Doppler Center Frequency Research Articles

Simulation Analysis of the Geometric Positioning Accuracy for MEO- and HEO-SAR Satellites

Due to the long synthetic aperture time, the large squint angle, and the large imaging width of medium Earth orbit (MEO) and high Earth orbit (HEO) SAR satellites, it is difficult to simulate the geometric positioning accuracy of MEO- and HEO-SAR satellites through the SAR image simulation methods. In this paper, one non-zero Doppler simulation method of geometric positioning accuracy was proposed without simulating SAR images. In order to simulate the geometric positioning accuracy under different errors and imaging observation conditions, the virtual simulation geometric model was constructed by the simulated satellite ephemeris and the coordinates of ground control points (GCPs). On this basis, one geometric accuracy simulation method based on mean value compensation was proposed to simulate the geometric positioning accuracy with GCPs. The experimental results showed that the impact of Doppler center frequency error and velocity error of MEO- and HEO-SAR satellites on geometric positioning accuracy is significant compared with the LEO-SAR satellites, and the maximum error they affect can reach about 1597 m. In addition, the geometric positioning accuracies of MEO- and HEO-SAR satellites with GCPs can be achieved to 1~10 m and 7~29 m, respectively.

D-MFPN: A Doppler Feature Matrix Fused with a Multilayer Feature Pyramid Network for SAR Ship Detection

Ship detection from synthetic aperture radar (SAR) images has become a major research field in recent years. It plays a major role in monitoring the ocean, marine rescue activities, and marine safety warnings. However, there are still some factors that restrict further improvements in detecting performance, e.g., multi-scale ship transformation and unfocused images caused by motion. In order to resolve these issues, in this paper, a doppler feature matrix fused with a multi-layer feature pyramid network (D-MFPN) is proposed for SAR ship detection. The D-MFPN takes single-look complex image data as input and consists of two branches: the image branch designs a multi-layer feature pyramid network to enhance the positioning capacity for large ships combined with an attention module to refine the feature map’s expressiveness, and the doppler branch aims to build a feature matrix that characterizes the ship’s motion state by estimating the doppler center frequency and frequency modulation rate offset. To confirm the validity of each branch, individual ablation experiments are conducted. The experimental results on the Gaofen-3 satellite ship dataset illustrate the D-MFPN’s optimal performance in defocused ship detection tasks compared with six other competitive convolutional neural network (CNN)-based SAR ship detectors. Its satisfactory results demonstrate the application value of the deep-learning model fused with doppler features in the field of SAR ship detection.

Sparse Reconstruction of SFCWSAR Based on an Approximate Observation Operator

The traditional compressed sensing SFCWSAR (Stepped Frequency Continuous Wave Synthetic Aperture Radar) sparse reconstruction algorithm consumes a lot of computer memory and cannot compensate the range migration in the same pulse group. Based on this, this paper proposes a SFCWSAR sparse reconstruction algorithm based on an approximate observation operator. First, the algorithm replaces the accurate observation operator with the approximate observation operator, which greatly reduces the computer memory consumption while the algorithm is running and realizes the compensation of the range migration in the SFCWSAR pulse group. Furthermore, the SFCWSAR sub-band echo data under full sampling conditions are used to modify the important parameter of the Doppler center frequency of the approximate observation operator, which significantly improves the reconstruction accuracy of the scene. The SFCWSAR data show that, compared with the conventional sparse autofocus algorithm, the proposed algorithm takes less memory and can reconstruct scenes with high accuracy.

An Azimuth Antenna Pattern Estimation Method Based on Doppler Spectrum in SAR Ocean Images.

In synthetic aperture radar (SAR) ocean remote sensing, it is very difficult to estimate an accurate azimuth antenna pattern (AAP) from low-scattering SAR images without strong scattering targets. Therefore, an azimuth antenna pattern estimation method based on Doppler spectrum in SAR ocean images is proposed in this paper. In order to preserve the complete AAP information, an azimuth unweighted matched filter is used to re-image the SAR raw data in the proposed method. Then, the shape factor of AAP can be obtained by linear statistics of the relationship between Doppler center and edge frequency spectrum in Doppler spectrum of each distance gate. In addition, the impact of the uniformity and signal-to-noise ratio of SAR ocean images on the estimation results are also analyzed by simulation. Finally, the feasibility of proposed method is verified by data from ERS-2 (European remote sensing satellite (ERS) was the European Space Agency’s first Earth-observing satellite). Experimental results show that the AAP estimated by proposed method has a good estimation result.

An Efficient Method of Doppler Parameter Estimation in the Time–Frequency Domain for a Moving Object From TerraSAR-X Data

A method measuring Doppler parameters by joint time-frequency analysis is presented. Two-dimensional velocity retrieval can be achieved by estimation of the Doppler center frequency and Doppler frequency rate for the range and azimuth velocity components, respectively. The Doppler spectrum along a Doppler phase history line in the time-frequency domain was reconstructed and projected onto the frequency and time dimensions. The peak of the frequency axis projected spectrum corresponds to the Doppler center frequency, while that of the time axis indicates an azimuth time of closest approach. The Doppler frequency rate is obtained by measuring the slope of the Doppler spectrum in the time-frequency domain. Simulation using TerraSAR-X and Envisat ASAR parameters indicated that the velocity errors were less than 1 m/s or 5% for moving objects with a velocity higher than 3 m/s. While the measurement of Doppler center frequency was reliable over the entire velocity range, errors in Doppler frequency rate became large if the velocity was slower than 3 m/s (or 10.8 km/h). Range migration correction plays a key role for the precision of Doppler parameter estimation. An experiment using TerraSAR-X and truck-mounted corner reflectors validated the measurement accuracy of the approach. The absolute and percent errors of the range velocity (49.6 km/h) were 1.4 km/h and 2.8%, respectively, and the azimuth velocity (23.8 km/h) measurement was comparatively accurate, with a percent error of 1.8% under an assumption of zero acceleration. To apply the method to single-look complex data, the full Doppler bandwidth must be preserved.

The design of an onboard digital Doppler processor for a spaceborne scatterometer

A digital Doppler processor, which will permit the Doppler center frequency of the measurement cell bandwidths to be adjusted to compensate for the effects of the Earth's rotation, will be used in the next NASA spaceborne scatterometer known as NSCAT. The authors describe the design and genesis of the NSCAT digital Doppler processor and discuss the performance tradeoff issues that were evaluated during the design phase. In this FFT (fast Fourier transform)-based technique, computation of the adjustment to the cell center frequencies will be done onboard using an approximate expression for the Doppler shift of the cell center versus orbit time. This technique also permits modification of the parameters used to locate the radar-backscatter-coefficient measurements cells by ground command in response to orbit changes. >