrixs of any two subarrays. Then, this paper uses the gradient descent to iterative search the rotational invariance matrix and obtain direction information of the incident signals. The proposed method can achieve full DOFs, and there is no grid search in the range of directions. Simulation results corroborate the effectiveness and superiority of the proposed method, compared with the total spectral search method (TSS) and MUSIC for ULA with the same sensor number.

In most large field of view (FOV) observations, the distortion problem is inevitably and significantly more serious than in small FOV ones. In the circumstances, many traditional star ident...

In some multiźtarget tracking applications, appearing targets are suitably modeled as spawning from existing targets. However, in the original cardinalized probability hypothesis density (CPHD) filter, this type of model is not included; instead appearing targets are modeled by spontaneous birth only. Recently, two versions of CPHD filter modeling spawning targets have already been developed, but these two methods are tractable only when the spawning targets cardinality distribution is restricted to be the Bernoulli distribution, the Poisson distribution or the Zero-inflated Poisson distribution. In this paper, we derive a generalized CPHD filter which is tractable and has no constraint of the cardinality distribution of the spawning targets, that is to say, the spawning targets cardinality distribution can be arbitrary. The derivation is based on the finite set statistics (FISST) and the Faa di bruno's determinant formula. Moreover, how this generalized CPHD filter degrades into the two previous versions is also given in this paper. The resulting filter is different from the original CPHD filter in two aspects: first, the prediction equation of the PHD function changes to be identical with that of the probability hypothesis density (PHD) filter; and second, the cardinality distribution prediction equation is now an expression including the cardinality distribution information of the spawning targets. Simulation results show that the proposed method can response much faster than the original CPHD filter in target number estimate when spawning targets appear, and has a much smaller cardinality estimate variance than the PHD filter and the original CPHD filter. A comparison considering the optimal sub-pattern assignment (OSPA) metric also demonstrates the good performance of the proposed method. The general explicit cardinality prediction equation for the CPHD filter modeling spawning targets is derived.The derivation is based on the famous Faa di bruno's determinant formula.A tractable recursion computation technique of the general cardinality prediction equation is proposed.

Radar target detection probability will decrease as the target echo signal-to-noise ratio (SNR) decreases, which has an adverse influence on the result of multi-target tracking. The performances of standard multi-target tracking algorithms degrade significantly under low detection probability in practice, especially when continuous miss detection occurs. Based on sequential Monte Carlo implementation of Probability Hypothesis Density (PHD) filter, this paper proposes a heuristic method called the Refined PHD (R-PHD) filter to improve multi-target tracking performance under low detection probability. In detail, this paper defines a survival probability which is dependent on target state, and labels individual extracted targets and corresponding particles. When miss detection occurs due to low detection probability, posterior particle weights will be revised according to the prediction step. Finally, we transform the target confirmation problem into a hypothesis test problem, and utilize sequential probability ratio test to distinguish real targets and false alarms in real time. Computer simulations with respect to different detection probabilities, average numbers of false alarms and continuous miss detection durations are provided to corroborate the superiority of the proposed method, compared with standard PHD filter, Cardinalized PHD (CPHD) filter and Cardinality Balanced Multi-target Multi-Bernoulli (CBMeMBer) filter.

The Nyquist folding receiver (NYFR) can achieve a high-probability interception of an ultra-wideband (UWB) signal with fewer devices, while the output of the NYFR is converted into a hybrid modulated signal of the local oscillator (LO) and the received signal, which requires the matching parameter estimation methods. The linear frequency modulation (LFM) signal is a typical low probability of intercept (LPI) radar signal. In this paper, an estimation method of both the Nyquist Zone (NZ) index and the chirp rate for the LFM signal intercepted by NYFR was proposed. First, according to the time-frequency characteristics of the LFM signal, the accurate NZ and the rough chirp rate was estimated based on least squares (LS) and random sample consensus (RANSAC). Then, the information of the LO was removed from the hybrid modulated signal by the known NZ, and the precise chirp rate was obtained by using the fractional Fourier transform (FrFT). Moreover, a fast search method of FrFT optimal order was presented, which could obviously reduce the computational complexity. The simulation demonstrated that the proposed method could precisely estimate the parameters of the hybrid modulated output signal of the NYFR.

