Random Stepped Frequency Research Articles

Random Stepped Frequency ISAR 2D Joint Imaging and Autofocusing by Using 2D-AFCIFSBL

With the increasingly complex electromagnetic environment faced by radar, random stepped frequency (RSF) has garnered widespread attention owing to its remarkable Electronic Counter-Countermeasure (ECCM) characteristic, and it has been universally applied in inverse synthetic aperture radar (ISAR) in recent years. However, if the phase error induced by the translational motion of the target in RSF ISAR is not precisely compensated, the imaging result will be defocused. To address this challenge, a novel 2D method based on sparse Bayesian learning, denoted as 2D-autofocusing complex-value inverse-free SBL (2D-AFCIFSBL), is proposed to accomplish joint ISAR imaging and autofocusing for RSF ISAR. First of all, to integrate autofocusing into the ISAR imaging process, phase error estimation is incorporated into the imaging model. Then, we increase the speed of Bayesian inference by relaxing the evidence lower bound (ELBO) to avoid matrix inversion, and we further convert the iterative process into a matrix form to improve the computational efficiency. Finally, the 2D phase error is estimated through maximum likelihood estimation (MLE) in the image reconstruction iteration. Experimental results on both simulated and measured datasets have substantiated the effectiveness and computational efficiency of the proposed 2D joint imaging and autofocusing method.

Forward-looking high-resolution imaging using random stepped-frequency waveform

Forward-looking high-resolution imaging using random stepped-frequency waveform

Sensing Matrix Optimization for Random Stepped-Frequency Signal Based on Two-Dimensional Ambiguity Function

Sensing Matrix Optimization for Random Stepped-Frequency Signal Based on Two-Dimensional Ambiguity Function

MIMO Radar Sparse Recovery Imaging with Wideband Interference Prediction

Multiple-input multiple-output (MIMO) radar three-dimensional (3D) imaging is widely applied in military and civil fields. However, MIMO is easily affected by wideband interference (WBI). To solve this problem, in this study, we propose a sparse recovery imaging method with WBI prediction based on the predictive recurrent neural network (PredRNN) and the tensor-based smooth L0 (TSL0) algorithm. Firstly, we extract the time-frequency (TF) feature of the historical measured WBI via the short-time Fourier transform (STFT) operation. In this way, we can use PredRNN to exploit the spatiotemporal correlation of the WBI in the TF domain to predict the TF feature of the WBI in the future. Then, we adaptively design the random sparse stepped frequency waveform by selecting non-overlapped frequencies with the WBI according to the predicted WBI TF feature. Finally, we apply the TSL0 algorithm to reconstruct the 3D high-resolution target image from the sparse signal cube. Simulation results show the high performance and robustness of the proposed imaging method in the presence of different WBIs.

Joint random stepped frequency ISAR imaging and autofocusing based on 2D alternating direction method of multipliers

Random stepped frequency (RSF) inverse synthetic aperture radar (ISAR) has attracted more attention due to its outstanding electronic counter-countermeasure (ECCM) performance by frequency agility. However, the two-dimensional (2D) sparse sampling echo, i.e. sparse sub-pulses and sparse aperture, increases the difficulty of 2D high-resolution ISAR imaging and autofocusing. Aiming to solve this problem, a novel 2D alternating direction method of multipliers (2D-ADMM) based imaging and autofocusing framework is proposed to achieve 2D high-resolution for 2D sparse RSF ISAR. In order to obtain the optimal value for phase correction, the phase error corresponding to each sub-pulse is estimated by solving an unconstrained optimization problem. To jointly achieve autofocusing during the process of ISAR image reconstruction, the phase error estimation is merged into the image reconstruction framework, and then two different strategies under the 2D ADMM framework are derived to iteratively solve this compound optimization problem in matrix form. Experiments based on both simulated and measured data validate the effectiveness of the proposed 2D joint methods, meanwhile, maintaining an acceptable imaging efficiency.

