Search Results (14)

This paper proposes a frequency-modulated continuous-wave synthetic aperture radar (FMCW-SAR) imaging system for fast target detection. The system’s antenna array improves azimuthal resolution while maintaining low complexity using a 44-element equivalent virtual array and improves the data acquisition efficiency by employing the trigger and MCU control board. A series of improved algorithms are adopted to increase the speed of radar imaging and achieve fast detection. To solve the problem of large data volumes in traditional array antenna switching control methods, an array switching control algorithm is proposed based on the enhanced ordered statistical constant false alarm rate (EOS-CFAR). The data volume is reduced by dividing the array into several subarrays in advance. The echo signals acquired by the array switching control method are not continuous in the azimuthal direction, and data anomalies are handled by interpolating and compensating the received radar data to form compensated periodic data. The coherent background is subtracted from the padded signal using recursive averaging, resulting in high-resolution imaging while improving the data-processing speed. The TensorFlow-based Omega-K algorithm is employed for synthetic aperture radar (SAR) imaging, which customizes the optimization of TensorFlow for array radar signals. For the radar signal phase optimization, an improved Adam Optimizer optimizes the phase of the radar signal to maintain phase smoothing, thereby improving the clarity of the radar image. The Omega-K algorithm is optimized by TensorFlow and accelerated on the GPU to improve the efficiency of the large-scale fast Fourier transform (FFT) and Stolt interpolation operations, which improves the speed of radar imaging and enables fast detection. Full article

Automotive millimeter-wave (MMW) synthetic aperture radar (SAR) systems can achieve high-resolution images of detection areas, providing environmental perceptions that facilitate intelligent driving. However, curved path is inevitable in complex urban road environments. Non-uniform spatial sampling, brought about by curved path, leads to cross-coupling and spatial variation deteriorates greatly, significantly impacting the imaging results. To deal with these issues, we developed an Extended Omega-K Algorithm (EOKA) for an automotive SAR with a curved path. First, an equivalent range model was constructed based on the relationship between the range history and Doppler frequency. Then, using azimuth time mapping, the echo data was reconstructed with a form similar to that of a uniform linear case. As a result, an analytical two-dimensional (2D) spectrum was easily derived without using of the method of series reversion (MSR) that could be exploited for EOKA. The results from the parking lot, open road, and obstacle experimental scenes demonstrate the performance and feasibility of an MMW SAR for environmental perception. Full article

For ultra-high-resolution (UHR) squint spotlight airborne synthetic aperture radar (SAR), the severe range-azimuth coupling caused by squint mode and the spatial and frequency dependence of the motion error brought by ultra-wide bandwidth both make it difficult to obtain satisfactory imaging results. Although some autofocus methods for squint airborne SAR have been presented in the published literature, their practical applicability in UHR situations remains limited. In this article, a new 2D wavenumber domain autofocus method combined with the Omega-K algorithm dedicated to UHR squint spotlight airborne SAR is proposed. First, we analyze the dependence of range envelope shift error (RESE) and range defocus on the squint angle and then propose a new spectrum modification strategy, after which the spectrum transforms into a quasi-side-looking one. The accuracy of estimation and compensation can be improved significantly in this way. Then, the 2D phase error can be calculated with the 1D estimated error by the mapping relationship, and after that the 2D compensation is performed in the wavenumber domain. Furthermore, the image-blocking technique and range-dependent motion error compensation method are embedded to accommodate the spatial-variant motion error for UHR cases. Simulations are carried out to verify the effectiveness of the proposed method. Full article

As miniaturization technology has progressed, Synthetic Aperture Radar (SAR) can now be mounted on Unmanned Aerial Vehicles (UAVs) to carry out observational tasks. Influenced by airflow, UAVs inevitably experience deviations or vibrations during flight. In the context of cost constraints, the precision of the measurement equipment onboard UAVs may be relatively low. Nonetheless, high-resolution imaging demands more accurate track information. It is therefore of great importance to estimate high-precision tracks in the presence of both motion and measurement errors. This paper presents a robust track estimation method for airborne SAR that makes use of both envelope and phase errors. Firstly, weak navigation information is employed for motion compensation, which reduces a significant portion of the motion error. Subsequently, the track is initially estimated using additional envelope errors introduced by the Extended Omega-K (EOK) algorithm. The track is then refined using a phase-based approach. Furthermore, this paper presents the calculation method of the compensated component for each target and provides an analysis of accuracy from both theoretical and simulation perspectives. The track estimation and imaging results in the simulations and real data experiments validate the effectiveness of the proposed method, with an estimation accuracy of real data experiments within 5 cm. Full article