Direction-of-arrival (DOA) estimation in multipath environment is an important issue for passive bistatic radar (PBR) using frequency agile phased array VHF radar as illuminator of opportunity. Under such scenario, the main focus of this paper is to cope with the closely spaced uncorrelated and coherent signals in low signal-to-noise ratio and limited snapshots. Making full use of the characteristics of moduli of eigenvalues, the DOAs of the uncorrelated signals are firstly estimated. Afterwards, their contributions are eliminated by means of spatial difference technique. Finally, in order to improve resolution and accuracy DOA estimation of remaining coherent signals while avoiding the cross-terms effect, a new beamforming solution based iterative adaptive approach (IAA) is proposed to deal with a reconstructed covariance matrix. The proposed method combines the advantages of both spatial difference method and the IAA algorithm while avoiding their shortcomings. Simulation results validate its effectiveness; meanwhile, the good performances of the proposed method in terms of resolution probability, detection probability, and estimation accuracy are demonstrated by comparison with the existing methods.

A novel polarimetric calibration scheme on the basis of lexicographic matrix decomposition is proposed, and a new polarimetric active radar calibrator (PARC) with two independently rotatable antennas is designed to obtain the lexicographic matrices. Thus, the proposed method is realizable and can operate with the lexicographic target vectors instead of the polarization scattering matrix (PSM). The base elements of the lexicographic target vectors are the vectors corresponding to the new PARC with the antennas at different orientations. Moreover, the elements of the vector are just the elements of the PSM. Hence, the error coefficients corresponding to a polarimetric measurement system are directly achieved, and the polarimetric calibration is simple and accurate. Experimental results confirm the superiority of the presented strategy. A 35-dB improvement in the system cross-polarization isolation is obtained.

The effect of gain-phase perturbations and mutual coupling significantly degrades the performance of digital array radar (DAR). This paper investigates array calibration problems in the scenario where the true locations of auxiliary sources deviate from nominal values but the angle intervals are known. A practical algorithm is proposed to jointly calibrate gain-phase errors and mutual coupling errors. Firstly, a simplified model of the distortion matrix is developed based on its special structure in uniform linear array (ULA). Then the model is employed to derive the precise locations of the auxiliary sources by one-dimension search. Finally, the least-squares estimation of the distortion matrix is obtained. The algorithm has the potential of achieving considerable improvement in calibration accuracy due to the reduction of unknown parameters. In addition, the algorithm is feasible for practical applications, since it requires only one auxiliary source with the help of rotation platforms. Simulation results demonstrate the validity, robustness and high performance of the proposed algorithm. Experiments were carried out using an S-band DAR test-bed. The results of measured data show that the proposed algorithm is practical and effective in application.

Bistatic inverse synthetic aperture radar (BiISAR) imaging and interferometric inverse synthetic aperture radar (InISAR) imaging can provide complementary information to monostatic inverse synthetic aperture radar (MoISAR) imaging. Time synchronization is a requirement for both systems. This paper focuses on the problem of the time synchronization in the BiISAR and InISAR imaging with a polynomial time synchronization error model (PTSEM) in theory. The effects of the time synchronization errors to the range and azimuth compression with the synchronization errors are analyzed in detail. A phase error model transferred from the synchronization errors to the interferometric error is established. A simulation displays the results of the analysis.

The precession period was used to identify space targets in radar target recognition, especially the axisymmetric ballistic missile warheads and the similar shaped decoys. There are many precession period extraction methods based on RCS sequence. However, these methods have many restrictions and often yield poor results under noise condition. Aiming at extracting the precession period from RCS sequence, this paper designed a one-dimensional convolutional neural network. The precession period extraction is converted to a signal parameter estimation problem where the RCS sequence is the input signal and the period is the expected parameter. The proposed method was trained and validated on simulated RCS sequences and compared with spectral method, including CAUTOC, CAMDF, CAUTOC/CAMDF and trigonometric fitting method. The results showed that the proposed method yielded more accurate estimation results. Moreover, it can tell that there is no valid period by yielding a value that is not in the range [2, N /2]where $N$ is the length of the RCS sequence.

