Implementing digital terrain data in knowledge-aided space-time adaptive processing

Many practical problems arise when implementing digital terrain data in airborne knowledge-aided (KA) space-time adaptive processing (STAP). This paper addresses these issues and presents solutions with numerical implementations. In particular, using digital land classification data and digital elevation data, techniques are developed for registering these data with radar return signals, correcting for Doppler and spatial misalignments, adjusting for antenna gain, characterizing clutter patches for secondary data selection, and ensuring independent secondary data samples. These techniques are applied to select secondary data for a single-bin post-Doppler STAP algorithm using multi-channel airborne radar measurement (MCARM) program data. Results with the KA approach are compared with those obtained using the standard sliding window method for choosing secondary data. These results illustrate the benefits of using terrain information, a priori data about the radar, and the importance of statistical independence when selecting secondary data for improving STAP performance     

Noise factor and antenna gains in the signal/noise equation for over-the-horizon radar

A recommended form of the signal-to-noise equation that includes both internal and external system noise and signal/noise processing losses is discussed. The recommended form conforms to the internationally accepted definition of system operating noise factor but is extended to include signal/noise processing. The predetection signal-to-noise ratio (SNR) of a radar or communication system is proportional to the power gain of the transmit antenna and the directive gain of the receive antenna, and is inversely proportional to the operating noise factor of the receiving system. The operating noise factor is approximately equal to the product of the external noise factor and the signal/noise processing factor when the system is external noise limited, as is usually the case for over-the-horizon (OTH) radar.<>     

Noise factor of receiving system with arbitrary antenna impedancemismatch

The system operating noise factor of a receiving system is modeled for arbitrary impedance mismatch of the antenna to the transmission line feeding the receiver. The effect of this mismatch on the noise factors of the transmission line and the receiver is considered. The stochastic nature of the external noise factor is considered. The amount of mismatch that can be tolerated before the internal system noise factor exceeds the external noise factor is determined. Numerical results are presented for a VHF-FM radio receiving system with an electrically short monopole antenna. It is noted that a large impedance mismatch at the antenna-transmission-line interface of a radio receiving system can cause a significant increase in the system internal noise factor (more than a 50-dB increase for a voltage reflection coefficient of 0.999)     

Analog clutter cancellation algorithms for dynamic range reduction

A technique that effectively reduces the dynamic range of the input signal in a radar receiver prior to digitization is presented. The dynamic range reduction is accomplished through a process that predicts the next radar return signal from the previous return signals, generates a replica waveform, and subtracts this replica waveform from the radar return signal prior to digitization. This process allows the radar return signal to be digitized without distortion by an analog-to-digital converter (ADC) having a limited dynamic range. The full dynamic range of the radar return signal is then restored by adding the replica waveform to the ADC output. Test and evaluation results using both synthetic and recorded radar data demonstrate in excess of a 30-dB reduction in the dynamic range of the signal at the ADC input when strong clutter is present     

Design, implementation and evaluation of parallel pipelined STAP onparallel computers

Performance results are presented for the design and implementation of parallel pipelined space-time adaptive processing (STAP) algorithms on parallel computers. In particular, the issues involved in parallelization, our approach to parallelization, and performance results on an Intel Paragon are described. The process of developing software for such an application on parallel computers when latency and throughput are both considered together is discussed and tradeoffs considered with respect to inter and intratask communication and data redistribution are presented. The results show that not only scalable performance was achieved for individual component tasks of STAP but linear speedups were obtained for the integrated task performance, both for latency as well as throughput. Results are presented for up to 236 compute nodes (limited by the machine size available to us). Another interesting observation made from the implementation results is that performance improvement due to the assignment of additional processors to one task can improve the performance of other tasks without any increase in the number of processors assigned to them. Normally, this cannot be predicted by theoretical analysis     

Non-Gaussian random vector identification using sphericallyinvariant random processes

With the modeling of non-Gaussian radar clutter in mind, elegant and tractable techniques are presented for characterizing the probability density function (PDF) of a correlated non-Gaussian radar vector. The need for a library of multivariable correlated non-Gaussian PDFs in order to characterize various clutter scenarios is discussed. Specifically,. the theory of spherically invariant random processes (SIRPs) is examined in detail. Approaches based on the marginal envelope PDF and the marginal characteristic function have been used to obtain several multivariate non-Gaussian PDFs. An important result providing the PDF of the quadratic form of a spherically invariant random vector (SIRV) is presented. This result enables the problem of distributed identification of a SIRV to be addressed     

Binary integration of fluctuating targets

The detection performance of a binary integrator (M-out-of-N detector) against nonfluctuating, slowly fluctuating, and quickly fluctuating targets is given. Since the solution for the slowly fluctuating target is numerically intensive, a simpler approximate solution is developed. This approximation is very accurate and is valid even when the noise power varies from pulse to pulse within a single antenna scan     

Computer generation of correlated non-Gaussian radar clutter

We develop computer simulation procedures which enable us to generate any correlated non-Gaussian radar clutter that can be modeled as a spherically invariant random process (SIRP). In most cases, when the clutter is a correlated non-Gaussian random process, performance of the optimal radar signal processor cannot be evaluated analytically. Therefore, in order to evaluate such processors, there is a need for efficient computer simulation of the clutter. We present two canonical simulation procedures for the generation of correlated non-Gaussian clutter. A new approach for the goodness-of-fit test is proposed in order to assess the performance of the simulation procedure