Missile Terminal Guidance Trajectory Optimization with Radar Imaging Constraints

A SAR image problem for missile-borne based on the platform anomaly movement is discussed in this paper. In this paper, a approximate illumination geometry has been defined and a new image model is presented. Image formation from missile-borne synthetic aperture radar (SAR) is limited by image degradations caused by missile's non-level straight line movement. In this paper we assume that the missile is uniformity and beeline motion in a synthetic aperture time due to the missile's high speed. A different reference range is adopted to cope with the range-dependent secondary range compression (SRC) to different synthetic aperture. By this approach, the SRC error can be reduced effectively. Finally, simulation results verify the correctness and validity of the proposed methods.

In order to solve the problems that the existing high squint missile-borne synthetic aperture radar( SAR) algorithm can not deal with too high squint angle,and that the calculated amount of traditional back-projection( BP) imaging algorithm is too large,as well as that the current fast BP algorithm can not process the echo data in parallel,an improved approximate forward looking missile-borne SAR back-projection imaging algorithm which can process echo data in parallel was proposed. According to geometrical relationship of approximate forward looking missile-borne SAR,an echo signal model was established. And the imaging area was divided to equidistance strip in range along the distance direction. The image was split when merging sub aperture,and the splitting and merging did not stop until the accuracy of image met the requirements.Next the raw echo data was back projected to each strip for coherent integration,and then the missile-borne SAR image was obtained. If these strips were processed for imaging in parallel,the imaging speed would be further improved. Finally,results of simulation echo data and real echo data validate that the improved algorithm can deal with approximate forward looking scene with 86° squint angle,and it can greatly improve the computation speed compared with that of traditional BP algorithm. Moreover,the computation speed is faster than that of other improved BP algorithms by processing those strips in parallel.

The researches of imaging algorithms for synthetic aperture radar (SAR) is focused on the air- and space-borne SAR from the theory of SAR was formed . In this paper, we propose a modified frequency scaling algorithm for missile-borne SAR imaging. According to the characters of missile-borne SAR including high flight speed, non-straight movement with non-constant velocity and big squint angle, which forward a greater requirement for real time performance, precision of the motion compensation. On the base of the missile-borne SAR geometry, we described the complete derivation of the algorithm and the refined frequency scaling factors of each signal domain, the refined azimuth scaling factor can reduce azimuth time expansion and improve efficiency of azimuth processing. Then simulated the algorithm using Matlab programs in computer, analyzed the imaging result of the point target. And the simulation verified the algorithm is valid.

The SEASAT satellite system, using an onboard 23-cm-wavelength synthetic aperture radar (SAR), collected approximately 25- to 40-m-resolution radar images of the ocean in 100-km-wide swaths ranging in length from about 300 to 3000 km. Here we report results from the 18 SEASAT SAR passes during the Joint Air-Sea Interaction (JASIN) experiment conducted off the west coast of Scotland in the summer of 1978. These many SAR images, when coupled with the intensive ship, buoy, and aircraft measurements of the JASIN experiment, provide a unique opportunity to assess the ability of satellite SAR to measure ocean surface phenomena, particularly surface wave fields. In this study we use only optically processed SAR images. Although gravity waves of length approximately 80 to 300 m are often seen in SAR imagery, they are not always seen. We find that SAR resolution and wave height are important criteria for determining wave visibility and suggest that wind velocity is also. Comparisons between SAR and buoy estimates of dominant wavelength and direction (including data from several other experiments) agree to within about ±14% and ± 10°, respectively. We use a focus sharpness algorithm to resolve the 180° directional ambiguity of SAR image directional estimates. Correlation of buoy measurements of significant wave height (H 1/3) with peak signal- to-noise ratio in Fourier transforms of SAR images (r = 0.7) suggests that H 1/3 can be estimated to an accuracy of about ± 1 m. However, a similar, but weaker, correlation exists between signal-to-noise and wave length, i.e., H 1/3 and wavelength are correlated in the JASIN data set. SAR image power spectra are in rough agreement with buoy measurements of omnidirectional ocean wave height spectra Ψ(K) and correspond less closely with ocean wave slope spectra Ψ(K) However, the dominant wavenumber (Kpeak.) in SAR spectra typically falls below the dominant wavenumber of corresponding buoy spectra. Further, the slope of SAR spectra for K K Peak is typically less steep than corresponding buoy spectra Ψ(K). These differences between SAR spectra and buoy measurements of form qualitative support for the imaging mechanisms developed by Alpers, Ross, and Rufenach. Analysis algorithms based on this theory, but including empirical modifications, should substantially improve estimates of Ψ (K,θ) using SAR images.