To realize real-timely detection of low altitude aircraft target, this paper introduces a parallel model based on multicore CPU aiming at pulse compression (PC). Eight cores are utilized to process parallel intensive task. The detailed implementation of pulse compression (PC) algorithm is introduced in this paper. The result indicates that this design meets the requirement of real-timely pulse compression with one echo sample.

A247 the template matching method is an effective way to acquire direct waves, there is a certain deviation in the acquisition and estimation of direct waves. The deviation of direct wave parameters directly affects the performance of subsequent related algorithms. In this paper, we mainly evaluate the performance of the pulse compression(PC) algorithm with non-cooperative radar parameters, and propose a direct detection method based on fractional Fourier and wavelet denoising. This paper gives the detection performance curve of this new method. The result shows that there is a certain gap between the proposed method and the ideal pulse compression method, but it has excellent performance with respect to the deviation of the direct wave parameters. Therefore, it is an excellent method to detect the target under a specific signal-to-noise ratio in the non-cooperative bistatic radar.

T248 separate and estimate the parameters of multi-component linear frequency modulation(LFM) signals in the presence of white Gaussian noise, a new method based on fractional Fourier transform(FRFT) with the help of short-time Fourier transform(STFT) and Hough transform is proposed. The theory of FRFT for estimating the chirp rate of the multi-component LFM signal is analyzed. Pointing out the phenomenon that when the amplitudes of the strong and weak component signals differ greatly, the weak component signal is disturbed by the strong component signal and can not be detected by the search method. This paper analyzes the advantages of using Hough transform after STFT, and then proposes the use of STFT+Hough transform to estimate the chirp to guide FRFT for small-scale search. The parameters of the strong signal components are estimated one by one, and the strong signal are eliminated in the fractional Fourier transform domain by using very narrow band-stop filtering to improve the reliability of the multi-component signal parameter estimation. The effectiveness of this method is verified by computer simulation.

I192 order to solve the distortion of one-dimensional range profile caused by the target motion in stepped frequency radar, a new method of target motion compensation based on the cross-correlation Fast Fourier transform (FFT) and the least burst error function is proposed. Firstly, the target echo model of stepped frequency signal is established. Then the effect of target radial motion on the high-resolution range profile is analyzed. Finally, aiming at accurate motion compensation, a new method which combinates fast rough estimation and accurate searching is proposed. Simulation results show that the proposed method has the advantages of good anti-noise performance, high estimation accuracy and low computational complexity in realtime processing.

Under the trend of complex electromagnetic environment and development of new radar system, the direct wave of noncooperative radar is easily affected by clutter, multipath, and other noncooperative illuminators. These problems make it difficult for passive radar to extract the parameters of the direct wave. In this paper, a novel signal processing method using suboptimal nearest-neighbor (SNN) approach is proposed to acquire accurate parameters of direct wave. In this method, the parameter estimation of direct wave is converted to time-frequency data association. Firstly, the direct wave is processed with time-frequency analysis. Then, the SNN approach is applied to perform time-frequency data association and acquire the associated time-frequency line. Finally, the rough parameters are measured by calculating the positions of time-frequency line and accurate parameters are obtained by template matching with the known parameters. Simulation and experimental results verify the feasibility of the proposed method.

