Gerard T. Capraro

Knowledge-based radar signal and data processing: a tutorial review

Radar systems are an important component in military operations. In response to increasingly severe threats from military targets with reduced radar cross sections (RCSs), slow-moving and low-flying aircraft hidden in foliage, and in environments with large numbers of targets, knowledge-based (KB) signal and data processing techniques offer the promise of significantly improved performance of all radar systems. Radars under KB control can be deployed to utilize valuable resources such as airspace or runways more effectively and to aid human operators in carrying out their missions. As battlefield scenarios become more complex with increasing numbers of sensors and weapon systems, the challenge will be to use already available information effectively to enhance radar performance, including positioning, waveform selection, and modes of operation. KB processing fills this need and helps meet the challenge.

Multistatic radar systems signal processing

In this paper, a multistatic radar system with multiple receivers and one transmitter is analyzed. We address the rules for selecting the weights for fusing multiple receivers in order to meet pre-specified performance goals. A multistatic radar ambiguity function is used to relate different radar performance measures to system parameters such as radar geometry and radar waveforms. Simulations are used to demonstrate that different performance criteria can lead to different rules for combining the signals from multiple receivers.

Artificial intelligence applications to constant false alarm rate (CFAR) processing

False alarms are a significant problem in wide area surveillance radar. Many different constant false alarm rate (CFAR) algorithms have been developed to effectively deal with the various types of backgrounds that are encountered. However, any single algorithm is likely to be inadequate in a dynamically changing environment. The approach suggested is to intelligently select the CFAR algorithm or algorithms being executed at any given time, based upon the observed characteristics of the environment. This approach requires sensing the environment, employing the most suitable CFAR algorithm(s), and applying an appropriate multiple algorithm fusion scheme or consensus algorithm to produce a global detection decision.<<ETX>>

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

Improved STAP performance using knowledge-aided secondary data selection

Secondary data selection for estimation of the clutter covariance matrix, needed in space-time adaptive processing (STAP), is normally obtained from range rings nearby the cell under test. The assumption is that these range rings contain cells that are representative of the clutter statistics in the test cell. However, in a nonhomogeneous terrain environment, this may not be true. An innovative approach is presented, in the area of knowledge-aided STAP, which utilizes terrain data from the United States Geological Survey (USGS) to aid in the selection of secondary data cells. Results have been obtained and compared with the sliding (cell averaging symmetric) window method of secondary data selection. This comparison indicates that making use of the surveillance terrain knowledge improves STAP performance.

Waveform Diversity for Different Multistatic Radar Configurations

The multistatic ambiguity function has recently been proposed as a tool for analyzing and designing multistatic radar systems. It was demonstrated through examples that multistatic radar system performances can be improved by shaping the multistatic ambiguity function through waveform selection and adequate weighting of different receivers during pre-detection fusion. In this work we study sensor repositioning as a third way of shaping the multistatic ambiguity function. We provide some preliminary simulation results that illustrate how significant improvements in radar system performances can be achieved by combining waveform selection, receiver weighting and sensor placement strategies. These results can serve as a guideline for future multistatic fusion rule development.

Waveform diversity and sensors as robots in advanced military systems

Future military sensor systems will contain intelligent software that will dynamically assess a scenario and modify its emissions and its receivers signal and data processing to enhance performance. This capability is needed in multistatic radar situations. A multistatic radar ambiguity function is derived as a performance measure. The bistatic triangle should be taken into account when assigning weights for fusing the results of multiple receivers. By choosing the proper waveforms and their parameters the distortion experienced with difficult bistatic triangles may be neutralized. A simulation and experiment will be performed to develop the necessary rules choosing the proper waveforms for use in a sensors as robots proof of concept demonstration.

A Framework for the Analysis of Multistatic Radar Systems with Multiple Transmitters

The multistatic ambiguity function can be used as a tool for analyzing multistatic radar systems. It has been demonstrated that the multistatic ambiguity function can serve as a guideline for developing multistatic radar signal processing rules and waveform selection strategies in system configurations with a single transmitter and multiple receivers. In this work we extend the development of multistatic ambiguity function to radar systems with multiple transmitters and multiple receivers.

Signal processing and waveform selection strategies in multistatic radar systems

The multistatic ambiguity function has recently been used as a tool for analyzing multistatic radar systems. It was demonstrated that the multistatic ambiguity function with proper analytical foundation and corresponding graphic representation can serve as a guideline for developing multistatic radar signal processing rules. In this work we use this newly developed approach to combine optimal selection of weights for fusing signals from multiple receivers with waveform selection strategies in order to meet desired performance goals. We consider configurations with multiple receivers and one transmitter and demonstrate through examples that multistatic system performances can be significantly improved when selection of system parameters is based on shaping of the multistatic ambiguity function. This approach promises to be beneficial especially in scenarios with rapidly changing geometries, such as when the transmitter and/or receivers are moving, and when waveform diversity is applied, since the classical detection theory does not take into account the system geometry and waveform shape.

Using genetic algorithms for radar waveform selection

Genetic algorithms have proven to be useful tools in optimizing complex problems with large solution spaces. Radar waveform selection is a challenging problem that may benefit from the use of genetic algorithms. Furthermore, advances in the areas of waveform diversity, multistatic radars and knowledge-aided radars are making waveform selection even more challenging. As a design tool we used genetic algorithms to perform waveform selection utilizing the autocorrelation and ambiguity functions in the fitness evaluation. Monostatic, bistatic and multistatic notional examples are presented and early results indicate that genetic algorithms can provide a useful and effective tool in waveform selection for a variety of radar configurations.