Hughes Aircraft Company

Raytheon Company

Hughes Electronics Corporation

Ralph E Hudson

Robert T Frankot

Yoji G Niho

Jon H Sherman

John P Kilkelly

Helen L Sun

Ernest C Chen

Howard S Nussbaum

David D Lynch

Tammy L Flanders

Stanley O Aks

Robert H Latter

Peter P Bogdanovic

Gene W Zeoli

George H Senge

Provided is a non-statistical method for compressing and decompressing complex SAR data derived from reflected energy. The method includes selecting a first FFT to provide a target ratio of pixel spacing to resolution. A second FFT is then selected which is smaller than the first FFT. The data is zero-padded to fill the second FFT and transformed to provide at least one transfer frequency. This transfer frequency is then transferred to the at least one remote site. At the remote site the second FFT is inverted to restore the data from the received transfer frequency. The restored data is then zero-padded again to fill the first FFT. The first FFT is then used to transform the zero-padded restored data to provide a data set of points with the target ratio of pixel spacing to resolution.

Non-statistical method for compressing and decompressing complex SAR data

An inverse synthetic array radar (ISAR) system provides for improving the resolution of an ISAR image by providing compensation for non-uniformity in the magnitude of the angular velocity of a rotating target as the target rotates to generate the synthetic-aperture-angle, and enables use of a larger synthetic-aperture angle, without compromising ISAR image quality with respect to smear. The preferred embodiment records sampled data signals to generate a collected-data matrix indexed in each of two dimensions on the basis of uniform increments of time, and performs data processing to produce a translated data matrix indexed in a dimension on the basis of uniform increments of synthetic-aperture angle. Further processing of the translated data matrix produces data in a buffer for controlling a display device for the ISAR image.

Inverse synthetic array radar system and method

A method for automatically selecting subareas from reference image data such that registration accuracy is optimized between images. Optimal subarea selection reduces the on-line computation and reference data storage required for multi-subarea correlation or, alteratively, improves its effectiveness. Example results for a synthetic aperture radar (SAR) image are described. The results indicate that the automatic subarea selection method of the present invention reduces on-line computation by a factor of 2 to 3 (relative to random subarea selection) without degradation in accuracy. The present selection method minimizes the predicted total mean squared registration error (MSE). The total MSE is predicted in terms of the position and predicted measurement covariance (derived from local image statistics) of each candidate subarea. Combinatorial optimization procedures select a predetermined number of subareas to minimize total MSE. The present method jointly considers the quality and position for each local measurement, optimally handles any order polynomial model, and includes bounds on the uncertainty of registration parameters.

Automatic subarea selection for image registration

Apparatus including a multi-scale adaptive filter for smoothing interferometric SAR (IFSAR) data in areas of low signal-to-noise ratio (SNR) and/or coherence while preserving resolution in areas of high SNR/coherence. The multi-scale adaptive filter uses simple combinations of multiple linear filters applied to a complex interferogram. The multi-scale adaptive filter is computationally efficient and lends itself to parallel implementation. A pyramid architecture comprising a plurality of cascaded stages is employed which reduces the computational load and memory required for implementation of the processing algorithm. The multi-scale adaptive filter implements a processing algorithm that may be applied to standard IFSAR data. Its input is a complex interferogram (the conjugate product of two complex images) and its output is a filtered interferogram (A) which is passed to an information extraction processor, that extracts a terrain elevation map, for example. The adaptive filter incorporates linear filters at two or more scales (i.e. filter impulse response widths) whose outputs are combined in a data-dependent manner. The combination rules result in an output interferogram (A) that is filtered heavily in areas of low coherence and receives little or no filtering in areas of high coherence. The combination rules use a coherence measure that is a simple nonlinear function of the linear filter outputs themselves.