The high wavenumber detection cut-off is determined above which the spectrum of ocean waves imaged by a synthetic aperture radar (SAR) is lost in the background noise spectrum consisting of the clutter noise associated with the Rayleigh statistics of the backscattering surface and the thermal noise originating in the SAR system itself. For given power, the maximum detection cut-off wavenumber is attained if the SAR resolution is chosen such that the clutter and noise spectra are equal at the cut-off wavenumber. Assuming a constant modulation transfer function relating the image modulation and wave slope spectra, the cut-off wavenumber is in this case proportional to (ρaρg)−1/2, where ρa and ρg represent the full bandwidth (single look) azimuthal and ground range resolutions, respectively. The same proportionality holds (but with a cut-off wavenumber increased by a factor √2) for a clutter limited cut-off, the normal operating condition of an SAR. To first order, incoherent multilook averaging has...

In this paper, feasibility study along with conceptual design of missile-borne synthetic aperture radar (SAR) has been accomplished, which can effectively improve the detection capability and the blow precision of a missile. First, SAR echo signal modeling, geometry configuration for missile-borne SAR scenario, and an appropriate image formation algorithm are presented. Then, a system block diagram along with complete discussions about different parts and system design considerations of the SAR seeker are explained. Finally, simulation of the whole system along with raw data generation considering the imaging geometry of the missile-borne SAR seeker has been performed. Experimental results show that the proposed system is capable of imaging and targeting in the complicated geometry of missile-borne SAR.

An extended nonlinear chirp scaling (NLCS) algorithm is proposed to process data of highly squinted, high-resolution, missile-borne synthetic aperture radar (SAR) diving with a constant acceleration. Due to the complex diving movement, the traditional signal model and focusing algorithm are no longer suited for missile-borne SAR signal processing. Therefore, an accurate range equation is presented, named as the equivalent hyperbolic range model (EHRM), which is more accurate and concise compared with the conventional fourth-order polynomial range equation. Based on the EHRM, a two-dimensional point target reference spectrum is derived, and an extended NLCS algorithm for missile-borne SAR image formation is developed. In the algorithm, a linear range walk correction is used to significantly remove the range-azimuth cross coupling, and an azimuth NLCS processing is adopted to solve the azimuth space variant focusing problem. Moreover, the operations of the proposed algorithm are carried out without any interpolation, thus having small computational loads. Finally, the simulation results and real-data processing results validate the proposed focusing algorithm.

More than 69,000 people died following the magnitude 8.0 Wenchuan earthquake of May 12, 2008. Bad weather hampered relief efforts, and in some cases rescuers had to trek into the disaster area on foot and search for trapped survivors by hand as roads were blocked by debris. Due to travel difficulties, spatial information needs to be extracted in the disaster area by remote sensing techniques. The main problem focused on in this paper is how to use the all-weather and all-day/night capability of Synthetic Aperture Radar (SAR) to extract primary seismic disaster information after the earthquake. Using air- and space-borne SAR images with different bands, polarizations and incidence angles, including multi-polarization X-band air-borne data, C-band polarimetric Radarsat-2, X-band TerraSAR-X with high resolution, multi-polarization X-band COSMO-SkyMed and L-band multi-polarization ALOS-PalSAR space-borne data, we perform image characteristics analysis of landslides. Obvious differences can be recognized between old and new landsides in SAR images with different bands. Multi-polarization SAR can play an important role in landslide discrimination. Two SAR images at different incidence angles do not provide much more information if the difference between the two angles is small. Landslide recognition accuracy strongly depends on the direction of view, especially for large incidence angles, in which case the characteristic difference for landslide recognition is great. There are different polarization responses between a landslide and its surroundings that can be used to recognize the landslide. Interferometric SAR images, on the other hand, do not provide good recognition capability due to temporal decorrelation and resolution. Meanwhile, information extraction of barrier lakes using different resolution and incidence angle SAR images is analyzed in this paper; small incidence angles and high resolutions improve the object recognition and information extraction of barrier lakes.