Synthetic aperture radar (SAR) raw data do not have a direct application; therefore, SAR raw signal processing algorithms are used to generate images that are used for various required applications. Currently, there are several algorithms focusing SAR raw data such as the range-Doppler algorithm, Chirp Scaling algorithm, and Omega-k algorithm, with these algorithms being the most used and traditional in SAR raw signal processing. The most prominent algorithm that operates in the frequency domain for focusing SAR raw data obtained by a synthetic aperture radar with large synthetic apertures is the Omega-k algorithm, which operates in the two-dimensional frequency domain; therefore, in this paper, we used the Omega-k algorithm to produce SAR images and modify the Omega-k algorithm by adding the Doppler factor to improve the accuracy of SAR raw data processing obtained by the continuous wave and pulsed frequency modulated linear frequency modulated radar system from the surfaces of interest. On the other hand, for the case of unmanned aerial vehicle-borne linear frequency modulated continuous wave (LFM-CW) SAR systems, we added motion compensation to the modified Omega-k algorithm. Finally, the testing and validation of the developed Omega-k algorithm used simulated and real SAR raw data for both pulsed synthetic aperture and continuous wave radars. The real SAR raw data used for the validation of the modified Omega-k algorithm were the raw data obtained by the micro advanced synthetic aperture radar (MicroASAR) system, which is an LFM-CW synthetic aperture radar installed on board an unmanned aerial system and the raw data obtained by European remote sensing (ERS-2) satellite with a synthetic aperture radar installed. Full article

The availability of low-cost microwave components today enables the development of various high-frequency sensors and radars, including Ground-based Synthetic Aperture Radar (GBSAR) systems. Similar to optical images, radar images generated by applying a reconstruction algorithm on raw GBSAR data can also be used in object classification. The reconstruction algorithm provides an interpretable representation of the observed scene, but may also negatively influence the integrity of obtained raw data due to applied approximations. In order to quantify this effect, we compare the results of a conventional computer vision architecture, ResNet18, trained on reconstructed images versus one trained on raw data. In this process, we focus on the task of multi-label classification and describe the crucial architectural modifications that are necessary to process raw data successfully. The experiments are performed on a novel multi-object dataset RealSAR obtained using a newly developed 24 GHz (GBSAR) system where the radar images in the dataset are reconstructed using the Omega-k algorithm applied to raw data. Experimental results show that the model trained on raw data consistently outperforms the image-based model. We provide a thorough analysis of both approaches across hyperparameters related to model pretraining and the size of the training dataset. This, in conclusion, shows how processing raw data provides overall better classification accuracy, it is inherently faster since there is no need for image reconstruction and it is therefore useful tool in industrial GBSAR applications where processing speed is critical. Full article

The ground moving target (GMT) is defocused due to unknown motion parameters in synthetic aperture radar (SAR) imaging. Although the conventional Omega-K algorithm (Omega-KA) has been proven to be applicable for GMT imaging, its disadvantages are slow imaging speed, obvious sidelobe interference, and high computational complexity. To solve the above problems, a SAR-GMT imaging network is proposed based on trainable Omega-KA and sparse optimization. Specifically, we propose a two-dimensional (2-D) sparse imaging model deducted from the Omega-KA focusing process. Then, a recurrent neural network (RNN) based on an iterative optimization algorithm is built to learn the trainable parameters of Omega-KA by an off-line supervised training method, and the solving process of the sparse imaging model is mapped to each layer of the RNN. The proposed trainable Omega-KA network (Omega-KA-net) forms a new GMT imaging method that can be applied to high-quality imaging under down-sampling and a low signal to noise ratio (SNR) while saving the imaging time substantially. The experiments of simulation data and measured data demonstrate that the Omega-KA-net is superior to the conventional algorithms in terms of GMT imaging quality and time. Full article

For parallel bistatic forward-looking synthetic aperture radar (SAR) imaging, the instantaneous slant range is a double-square-root expression due to the separate transmitter-receiver system form. The hyperbolic approximation provides a feasible solution to convert the dual square-root expression into a single-square-root expression. However, some high-order terms of the range Taylor expansion have not been considered during the slant range approximation procedure in existing methods, and therefore, inaccurate phase compensation occurs. To obtain a more accurate compensation result, an improved hyperbolic approximation range form with high-order terms is proposed. Then, a modified omega-K algorithm based on the new slant range form is adopted for parallel bistatic forward-looking SAR imaging. Several simulation results validate the effectiveness of the proposed imaging algorithm. Full article

Imaging simulation of synthetic aperture radar (SAR) is one of the potential tools in the field of remote sensing. The echo signal in imaging simulation based on the point target model cannot be linked to practical scenes due to the model being a simple mathematical form, stating only the synthetic process and lacking the physical process based on electromagnetic theory. In this paper, the full-wave method is applied to include the electromagnetic effects in raw data generation, and then a refined omega-K algorithm is used to perform image focusing. According to the proposed method, the focused images not only demonstrate the difference under dielectric constant variation but also present the diversified interaction among the targets with the spacing change. In addition, the images are simulated in different observation modes and bandwidths to provide a satisfactory reference for the design of system parameters. The simulation results from the full-wave method also compare well with chamber experiments. The simulation of SAR imaging based on a full-wave method offers more complete recovery of scattering information and is useful in designing future novel SAR systems and in speckle reduction analysis. Full article