Multi-scale adaptive filter for interferometric SAR data

A terrain height radar system and processing method comprising a high resolution synthetic aperture radar (SAR) mounted on an air vehicle and a SAR signal processor containing a signal processing algorithm or method for computing terrain height and radar backscatter power. The system contains motion sensing and navigation functions that also provide data to the signal processor to provide motion compensation. Signal processing algorithms in the method compensate for planar motion of the air vehicle for variations of terrain height in the field of view. The algorithms also compensate for nonplanar motion of the radar, and for scatterers in or very near to a reference plane in the field of view. The algorithms exploit defocusing due to displacement from the reference plane to estimate the terrain height above the reference plane. The algorithm is computationally efficient because the bulk of the radar signal processing is common to both the SAR function and the terrain height estimation function. The following processing method is implemented. The synthetic array radar is operated to produce out-of-plane motion and generating radar return signals derived therefrom. The radar return signals are processed to compensate for relative motion, generate ground plane polar format data, generate range compressed data, and generate autofocused data. Azimuth compressed data is generated by focusing uncompressed azimuth data for each of many discrete terrain height reference levels, compressing the focused data using a weighted FFT, temporarily recording the magnitude of each pixel, storing separate image magnitude values corresponding to each terrain height reference level for each pixel and outputting the largest of these magnitudes for each pixel as a radar cross section value of that pixel, and outputting the height reference level at which that maximum occurred as the height of that pixel. Interpolation may be used to refine the height estimate. The strong scatterer signal is then canceled by estimating the amplitude phase height and azimuth of the strong scatterer, using these parameters to determine a contribution in the uncompressed data set due to the strong scatterer, subtracting this contribution from the uncompressed data, and repeating the amplitude phase height and azimuth height estimating step to provide for for improved accuracy.

Terrain height radar

A method and autofocus processor that is adapted to automatically correct focus phase errors associated with synthetic array radar signals. The method comprises processing the synthetic array radar signals to produce a SAR image; identifying and locating potential targets contained in the SAR image; storing the range bin and azimuth location of the target in a target list; bandpass filtering the SAR signals associated with the target scatterer to remove the interference therefrom; forming the pulse pair product of the phase history samples from each reference target to produce a differential phase function; integrating the differential phase function over all reference targets to provide the averaged differential phase history associated therewith; interpolating the averaged differential phase function to restore the original time scale and number of samples; and computing the focus error from interpolated differential phase history. The focus error is estimated as a weighted average of the phase history of the detected scatterers. The autofocus processor automatically finds targets and combines data from multiple scatterers effectively and efficiently. The autofocus processor extends the range and resolution limits of SAR systems, and enables the effective use of a SAR sensor with less expensive motion compensation function. The autofocus processor also decouples the SAR sensor (including motion sensors) from its SAR processor, so that the autofocus processor produces better images, no matter what sensor is employed.

Multiple discrete autofocus

The method and system for reducing phase error of phased array radar beam steering controllers having digitally controlled phase shifters includes the monitoring of individual digitally controlled phase shifter elements, determining an additive phase correction to reduce the number of failed phase shifter elements, determining whether said additive phase correction is achievable by comparing the stuck bit state at each said failed changers element with said additive phase correction and adjusting the phase commands to the nearest values which can be achieved if the additive phase correction is unachievable.

Method and system for reducing phase error in a phased array radar beam

A technique is disclosed for encoding SAR image data to achieve data compression. In the image encoding stage, the SAR image is transformed into a list of high reflectivity radar discretes and a small array of frequency filters. In the target list, the location data and intensity levels above the local average background clutter are tabulated for a predetermined number of the highest intensity radar discretes. The array of frequency filters is divided into three zones; the inner, middle, and higher frequency zones relative to the d.c. filter. Only the inner and middle zones of filters are retained and the outer filters are discarded, thus acheiving the desired data reduction. The inner zone filters are quantized to a higher level of precision than the middle zone of filters. The saturation levels of the filters are determined adaptively. In the decoding stage, the original SAR image is reconstructed from the radar discrete list and the small array of frequency filters.

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Los Angeles, CA, United States of America

Ralph E Hudson