In high-resolution (HR) and very-high resolution (VHR) synthetic aperture radar (SAR) images, focus is now on the patch-oriented image categorization in contrast to the pixel-based classification in low-resolution SAR images. SAR image categorization requires the generation of a compact feature descriptor that accurately defines the content of the image patch under consideration. In this paper we propose a parametric feature descriptor generated on the complex-valued SAR image within a transformation space. The fractional Fourier transform (FrFT), has been considered to transform the image pixels of the single-look complex (SLC) SAR images in order to obtain a simpler statistical response. The real and imaginary components of the complex-valued FrFT coefficients have been modelled with generalized Gaussian distribution (GGD). The proposed feature descriptor is compared with a Wavelet-decomposition-based parametric feature descriptor; and with the FrFT-based and Gabor-filter-bank-based non-parametric feature descriptors. Categorization accuracy enhancement is demonstrated over several categories comprising of natural topologies. The experimental database consists of 2000 image patches (of size 200 × 200 pixels) extracted from SLC HR TerraSAR-X scenes.

Improving the resolution of Synthetic Aperture Radar (SAR) is a major issue that has gained significant attention recently, especially in the squint mode where obtaining high resolution is challenging. Generally speaking, the azimuth and range resolutions can be improvement by applying matched filtering for compressing the signal. Signal compression is a major part of the well-known algorithms such as the Range Doppler algorithm (RDA) and the Chirp Scaling Algorithm (CSA) by which high positioning accuracy and high resolution are obtained. The transmitted signal by SAR is most often in the form of linear frequency modulation (LFM). However, the chirp signal is always generated in the SAR azimuth direction at the receiver. The squint of observation angle of the radar makes the chirp signal non-linear and non-stationary. Consequently, this study particularly focuses on the azimuth signal compression and instead of the Matched Filter (MF) based on Fourier Transform (FT), an alternative Matched Filter is used for signal compression on time-frequency domain that is known as Matched on Slope Filter (MSF). The Matched on Slope Filter improves the effectiveness of the compression in both squint and non-squint modes. The simulation results indicate that the proposed method improves the resolution and decreases the Peak to Side Lobe Ratio (PSLR) in the target detection.

The new spaceborne very high resolution (VHR) synthetic aperture radar (SAR) sensors onboard the TerraSAR-X and COSMO-SkyMED satellites can achieve spatial resolutions in the order of 1 meter. In VHR SAR data, features from individual urban structures (like buildings) can be identified in their characteristic settings in urban settlement patterns. In this paper, we extend a methodology for the height estimation of generic man made structures from single detected VHR SAR imagery for dual-aspect data sets. The method is based on two subsequent steps. Firstly, the height of a building is estimated from the two aspects separately. Then, in a second step, the quality of each estimate is assessed. This information is used either to select one of the height estimates as final height assumption or to fuse the different height estimates to derive a new height assumption for the building. As test data we use dual-aspect VHR airborne SAR scenes and a pair of ascending and descending TerraSAR-X data, in which we analyze a set of 33 flatand gable roof buildings in a residential area in Dorsten, Germany. The airborne results show that by using an additional perpendicular flight path, some improvements in the accuracy of the height estimates can be achieved. However, combining the results from the satellite ascending and descending orbits does not yield any significant improvement compared to the estimation from a single scene.

SAR (Synthetic Aperture Radar) is an important means of earth observation. However, due to factors such as system parameters/signal processing methods/original instrument noise and windowing/etc., the resolution of SAR has theoretical limitations. Resolution is the core index of image information and interpretability. In order to improve the interpretability of SAR images, this paper uses wavelet filter and Gaussian mixture model to post-process the SAR images to improve the Image interpretability of SAR images.