A sensing matrix optimization method for sparse random stepped-frequency signal based on autocorrelation function

Based on the theory of sensing matrix optimization and the characteristics of step frequency waveform, this paper proposes a sensing matrix optimization method for sparse random step frequency signal based on autocorrelation function. Firstly, on the basis of constructing sparse reconstruction model, the internal relationship between the waveform parameters of sparse random step frequency signal and the construction mode of sensing matrix is given. Secondly, the relationship between the mutual coherence matrix and the autocorrelation function matrix of signal is analyzed, and the conclusion that under specific conditions, these two matrices are the same. Therefore, the optimal design of sensing matrix is transformed into the optimal design of sparse random stepped frequency signal based on autocorrelation function. Finally, a sparse waveform design method based on the joint constraint of the maximum and average sidelobes of the autocorrelation function is proposed. Simulation experiments verify the relationship between the mutual coherence matrix and the autocorrelation function matrix of signal, and through waveform design, the sensing matrix optimization and the improving performance of signal sparse recovery are realized.

Random Stepped-Frequency SAR Imagery With Full Cell Doppler Coherent Processing

Random stepped-frequency chirp waveform (RSFCW) is capable of achieving high-resolution synthetic aperture radar (SAR) imagery as well as certain electronic countermeasure capabilities. The Doppler-dependent phase incoherence due to the platform motion leads to degradation of the frequency synthesis of RSFCW. In this letter, a coherence processing algorithm for RSFCWSAR is developed with full cell Doppler correction (FCDC). The waveform structure of RSFCW is exploited to decompose the signal into different Doppler cells, which paves a way to the FCDC in phase alignment for frequency synthesis. Extensive simulation has confirmed the superiority of our method’s performance in comparison to those of traditional techniques.

Interference Mitigation in Automotive Radars Using Pseudo-Random Cyclic Orthogonal Sequences.

The number of small sophisticated wireless sensors which share the electromagnetic spectrum is expected to grow rapidly over the next decade and interference between these sensors is anticipated to become a major challenge. In this paper we study the interference mechanisms in one such sensor, automotive radars, where our results are directly applicable to a range of other sensor situations. In particular, we study the impact of radar waveform design and the associated receiver processing on the statistics of radar–radar interference and its effects on sensing performance. We propose a novel interference mitigation approach based on pseudo-random cyclic orthogonal sequences (PRCOS), which enable sensors to rapidly learn the interference environment and avoid using frequency overlapping waveforms, which in turn results in a significant interference mitigation with analytically tractable statistical characterization. The performance of our new approach is benchmarked against the popular random stepped frequency waveform sequences (RSFWS), where both simulation and analytic results show considerable interference reduction. Furthermore, we perform experimental measurements on commercially available automotive radars to verify the proposed model and framework.

Computationally efficient range profile synthesis method based on zero-padding inverse discrete Fourier transform for random stepped frequency radar

The modulation frequencies of random stepped frequency radars are randomly distributed over the expected specific bandwidth. The characteristics of random modulation frequencies make it difficult to synthesize the high-resolution range profile fleetly. A zero-padding inverse discrete Fourier transform method is proposed to address the problem. Theoretical analysis shows that the proposed method has the same signal-to-noise ratio improvement performance as the classical methods, but relatively lower computational complexity. The experimental results validate the effectiveness and efficiency of the proposed method.

Cognitive Transmitting Frequency Codewords Design for Sparse Stepped Frequency Radar Based on Sensing Matrix Optimization

Cognitive waveform design is of great significance for radar transmitting system to better adapt to the changing electromagnetic environment and target scene. In this paper, a novel cognitive waveform design method of sparse stepped frequency based on sensing matrix optimization is proposed. The proposed cognitive waveform exploits the information returned from the target scene to continuously optimize the transmitted frequency and sensing matrix. Compressive sensing (CS) technique is applied for range-Doppler reconstruction due to the sparse property of the target to be estimated. Comparing with random stepped frequency, this optimized cognitive waveform could further improve the upper limit of the number of detection targets; also increase the accuracy and robustness.

