Range migration compensation based on keystone transform in sea clutter

This study considers the coherent integration problem for detecting a manoeuvring target, involving range migration (RM) and Doppler frequency migration (DFM) within the coherent processing interval. An efficient coherent integration method based on keystone transform (KT) and Lv's distribution (LVD) is proposed. It can not only correct the RM effect by KT and fold factor phase compensation, but also remove the DFM effect and achieve the coherent accumulation via LVD. Compared with the moving target detection, Radon‐Fourier transform, and Radon‐fractional Fourier transform algorithms, the proposed method can obtain better detection ability under low signal‐to‐noise ratio environment. Finally, several simulations are provided to demonstrate the effectiveness.

Velocity measurement is a basic task of radars. The target velocity is usually estimated according to the Doppler frequency shift. While traditional Doppler methods are unsuitable for high-speed targets, since the serious range migration between adjacent echoes causes phase wrapping. The serious range migration also interferes the coherent integration to improve the accuracy of the velocity estimation. A velocity measurement method based on Keystone transform using entropy minimization is studied to solve this problem. This method applies Keystone transform to the echo to calculate the ambiguity degree with the help of entropy minimization. The proposed algorithm estimates the ambiguity degree with no error at a wider range of SNR than the traditional method. The ambiguous Doppler frequency is obtained according to the slow time. Theoretical analyses and simulations show that this method has very high precision.

In pulse Doppler (PD) radar, a novel motion compensation method based on keystone transform is proposed for handling with the data of a low signal-to-noise ratio (SNR). The keystone transform is used to correct the linear range migration which results from the radial velocity of targets. The SNR is improved through coherently accumulating all pulses. The simulation results show that the performance of the method based on keystone transform is better than that of the conventional coherent integration method using fast Fourier transform (FFT).

Keystone Transform (KT) and Radon Fourier Transform (RFT) are two popular methods proposed to overcome range migration in radars. A major concern in these methods is the computational complexity for real time operations. In this paper, a low complexity implementation of recurrent chirp-z transform (CZT) is offered in order to be employed in fast KT with no loss in performance. Additionally, a novel RFT implementation utilizing recurrent CZT is proposed to take advantage of the fast execution of repeated CZT. A mathematical analysis and simulation results are presented to show the performance and efficiency of the proposed techniques.

Methods for remote sensing of the environment with the help of Noise Waveform SAR systems with nonlinear antenna motion trajectory are widely applied at the present time. The elaboration of simulation models for the reflection and SAR imaging algorithms is necessary for successful design of such radar systems. The algorithm for numerical simulation of the reflected sequence of noise pulses obtained on the basis of the approximate model of local scattering sources for the case of SAR with an arbitrary antenna motion trajectory is presented in the paper. A universal SAR imaging algorithm with the range migration compensation is also presented; it can be used to obtain SAR images for both monostatic and bistatic cases while antenna trajectories of any type (linear, arc, etc) can be used for the survey.

Sea clutter is the backscattered returns from a patch of the sea surface illuminated by a radar pulse. Robust detection of targets within sea clutter is of significant importance to our coastal and national security, to navigation safety, and to environmental monitoring. However, no simple and reliable methods for detecting targets within sea clutter have been proposed. We apply the structure function based multifractal theory to analyze 392 sea clutter datasets measured under various sea conditions. The method developed is mathematically rigorous, conceptually elegant, computationally fast, and, practically, easily implementable. Our results demonstrate that the method can robustly detect low observable objects within sea clutter.

Maritime Conditions represent a challenging environment for radar detection due to the so-called sea clutter. It may lead to a lower probability of a correct target detection. Thus, a detailed modelling of sea clutter with its time-dependent correlations is crucial for evaluating the robustness of modern radar systems and their signal processing. This paper utilizes a stochastic approach to generate time-correlated sea clutter, dependent of the used radar system, the environment and the measuring geometry. Therefore, different models are used to determine the RCS of the illuminated sea surface, the time-dependent correlations and finally the statistical distribution of the received signal-amplitudes. Furthermore, bistatic radar geometries should be at the scope of this paper. Afterwards the simulated signal data is compared and validated with free accessible, recorded radar data from different locations and environmental conditions. The comparison with the correlation properties and probability density functions of the radar data shows good accordance with the data generated by the model. Furthermore the results show that generating sea clutter with the developed stochastic model and a minimum of basic observeable and available quantities (sea state, wind direction) is sufficient accurate and possible. By utilizing the presented method, time-correlated radar data, which imitates the different properties of real-world sea clutter, is generated in an efficient, less time consuming and reliable laboratory environment with a minimum of known environmental parameters.