Synthetic aperture radar (SAR) equipped on the hypersonic air vehicle in near space has many advantages over the conventional airborne SAR. However, its high-speed maneuvering characteristics with curved trajectory result in serious range migration, and exacerbate the contradiction between the high resolution and wide swath. To solve this problem, this paper establishes the imaging geometrical model matched with the flight trajectory of the hypersonic platform and the multichannel azimuth sampling model based on the displaced phase center antenna (DPCA) technology. Furthermore, based on the multichannel signal reconstruction theory, a more efficient spectrum reconstruction model using discrete Fourier transform is proposed to obtain the azimuth uniform sampling data. Due to the high complexity of the slant range model, it is difficult to deduce the processing algorithm for SAR imaging. Thus, an approximate range model is derived based on the minimax criterion, and the optimal second-order approximate coefficients of cosine function are obtained using the two-population coevolutionary algorithm. On this basis, aiming at the problem that the traditional Omega-K algorithm cannot compensate the residual phase with the difficulty of Stolt mapping along the range frequency axis, this paper proposes an Exact Transfer Function (ETF) algorithm for SAR imaging, and presents a method of range division to achieve wide swath imaging. Simulation results verify the effectiveness of the ETF imaging algorithm. Full article

Ground-based synthetic aperture radar (GB-SAR) has become an important technique for remote sensing deformation monitoring. However, most of the existing GB-SAR systems realize synthetic aperture by exploiting two closely spaced horn antennas to move along a linear rail. In order to obtain higher data acquisition efficiency and a wider view angle, we introduce arc antenna array technology into the GB-SAR system, which realizes a novel kind of system: ground-based arc array SAR (GB-AA-SAR). In this paper, we analyze arc observation geometry and derive analytic expressions of sampling criteria. Then, we propose a novel wavenumber domain imaging algorithm for GB-AA-SAR, which can achieve high image reconstruction precision through numerical solutions in the wavenumber domain. The proposed algorithm can be applied in wide azimuth view angle scenarios, and the problem of azimuth mismatch caused by distance approximation in arc geometric efficient omega-k imaging can be solved successfully. Finally, we analyze the two-dimensional (2D) spatial resolution of GB-AA-SAR, and verify the effectiveness of the proposed algorithm through numerical simulation experiments. Full article

Defocus of the reconstructed image of synthetic aperture radar (SAR) occurs in the presence of the phase error. In this work, a phase error correction method is proposed for compressed sensing (CS) radar imaging based on approximated observation. The proposed method has better image focusing ability with much less memory cost, compared to the conventional approaches, due to the inherent low memory requirement of the approximated observation operator. The one-dimensional (1D) phase error correction for approximated observation-based CS-SAR imaging is first carried out and it can be conveniently applied to the cases of random-frequency waveform and linear frequency modulated (LFM) waveform without any a priori knowledge. The approximated observation operators are obtained by calculating the inverse of Omega-K and chirp scaling algorithms for random-frequency and LFM waveforms, respectively. Furthermore, the 1D phase error model is modified by incorporating a priori knowledge and then a weighted 1D phase error model is proposed, which is capable of correcting two-dimensional (2D) phase error in some cases, where the estimation can be simplified to a 1D problem. Simulation and experimental results validate the effectiveness of the proposed method in the presence of 1D phase error or weighted 1D phase error. Full article

In this work, the staggered SAR technique is employed for high-speed platform highly-squint SAR by varying the pulse repetition interval (PRI) as a linear function of range-walk. To focus the staggered SAR data more efficiently, a low-complexity modified Omega-k algorithm is proposed based on a novel method for optimal azimuth non-uniform interpolation, avoiding zero padding in range direction for recovering range cell migration (RCM) and saving in both data storage and computational load. An approximate model on continuous PRI variation with respect to sliding receive-window is employed in the proposed algorithm, leaving a residual phase error only due to the effect of a time-varying Doppler phase caused by staggered SAR. Then, azimuth non-uniform interpolation (ANI) at baseband is carried out to compensate the azimuth non-uniform sampling (ANS) effect resulting from continuous PRI variation, which is further followed by the modified Omega-k algorithm. The proposed algorithm has a significantly lower computational complexity, but with an equally effective imaging performance, as shown in our simulation results. Full article

The spatial resolution of a conventional imaging lidar system is constrained by the diffraction limit of the telescope’s aperture. The combination of the lidar and synthetic aperture (SA) processing techniques may overcome the diffraction limit and pave the way for a higher resolution air borne or space borne remote sensor. Regarding the lidar transmitting frequency modulation continuous-wave (FMCW) signal, the motion during the transmission of a sweep and the reception of the corresponding echo were expected to be one of the major problems. The given modified Omega-K algorithm takes the continuous motion into account, which can compensate for the Doppler shift induced by the continuous motion efficiently and azimuth ambiguity for the low pulse recurrence frequency limited by the tunable laser. And then, simulation of Phase Screen (PS) distorted by atmospheric turbulence following the von Karman spectrum by using Fourier Transform is implemented in order to simulate turbulence. Finally, the computer simulation shows the validity of the modified algorithm and if in the turbulence the synthetic aperture length does not exceed the similar coherence length of the atmosphere for SAIL, we can ignore the effect of the turbulence. Full article