This paper considers synthetic-aperture radar and other synthetic-aperture imaging systems in which a backscattered wave is measured from a variety of locations. We consider the case in which the reflections take place on a known surface, but the reflection at a particular point depends on frequency. The ability to determine a frequency-dependent reflectivity function may aid in the characterization of materials.

A new technique is developed for phase adjustment in ISAR imaging. The adjustment phase is found by iteratively solving an equation, which is derived by minimizing the entropy of the image. This technique can be used to estimate adjustment phases of any form. Moreover, the optimization method used in this technique is computationally more efficient than trial-and-error methods.

Repeat pass radar interferometry has developed into a wide ranging geodetic and change mapping tool from space. For large areas of the Earth, and for numerous applications that demand more persistent monitoring, the true potential of the repeat pass technique remains largely untapped. While the research community has made enormous strides in extracting as much information as possible from existing data sets, fundamentally new observing systems are needed for breakthroughs in a number of areas. Repeat pass interferometry techniques applied on missions to the terrestrial planets, Earth?s moon and the icy moons of Jupiter can reveal new insights in to the history of the surface and near subsurface.

Basic turntable spotlight inverse synthetic aperture radar (ISAR) systems that employ stepped frequency waveforms implement image formation algorithms based on the premise that data collection over uniform frequency and angle steps results in a rectangular sampling of the image spatial frequency (k-space) domain. As a result, a simple image formation algorithm implementing only a computationally efficient inverse 2-D discrete Fourier transform (DFT) may be realized. However, this approach imposes limitations on resolution and/or scene size since the conventional data collection procedure actually results in a polar sampling of k-space, invalidating the rectangular grid assumption. This paper introduces a new data collection scheme using stepped frequency waveforms that are non-uniformly stepped in time and frequency so as to collect sample points according to the desired k-space shape. This procedure allows the use of a single inverse 2-D DFT as the image formation algorithm, thereby reducing traditional constraints on resolution and scene size while maintaining good image focus and reducing computational complexity.

This paper describes a processing algorithm based on polynomial phase modeling scheme that increases visibility of maneuvering targets in HF over-the-horizon radar (OTHR). For the presence of the echo backscattered by the target that has significantly varying radial velocity within a coherent integration time (CIT), it is difficult for traditional coherent processing to centralize energy and peak in Doppler spectrum. Due to the polynomial property of the phase of maneuvering target radar echo, a polynomial phase signal (PPS) is introduced to model the complex Doppler variation of maneuvering targets. As an effective method to estimate the parameters of PPS, high-order ambiguity function (HAF) based algorithm is applied. And a compensation process follows to eliminate the coherent processing loss (CIL) caused by irregular motion of targets. The experimental results are given to illustrate the validity and efficiency of the proposed method.

Some aspects of detection and tracking of ballistic targets are analysed in this paper. In particular, the RCS model versus aspect angle is obtained for a notional target starting from its CAD model. Subsequently, the kinematic model of the ballistic target is derived; it includes the main forces acting on it, namely: the thrust, the drag and the gravity. On the basis of these two models it is possible to determine the detection probability of a notional L-band radar during the target flight and to build up an interactive multiple model (IMM) for target tracking. The performance evaluation of the design IMM tracking algorithm is obtained via Monte Carlo simulation. In particular, highly manoeuvring aircraft and ballistic target in the boost phase are used to check the capability of the IMM.

A Kalman filter-based approach to fusing track data from two separate phased array radar sensors is developed and applied to a select ICBM case to demonstrate the potential enhancement of position and velocity estimates over a single radar. When compared to a theoretical assessment based on steady state filter performance, the Kalman filter approach yielded performance enhancements within 7% of theoretical prediction. The theoretical assessment indicated a 33% improvement in position accuracy and a 29% improvement in velocity accuracy for an assumed bias error in both radars. The simulation yielded a 29% improvement in position accuracy and a 22% improvement in velocity accuracy with the same bias assumption. The improvement was computed relative to the radar with twice the beamwidth and the same sensitivity as the second ?fused? radar. The two radars were assumed to be collocated at the terminal area of ICBM flight. The simulated estimates were obtained by a Monte Carlo approach which averaged the results of simulation runs of fused and single radar position and velocity estimates of a known ICBM trajectory. The trajectory was generated by solving the matrix differential equations of motion for both the exo and endoatmospheric portions of the ICBM flight. MATHCAD-based simulations incorporating trajectory generation and track data fusion algorithms were developed to perform the described analyses.

This paper presented effect of the common process noise on track statistical distance and performance of state estimation fusion. Because of great computational load on cross-covariance matrix produced by process noise, indeed, which is a half of total computational load, thus real-time computation of cross-covariance matrix is avoided in system implement. First, computational load on cross-covariance matrix, track statistical distance, and state estimation fusion are estimated and compared. And effect of the common process noise on track statistical distance and performance of state estimation fusion are simulated. Simulation results show that in the case of less process noise, cross-covariance matrix is neglected, whereas it cannot.