High temporal resolution of synthetic aperture radar (SAR) imagery (e.g., Sentinel-1 (S1) imagery) creates new possibilities for monitoring green vegetation in urban areas and generating land-cover classification (LCC) maps. This research evaluates how different pre-processing steps of SAR imagery affect classification accuracy. Machine learning (ML) methods were applied in three different study areas: random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB). Since the presence of the speckle noise in radar imagery is inevitable, different adaptive filters were examined. Using the backscattering values of the S1 imagery, the SVM classifier achieved a mean overall accuracy (OA) of 63.14%, and a Kappa coefficient (Kappa) of 0.50. Using the SVM classifier with a Lee filter with a window size of 5×5 (Lee5) for speckle reduction, mean values of 73.86% and 0.64 for OA and Kappa were achieved, respectively. An additional increase in the LCC was obtained with texture features calculated from a grey-level co-occurrence matrix (GLCM). The highest classification accuracy obtained for the extracted GLCM texture features using the SVM classifier, and Lee5 filter was 78.32% and 0.69 for the mean OA and Kappa values, respectively. This study improved LCC with an evaluation of various radiometric and texture features and confirmed the ability to apply an SVM classifier. For the supervised classification, the SVM method outperformed the RF and XGB methods, although the highest computational time was needed for the SVM, whereas XGB performed the fastest. These results suggest pre-processing steps of the SAR imagery for green infrastructure mapping in urban areas. Future research should address the use of multitemporal SAR data along with the pre-processing steps and ML algorithms described in this research.

In this paper, the application of direct parameter optimization method to reentry vehicle three-dimension skipping trajectory optimization is introduced. Firstly, the model of trajectory optimization control problem to reentry vehicle skipping trajectory is established, while motion equation of the state variables is a three degree-of-freedom model. Performance is selected to minimize the sum of heat, overload and dynamic pressure. The control variables are angle-of-attack and bank angle. During the flight, reentry vehicle is subjected to the heat rate and overload constraints. Terminal state variables constraints are path angle, altitude and latitude constraints. Then, the optimal control problem is discretized into nonlinear programming problem (NLP) using direct parameter optimization method. The control variables are selected as optimal parameters at all nodes. Parameter optimization problem is solved using POWELL algorithm. The simulation result shows that the optimal solutions of skipping trajectory optimization problem are in real-time using direct parameter optimization method. Therefore, direct parameter optimization method is a viable approach to the reentry vehicle skipping trajectory optimization problem.

A synthetic aperture radar (SAR) can produce high-resolution two-dimensional images of mapped areas. The SAR comprises a pulsed transmitter, an antenna, and a phase-coherent receiver. The SAR is borne by a constant velocity vehicle such as an aircraft or satellite, with the antenna beam axis oriented obliquely to the velocity vector. The image plane is defined by the velocity vector and antenna beam axis. The image orthogonal coordinates are range and cross range (azimuth). The amplitude and phase of the received signals are collected for the duration of an integration time after which the signal is processed. High range resolution is achieved by the use of wide bandwidth transmitted pulses. High azimuth resolution is achieved by focusing, with a signal processing technique, an extremely long antenna that is synthesized from the coherent phase history. The pulse repetition frequency of the SAR is constrained within bounds established by the geometry and signal ambiguity limits. SAR operation requires relative motion between radar and target. Nominal velocity values are assumed for signal processing and measurable deviations are used for error compensation. Residual uncertainties and high-order derivatives of the velocity which are difficult to compensate may cause image smearing, defocusing, and increased image sidelobes. The SAR transforms the ocean surface into numerous small cells, each with dimensions of range and azimuth resolution. An image of a cell can be produced provided the radar cross section of the cell is sufficiently large and the cell phase history is deterministic. Ocean waves evidently move sufficiently uniformly to produce SAR images which correlate well with optical photographs and visual observations. The relationship between SAR images and oceanic physical features is not completely understood, and more analyses and investigations are desired.