Focusing on the inverse synthetic aperture radar (ISAR) imaging of targets with complex motion, this paper proposes a modified version of the Fourier transform, called non-uniform rotation transform, to achieve cross-range compression. After translational motion compensation, the target’s complex motion is converted into non-uniform rotation. We define two variables—relative angular acceleration (RAA) and relative angular jerk (RAJ)—to describe the rotational nonuniformity. With the estimated RAA and RAJ, rotational nonuniformity compensation is carried out in the non-uniform rotation transform matrix, after which, a focused ISAR image can be obtained. Moreover, considering the possible deviation of RAA and RAJ, we design an optimization scheme to obtain the optimal RAA and RAJ according to the optimal quality of the ISAR image. Consequently, the ISAR imaging of targets with complex motion is converted into a parameter optimization problem in which a stable and clear ISAR image is guaranteed. Compared to precedent imaging methods, the new method achieves better imaging results with a reasonable computational cost. Experimental results verify the effectiveness and advantages of the proposed algorithm.

Focusing on the inverse synthetic aperture radar (ISAR) imaging of maneuvering targets, this paper presents a new imaging method which works well when the target’s maneuvering is not too severe. After translational motion compensation, we describe the equivalent rotation of maneuvering targets by two variables—the relative chirp rate of the linear frequency modulated (LFM) signal and the Doppler focus shift. The first variable indicates the target’s motion status, and the second one represents the possible residual error of the translational motion compensation. With them, a modified Fourier transform matrix is constructed and then used for cross-range compression. Consequently, the imaging of maneuvering is converted into a two-dimensional parameter optimization problem in which a stable and clear ISAR image is guaranteed. A gradient descent optimization scheme is employed to obtain the accurate relative chirp rate and Doppler focus shift. Moreover, we designed an efficient and robust initialization process for the gradient descent method, thus, the well-focused ISAR images of maneuvering targets can be achieved adaptively. Human intervention is not needed, and it is quite convenient for practical ISAR imaging systems. Compared to precedent imaging methods, the new method achieves better imaging quality under reasonable computational cost. Simulation results are provided to validate the effectiveness and advantages of the proposed method.

Accessional phase component needs to be estimated and compensated when interferometric inverse synthetic aperture radar (InISAR) imaging under squint mode. Base on strict analysis and deduction, we proposed a novel method for the compensation of accessional interferometric phases under squint mode. Comparing to precedent methods, new method is more convenient to implement with equivalent precision. Simulation results demonstrate the effectiveness of our method.

Phase compensation is a critical step in inverse synthetic aperture radar (ISAR) imaging. At present, the phase autofocus algorithm based on entropy minimization works well and is widely used in practice. However, due to the possible insufficient iteration time, the noise interference and other adverse factors, the compensation phases may be just a local optimization in solution space. In this paper, a novel phase compensation method for ISAR imaging based on genetic algorithm is proposed. Based on the phase autofocus result of entropy minimization method, the genetic algorithm is used to find the better local optimal solution or even global optimal solution. Genetic algorithm is a random search algorithm derived from the process of biological evolution, and it could be applied to a lot of optimization problems. According to the experimental results of real measured data, it is highly possible that the phase compensation effect of proposed method can exceed that of the entropy minimization method, which proves the effectiveness of our method.

Nyquist Folding Receiver (NYFR) is a novel ultrawideband interception receiver architecture, which can sample a large range wideband signal by single Analog-to-Digital Converter (ADC). And the Synchronous Nyquist Folding Receiver (SNYFR) is an improved structure of NYFR. In this paper, a new SNYFR structure based on composite Sinusoidal Frequency Modulated (SFM) Local Oscillator (LO) is proposed. For Linear Frequency Modulated (LFM) signals intercepted by this SNYFR, a Nyquist Zone (NZ) estimation and parameter estimation algorithm based on instantaneous autocorrelation is deduced. The complexity of the algorithm is lower, which reduces the difficulty of resolving the LFM/SFM hybrid modulated signals after the ADC of SNYFR. Computer simulation shows that effective detection of multi-component LFM signals and accurate parameters estimation can be achieved by the proposed algorithm when the signal to noise ratio (SNR) is better than −8dB.