Waveform design for random stepped frequency radar to estimate object velocity

A novel method to estimate the object velocity for random stepped frequency radar by the waveform design is proposed. The method utilises a so-called ‘complementary random code’ to modulate the transmitted waveform. Theoretical analyses show that the proposed method achieves a high precision in velocity estimation, which could meet the requirements of motion compensation. Moreover, its computational complexity can be significantly reduced, compared with the classical estimation methods. Finally, the simulations validate the effectiveness of the present method.

Motion Analysis and Compensation Method for Random Stepped Frequency Radar Using the Pseudorandom Code

To address the defocusing problem faced by random stepped frequency (RSF) radars, this paper presents a complementary code cancellation (CCC) method to estimate the target velocity and achieve motion compensation. The proposed CCC method is capable of eliminating the coupling effect between range profile and target velocity. In this paper, we first give a block diagram of RSF radar modulated by the M-sequence-generated pseudorandom code, and introduce the baseband sampling echo model of moving target. Then, the velocity estimation accuracy is derived to show the sensitivity of high-resolution range profile to target velocity. Subsequently, the CCC method is proposed and also investigated in the application of multi-target scenario. Performance analyses demonstrate that the proposed method can satisfy the estimation accuracy requirement and improve the signal to noise ratio by the velocity accumulation. Finally, simulations show that the method is effective and more computationally efficient than the existing popular methods.

A Novel Motion Compensation Method for Random Stepped Frequency Radar with M-sequence

The random stepped frequency radar is a new kind of synthetic wideband radar. In the research, it has been found that it possesses a thumbtack-like ambiguity function which is considered to be the ideal one. This also means that only a precise motion compensation could result in the correct high resolution range profile. In this paper, we will introduce the random stepped frequency radar coded by M-sequence firstly and briefly analyse the effect of relative motion between target and radar on the distance imaging, which is called defocusing problem. Then, a novel motion compensation method, named complementary code cancellation, will be put forward to solve this problem. Finally, the simulated experiments will demonstrate its validity and the computational analysis will show up its efficiency.

FM sequence optimisation of chaotic‐based random stepped frequency signal in through‐the‐wall radar

Chaotic-based random stepped frequency signal is applied in the multiple-input-multiple-output through-the-wall detection radar (MIMO-TWDR)recently. When the frequency modulation (FM) sequence of transmission signal is controlled by the chaotic signal, the single-frequency interference such as the power harmonics sneaking into the phase detector becomes periodical and therefore can be filtered in frequency domain. However, the target echo signal becomes random after chaotic modulation, where the matched filter usually is unable to be realised by Fourier transform and consequently the envelope of direct wave after the phase detector varies stochastically and is difficult to be eliminated by an analogue filter. The FM sequence of random disorganising cannot meet the demand of filtering out the single-frequency interference and direct wave simultaneously. Therefore, a new method of FM sequence optimisation of chaotic-based random stepped frequency signal based on genetic algorithm is proposed to solve these problems in this study. Simulations show that the optimised stepped frequency signal possesses the advantages of both chaotic-based random and linear stepped frequency signal. The proposed scheme achieves excellent performance on direct wave and single-frequency interference suppression and target detection. Moreover, it can avoid the interference between transmission antennas of MIMO radar.

A novel imaging method for random stepped frequency SAR with low SNR

ABSTRACTThis letter proposes a novel structured compressive sensing (CS) imaging method in random stepped frequency (RSF) synthetic aperture radar (SAR) for better imaging performance in the presence of strong additive noise. In this method, the multiple measurement vectors (MMV) model is established in range-Doppler domain with range migration taken into consideration and a modified sparse recovery algorithm is derived to synthesize the one-dimension range profile. Traditional cross-range focusing algorithm is then performed to obtain a well-focused image. The proposed method makes full use of the coherent information among different bursts, which may further illuminate the influence of additive noise and decrease the number of transmitted pulses needed in one burst. The simulated results demonstrate the validity of the proposed method.