HighlightsWhat are the main findings?This paper presents a high-order phase compensation and parameter estimation algorithm designed for maneuvering targets, which effectively balances detection performance and computational efficiency.The proposed method achieves precise compensation of high-order range migrations without the need for exhaustive parameter search, thereby circumventing the blind speed sidelobe effect. Futhermore, the incorporation of a time-delay variable significantly improves the anti-noise capability of the approach.What is the implication of the main finding?This algorithm introduces a novel motion compensation technique to address the challenges of range migration, Doppler frequency migration, and Doppler ambiguity in detecting weak maneuvering targets. It achieves precise range migration compensation with enhanced accuracy and robustness, forming a vital preprocessing step for high-quality SAR imaging.The algorithm supports simultaneous multi-target processing and holds promising application potential in fields such as airborne radar detection, maritime moving target indication, cooperative observation by UAV formations, and other fields.This study focuses on the key challenges in detecting and estimating motion parameters of ground maneuvering targets for airborne radar sensors. The complex unknown motion states of the ground maneuvering target, including velocity, acceleration, and jerk, result in range migrations (RMs) and Doppler frequency migrations (DFMs). These effects severely degrade the long-time coherent accumulation performance of the airborne radar, thereby limiting the reliable detection and precise parameter estimation of maneuvering targets. To address this issue, a new detection and motion parameter estimation method based on the range frequency reversal transform (RFRT) and searching Lv’s distribution (SLVD), i.e., RFRT-SLVD, is proposed. Specifically, the third-order RM (TRM) and quadratic DFM (QDFM) are considered. The proposed method operates as follows: First, RMs are eliminated simultaneously via the RFRT operation, which multiplies the echo by its reversed data in the range frequency and slow-time domains, leveraging the symmetric equal-interval sampling property of the range frequency. Subsequently, a phase compensation function (PCF) related to the jerk is constructed to compensate the QDFM. Finally, the LVD is performed to remove residual DFMs and achieve effective signal energy accumulation. Additionally, the case of a fast-moving target with Doppler ambiguity is analyzed, and a method for estimating three motion parameters is provided. A key advantage of the proposed technique is its ability to directly compensate the RMs without requiring prior knowledge of the maneuvering target, while also avoiding the blind speed sidelobe (BSSL) effect. In comparison with existing algorithms, RFRT-SLVD achieves a balanced trade-off between parameter estimation performance and computational efficiency. Numerical analyses and experiments are conducted to validate the method, assessing its detection capability for ground maneuvering targets, Doppler ambiguity resolution in parameter estimation, computational complexity, and method applicability in multi-target scenarios.

This paper evaluates the potentialities of polarimetric ship scattering for basing classification methods that provide reasonable performance within cluttered scenes. Both simulated and airborne polarimetric synthetic aperture radar (SAR) images have been used to validate the conclusions of a previous phenomenological study. Numerical simulations have been carried out with GRECOSAR, a polarimetric interferometric SAR simulation tool that is able to process highly complex targets with a fast and accurate radar-cross-section prediction module. A representative set of scenarios has been defined, which includes various realistic ship models, sea states, and imaging geometries. In all of them, a two-scale sea surface model precisely accounting for sea-ship interaction and sea clutter has been added. The analysis of different images has shown that, with an adequate spatial resolution, ships may be characterized by a particular spatial arrangement and polarization state distribution of dominant scattering centers. This feature has allowed one to propose a new classification algorithm, which shows a promising behavior after various preliminary tests. In this paper, the performance of this technique is further evaluated with realistic clutter. The results show that robust classification is possible even in highly cluttered scenes if quad-pol imagery is available. On the contrary, in low clutter conditions, the usage of less restrictive solutions, like circular dual-pol schemes, is feasible and may still get an acceptable performance.

Range migration is unavoidable and cannot be neglected in pulse Doppler radar when target velocity is high and/or wideband transmit signals are used. In this paper, we investigate the range and Doppler degradation due to target motion in wideband pulse Doppler radar. To correct range cell migration, an alternative focusing method is introduced by using the backprojection algorithm. The success of the method is shown by simulations.

Airborne radar system performance primarily depends on the target characteristics and the type of waveform used. Based on the type of waveform used, target detection, range resolution, doppler resolution and peak to side lobe level ratio (PSLR) can be improved in a radar system. Hence for a radar engineer choosing a proper waveform is critical in order to meet the system performance in different target environment conditions. This paper focuses on designing a suitable radar waveform for sea environment considering Sea clutter, Sea states, doppler and Swerling target models in X Band. In order to design a waveform under different Sea environments, an end to end radar system will be modelled, simulated and verified the system performance in Matlab. PSLR, Integrated Side Lobe ratio (ISLR), range resolution and target detection will be considered as performance metrics for the design of the waveform.