Aiming to integrate the target energy for stepped frequency radar, an algorithm combining Keystone transform and radon transform is proposed in this paper. Analyzing the reason for Doppler frequency migration (DFM) after coherent integration for the same frequency, Keystone transform is applied to eliminate the coupling relationship and then the DFM is corrected completely combining with Radon transform and finally the unambiguous velocity can be obtained. Simulation results have proved the validity of the proposed method.

Aiming at solving the problem of great difficulty and low accuracy existing in parameter estimation of chirp signal under low signal-to-noise ratio (SNR) condition, an algorithm of accurate parameter estimation of chirp signal is proposed. First, the algorithm extracts ridge frequency of chirp signal based on Short Time Fourier Transform. Second, the protruding glitch frequencies are eliminated through median filter with proper size and the smoothing frequencies are obtained corresponding to the time. Third, the frequency time frequency-modulated (FM) line is fitted coarsely by the least-square linear fitting method, and some frequency points are removed, which are far away from the FM line. Repeat the process several times until the sample correlation coefficient of the fitted line is in high degree when the optimum chirp line is fitted out, so chirp rate and initial frequency are obtained. Simulation shows that the correct rate of parameter estimation is very high. When SNR is not less than −17 dB, the correct rate is 100%, and when the SNR is −18 dB, the correct rate is still capable of up to 99%. This algorithm has a lower SNR threshold of about −17 dB. When the SNR is greater than threshold, parameter estimation accuracy is close to Cramer Rao low bound (CRLB), higher than parameter estimation based on the phase field method. Measured data experiments show that the algorithm can reasonably fit the chirp line of measured chirp signal, which better characterizes the FM features of chirp signal.

This paper proposed a novel parallel sampling technique by twice mixing operation is proposed. Firstly, the input signal is equally split into two channel, and sent to an analog mixer to respectively mix a square wave signal that are out of phase by 180 degrees. The DC and firstorder harmonic component allows the entire component of the input signal to be folded into the same Nyquist sub-band of the digitizer after low-pass filtering. Secondly, the filtered signals are digitized synchronously at the same sampling clock. A pair of linear phase FIR filters can be implemented to easily compensate the non-ideal or the mismatch of the digitizers in a whole subband. Finally, remix the sampled data respectively after digital down conversion, and the results are combined to obtain the parallel sampling result. The technique is step by step deduced and analyzed mainly based on the frequency domain method. Through a rational design the resulting signal spectrum substantially matches the original frequency spectrum. Analog and digital twice mixing technique break the bandwidth limit of digitizers, and its performance benefit from the symmetry of the two acquisition and propagation channel.

In radar signal processing, across range unit (ARU) and Doppler frequency migration (DFM) effect are important problems, which results in the degradation of integration performance. Especially for uniformly accelerated targets, both ARU and DFM will occur in long time integration. In order to deal with it, a hybrid integration algorithm is proposed in this paper. The keystone transform is applied in each frame for coherent integration. Then the trajectory feature of target is used for incoherent integration by parabola fitting. The experiment compares the performance of different methods and the results verify the validity of the proposed method.

The target motion parameter estimation is an important index to measure the performance of radar system. Accurate estimation of acceleration parameter has important influence on improving the detection and tracking performance of high maneuvering target. When radar transmits a signal with fixed frequency, the maneuvering target echo signal can be approximated as linear frequency modulation signal. Therefore, the acceleration parameter estimation of maneuvering target is transformed into the parameter estimation of linear frequency modulation signal. In this paper, a novel method for acceleration parameter estimation of maneuvering target is proposed based on the compressed sensing theory. The linear frequency modulation signal has sparse characteristic in fractional Fourier transform domain and compressed sensing theory asserts that we can employ nonadaptive linear projections to preserve the structure of the signal and reconstruct the signal with these projections using an optimization process. Theoretical analysis and experimental results verify that the method can effectively solve the precision problem of parameter estimation caused by the large sampling rate in the traditional signal processing method, and effectively reduce the data storage and transmission pressure in radar signal processing.