Moving target detection method for bistatic radar using random stepped-frequency chirp signal

As random stepped-frequency chirp (RSFC) signal is used in wide-band radar applications such as synthetic aperture radar (SAR) and inverse SAR. RSFC has advantages over the linear stepped-frequency chirp, including suppressing the range ambiguity, decoupling the range-Doppler coupling, and reducing the signal interference. RSFC is usually descrambled and then fed to the inverse fast Fourier transform (IFFT) to achieve a coherent integration as well as a high-resolution range. However, this method needs frequency descrambling and accurate velocity pre-estimation for moving target detection. We propose a coherent integration method based on time-dechirping for bistatic radar. This method can detect moving targets without frequency descrambling or accurate velocity pre-estimation. This paper first models the target echo mathematically and outlines the difficulties associated with the processing of IFFT for RSFC. Then the detailed principles of the proposed method are introduced and the flowchart is given. Finally, numerical simulations are conducted to verify the effectiveness of the proposed method and show its detecting ability in the presence of noise.

利用压缩感知实现随机变频雷达散射中心估计

利用压缩感知实现随机变频雷达散射中心估计

Three-dimensional sparse image reconstruction for terahertz surface layer holography with random step frequency.

In this Letter, a sparse image reconstruction approach is proposed for three-dimensional (3D) terahertz (THz) surface layer holography by a sharply dwindled amount of frequency samples, without reducing the high quality of the final reconstructed 3D THz images. To avoid the range ambiguity resulting from the reduction of frequency samples, a random step frequency method is adopted to evaluate the rough range profile of the 3D surface layer. With the obtained range profile, a de-ambiguity procedure is proposed to demodulate the sparse echoed data to greatly compress the maximum nonambiguous range and recover all the information for 3D holography image reconstruction. Proof-of-state experiments are performed in 0.2-THz band. The results verify the effectiveness and the efficiency of the sparse imaging scheme for THz surface layer 3D holography.

Performance of ML Range Estimator in Radio Interferometric Positioning Systems

The radio interferometric positioning system (RIPS) is a novel positioning solution used in wireless sensor networks. This letter explores the ranging accuracy of RIPS in two configurations. In the linear step-frequency (LSF) configuration, we derive the mean square error (MSE) of the maximum likelihood (ML) estimator. In the random step-frequency (RSF) configuration, we introduce average MSE to characterize the performance of the ML estimator. The simulation results fit well with theoretical analysis. It is revealed that RSF is superior to LSF in that the former is more robust in a jamming environment with similar ranging accuracy.

Decoupled ISAR imaging using RSFW based on twice compressed sensing

Random stepped-frequency radar without delay-Doppler coupling can suppress the range ambiguity and become less sensitive to electronic countermeasures. Considering its inherent randomness, this paper focuses on sidelobe reduction in inverse synthetic aperture radar imaging for sparse target scenes based on the compressed sensing (CS) theory. First, precise motion compensation and a high-resolution range profile (HRRP) with a low sidelobe are simultaneously achieved by the CS scheme for each train containing fewer pulses. Then, we analyze the disadvantages of conventional cross-range compression algorithms, which cannot guarantee high-quality focusing performance because there may be some false HRRPs caused by the uncertainty of the CS theory or some other factors. Finally, the modified correlation coefficient is defined to discard a large percentage of those uncorrelated HRRPs and the cross-range resolution is achieved by using the CS theory again. The validity of this decoupled imaging algorithm is demonstrated by some simulation and experimental results, which indicate that the approach is capable of precise estimation of scattering centers and effective suppression of a high sidelobe.