Sea clutter has a serious influence on the detection performance of a shipborne high‐frequency surface wave radar (HFSWR) system, especially when the target is submerged in the spread Doppler spectrum of the first‐order sea clutter. A prediction cancellation (PC) method based on the radial basis function neural network (RBFNN) is put forward to suppress the first‐order sea clutter in time domain for shipborne HFSWR. Using the RBFNN trained by the averaged sea clutter data from the range cells surrounding the range cell of interest, the PC algorithm is implemented in the interested range cell. Compared with the two‐dimensional fast Fourier transformation plus digital beam forming algorithm, the proposed PC algorithm is shown to give far superior sea clutter suppression results based on real data.

In order to study the space-time characteristics of sea clutter, the sea clutter is always measured by the airborne multichannel radar; however, the sea clutter shows the heterogeneity between range gates, which means the space-time covariance matrix’s correspondence to the single range gate cannot be estimated accurately. Meanwhile, the measurement of the sea clutter data by the airborne radar is usually affected by the motion of the platform, which makes the analysis results unrepresentative of the space-time characteristics of the pure sea clutter. In this paper, a sea clutter measurement method based on L-band shore-based multichannel radar is proposed, where the transmit sub-array periodically moves with the pulse repetition period to obtain multiple sets of coherent processing interval pulses for each range gate. This measurement method can exclude the influences of the moving platform. Moreover, a sea clutter space-time signal model of the single range gate is proposed, and the model is used to simulate three-dimensional sea clutter data with space-time coupling characteristics. With verification of the measured and simulated data, it can be seen that the data composed of single range gate and multiple coherent processing interval pulses can accurately estimate the space-time covariance matrix corresponding to this single range gate. Furthermore, the space-time characteristics are analyzed based on the measured data. The results show that the eigenvalue spectrum and the spread width of space-time power spectrum are influenced by the backscattering coefficient of sea clutter and the speed of sea surface motion. In comparison, the decorrelation effect caused by the backscattering coefficient of sea clutter is stronger than that caused by the speed of the surface motion. The proposed method is helpful for guiding multichannel sea clutter measurement and the analysis results are of great significance to the clutter suppression algorithms of the marine multichannel radar.

Sea clutter refers to the backscattered returns from a patch of the sea surface illuminated by a transmitted radar pulse. Since the complicated sea clutter signals depend on the complex wave motions an the sea surface, it is reasonable to study sea clutter from nonlinear dynamics, especially chaos, point of view, instead of simply based on random processes. In the past decade, Dr. Simon Haykin's (1997) group at the McMaster University of Canada carried out analysis of some sea clutter data using chaos theory, based on the the assumption that a chaotic attractor is fully characterized by a non-integer fractal dimension and a positive Lyapunov exponent. Thus, they concluded that sea clutter signals are chaotic. In other words, the complicated sea clutter waveforms are generated by nonlinear deterministic interactions of a few modes (i.e., number of degrees of freedom). However, a numerically estimated non-integral fractal dimension and a positive Lyapunov exponent may not be sufficient indication of chaos. Cowper and Mulgrew (see Proc. UCNN, vol.4, p.2633, July 1999), Noga (see Ph.D thesis, Cambridge University, 1998), and Davies (1994) separately have questioned the chaoticness of the radar sea clutter. We show, using the direct dynamical test for deterministic chaos developed by Gao and Zheng, which is one of the more stringent criteria for low-dimensional chaos, a two minute duration sea clutter data is not chaotic. We also carry out a multifractal analysis of this sea clutter data set, and find that the original sea clutter amplitude signal is approximately multifractal, while the envelope signal, formed by picking up the successive local maxima of the amplitude signal, thus measuring the energy of successive waves on the sea surface, is well modeled as multifractals. These behaviors determine that the amplitude signal follows approximately log-normal distribution while the envelope signal, and thus the energy of the successive waves of the sea surface, is log-normally distributed. Approximate log-normal distributions for the amplitude signal has been observed earlier. However, by using the multiplicative multifractal theory, there is theoretical justification for the log-normal distribution of sea clutter, as discussed. The implications of the multifractal nature of sea clutter may have relevance for the detection of point targets on the sea surface.

Usually, in traditional Inverse Synthetic Aperture Radar (ISAR) systems design and mode selection for space satellite targets, coherent integration gain in azimuth direction hardly can be analyzed, which depends on target’s motion. In this study, we combine the target orbit parameters to determine its motion relative to radar and deduce coherent integration equation in ISAR imaging to realize the selection of imaging intervals based on coherent integration, which can ensure the resolution in azimuth direction. Meanwhile, we analyze the influence of target orbit altitude to echo power and imaging Signal-to-Noise Ratio (SNR) that provides a new indicator for space observation ISAR systems design. The result of simulation experiment illustrates that with target orbit altitude increasing, coherent integration gain in azimuth direction of large-angular observation offsets the decreasing of imaging SNR in a degree, which provides a brand-new perspective for space observation ISAR systems and signal processing design.