It is the research focus of electronic reconnaissance at all levels to locate the ground jamming source by using the spacecraft equipped with the direction finding system. Aiming at the characteristics of spacecraft working platform, the localization algorithm of single-satellite to ground radiation source based on direction finding is studied. In the simulation experiment, the STK satellite orbit software is used to generate the satellite position data and the azimuth angle and azimuth data of the target relative to the satellite. The data are modeled by the unscented Kalman filter. The positioning accuracy of extended Kalman filter and the unscented Kalman filter are analyzed. And the validity of the unscented Kalman filter in single-satellite positioning is verified.

Aiming at the common radar signal, a fast recognition algorithm of intra-pulse modulation with low SNR is proposed. Firstly, the signal is filtered by FFT, and the instantaneous frequency is calculated by phase difference. Then, characters are extracted by peak detection, least squares linear estimation and second order phase difference. Finally, the combined parameters are used to realize rapid recognition. The method of positive and negative frequency labeling is given, which is differentiated in the ambiguity resolution stage. Based on the analysis of phase error sources, the dual-threshold phase unwrapping method is proposed by using the principle of hysteresis comparator, which can effectively suppress the phase mutation caused by noise. Simulation results show that the recognition rate can reach 90% when SNR is above −4dB.

In order to solve the problem of frequency-hopping signal detection, an improved algorithm based on power spectrum elimination and pulse detection is proposed. Firstly, judge the signal type and estimate the width of signal with the hopping frequency by STFT and pulse detection. Then, eliminate the frequency-fixed signal interfere by suppressing the signal. Finally, judge the threshold to realize the fast detection and estimate the hopping frequency. This paper is aimed at Costas-encoding frequency-hopping signals. The proposed method decreases the computation and improves the detection probability. Simulation results indicate that it can achieve a detection probability of more than 90% when SNR is above −3.7dB.

For the problem of typical polyphase codes recognition, an algorithm based on integrated quadratic phase function (IQPF) and fractional Fourier transform (FRFT) is proposed. Firstly, IQPF is used to estimate the chirp rate of the signals for pre classification. Then accurate identification is realized according to the spectral characteristics of the signals' FRFT. The simulation results show that the probability of correct recognition is higher than 90% when signal to noise ratio (SNR) is above −7dB.

This paper investigates a subarray based algorithm for direction of arrival (DOA) estimation of wideband uniform linear array (ULA), under the presence of frequency-dependent mutual coupling effects. Based on the Toeplitz structure of mutual coupling matrices, the whole array is divided into the middle subarray and the auxiliary subarray. Then two-sided correlation transformation is applied to the correlation matrix of the middle subarray instead of the whole array. In this way, the mutual coupling effects can be eliminated. Finally, the multiple signal classification (MUSIC) method is utilized to derive the DOAs. For the condition when the blind angles exist, we refine DOA estimation by using a simple approach based on the frequency-dependent mutual coupling matrixes (MCMs). The proposed method can achieve high estimation accuracy without any calibration sources. It has a low computational complexity because iterative processing is not required. Simulation results validate the effectiveness and feasibility of the proposed algorithm.

This study focuses on sparse subband fusion imaging. A method based on high-precision parameter estimation of geometrical theory of diffraction (GTD) model is given. Considering the incoherence problem in each subband data, a coherent processing method is adopted in the paper. Based on an all-pole model, it makes use of the phase difference of pole and scattering coefficient between each sub-band to effectively estimate the incoherent components. After coherent processing, the high and low frequency subband data can be expressed as a uniform all-pole model. The gapped-data amplitude and phase estimation algorithm is adopted to fill up the gapped band. Finally, fusion data is gained by high precision parameter estimation of GTD-all-pole model with full-band data, such as scattering center number, scattering center type and amplitude. The experimental results of simulated data with fixed-points indicate that the resolution of one-dimensional (1D) range profile and 2D inverse synthetic aperture radar (ISAR) image based on this method is better than that of each sub-band. In this way, the validity of the proposed method is proved.

In this paper, a new direction of arrival (DOA) estimation algorithm is proposed when uncorrelated and coherent wideband signals coexist. The proposed method estimates the uncorrelated signals and coherent signals respectively at two different stages. It makes the method resolve more sources than the array elements. Compared with the coherent signal subspace method (CSSM), the proposed method does not require any initial DOA estimates, thus avoid errors brought by incorrect initial values. Simulation and experimental results validate the effectiveness of the proposed algorithm.

In this paper, a computationally efficient long-time integration and parameters estimation method via Keystone transform(KT), Radon Transform(RT) and Lv’s Transform(LVT) is proposed. Firstly, the KT-Radon is used to eliminate the range cell migration(RCM) of the echo after pulse compression, then using LVT to obtain the long-time integration and parameters estimation of high-speed target with acceleration motion. Compared with the GRFT algorithm, the proposed method only needs one dimensional search for range. Compared with the KT-FRFT algorithm, there is no need to search Doppler fold factor in this proposed method. So the proposed method doesn’t suffer from huge computation cost and obtain a good tradeoff between the computational cost and detection performance in comparison with the above algorithms. Finally, several experiments are provided to demonstrate the effectiveness of RadonKT -LVT.

This paper presents an algorithm for estimating the parameters of polynomial-phase signals (PPSs). This algorithm combines the CPF-HAF method and Radon transform, and can be referred to as the Radon-CPF-HAF method. In the proposed algorithm, the HAF is first applied on the original PPS to produce a cubic phase signal, whose parameters are then estimated by the Radon transform of the cubic phase function for the derived cubic phase signal. As involving in lower order nonlinearity, the proposed algorithm outperforms the HAF in terms of the accuracy and signal-to-noise-ratio (SNR) threshold. When multicomponent PPSs are considered, the product version of the proposed algorithm removes the identifiability problem which the product version of the CPF-HAF (PCPF-HAF) suffers from. Computer simulations have been carried out to support the theoretical results.

In order to eliminate the negative in∞uence of the rotational phase component (RPC) of target prominent scattering centres on the performance of Doppler centroid tracking (DCT) method, a coherent phase compensation method is proposed. The coherence of echo pulses sampled directly in intermediate frequency (IF) is flrstly analyzed and proved. Based on the coherence property, the proposed approach improves the translational phase component (TPC) estimation accuracy of DCT. Compared to the modifled Doppler centroid tracking (MDCT) algorithm, the proposed method achieves better phase compensation performance with simpler operations. Both the theoretical analysis and experimental results based on the real ISAR data prove the efiectiveness and e-ciency of the presented strategy.

Accurate and quick parameter estimation of linear Frequency Modulation (LFM) signal is always the research hot. In this paper, an improved dual-channel joint analysis of measurement algorithm is proposed for the recognition and parameter estimation of cross-channel LFM signal. First, coarse chirp rates are estimated by delay-related correlative dechirp, and then signal is identified coarsely by a comparison of chirp rates of two channels. Second, the initial frequency and chirp rate of channel A are estimated based on the Fractional Fourier Transform (FRFT) which needs to do local search. Finally, the estimated chirp rate of channel A is utilized to identify the signal in channel B with the parameters in time and frequency domain. Simulation results show that accuracy of recognition and parameter estimation for cross-channel LFM signal is satisfying when the signal to noise rate (SNR) is not less than −16 dB.

A robust beamforming scheme for the processing of wideband signals against direction of arrival (DOA) estimation errors with frequency response invariant property is proposed, where a response variation (RV) element is invoked to control the consistency of the beamformer's response over the frequency range of interest, simultaneously the eigenvector constraints are combined together to overcome the manifold errors caused by the DOA mismatch.

In radar multi-target detection, the weak target might be interfered by the near strong target, which badly influences the detection of weak target. A novel detection algorithm for weak targets is proposed to cope with the situation above. Combining the Keystone transform and the Least Square (LS) filter, the return of strong target is reconstructed and then removed from the original. Finally, the weak target is detected by integration for the residual. The effectiveness of the proposed algorithm is illustrated and analyzed by simulations.