Joseph R. Guerci

On Using a priori Knowledge in Space-Time Adaptive Processing

In space-time adaptive processing (STAP), the clutter covariance matrix is routinely estimated from secondary ldquotarget-freerdquo data. Because this type of data is, more often than not, rather scarce, the so-obtained estimates of the clutter covariance matrix are typically rather poor. In knowledge-aided (KA) STAP, an a priori guess of the clutter covariance matrix (e.g., derived from knowledge of the terrain probed by the radar) is available. In this note, we describe a computationally simple and fully automatic method for combining this prior guess with secondary data to obtain a theoretically optimal (in the mean-squared error sense) estimate of the clutter covariance matrix. The authors apply the proposed method to the KASSPER data set to illustrate the type of achievable performance.

Signal Waveform's Optimal-under-Restriction Design for Active Sensing

We consider Signal Waveforms Optimal-under-Restriction Design (SWORD) for active sensing. In the presence of colored interference and noise with known statistical properties, waveform optimization for active sensors such as radar can significantly increase the signal-to-interference-plus-noise ratio needed for much improved target detection. However, the so-obtained optimal waveforms can result in significant modulus variation, poor range resolution, and/or high peak sidelobe levels. To mitigate these problems, we can constrain the waveform optimization problem by restricting the sought-after waveform to be similar to a desired waveform, which is known to have, for example, constant modulus as well as reasonable range resolution and peak sidelobe level. One example of the desired waveform is the widely used linear frequency modulated waveform or chirp. We will provide a detailed solution to the constrained optimization problem and explain how it is related with the existing waveform optimization methods

CoFAR: Cognitive fully adaptive radar

A new and fully adaptive environmentally aware (cognitive) radar and signal processing architecture is introduced to meet the challenges of increasingly complex operating environments. The system features fully adaptive transmit, receive, and controller/scheduler functions. “Cognition”, i.e., learning/understanding the complete multidimensional radar channel (targets, clutter, interference, etc.) and operating environment is achieved via a sense-learn-adapt (SLA) approach, which is a radar centric application of the OOPDA (observe, orient, predict, decide, act) loop concept. Learning in turn is achieved via expert system, knowledge-aided (KA) supervised training. Lastly, a MIMO probing approach is introduced as a learning aid for signal dependent channel effects and illustrated with a MTI radar example where it is shown that a full rank estimate of the clutter covariance matrix is possible from the returns in a single range bin, thereby alleviating the so-called “sample starved” covariance estimation problem that arises in highly nonstationary environments.

Joint design and operation of shared spectrum access for radar and communications

A new theoretical foundation for the joint design and operation (JDO) of shared spectrum access for radar and communications (SSPARC) is presented. The JDO SSPARC framework entails advanced radar-comms channel estimation, along with a real-time adaptive space-time transmit and receive optimization procedure to maximize forward channel signal-to-noise while simultaneously minimizing co-channel interference. Additionally, a new expression for radar capacity is introduced that when combined with traditional communication capacity provides a unified measure of the total capacity of the combined radar-comms network. High fidelity site-specific electromagnetic radiation propagation simulations are conducted to provide a sense of the real-world potential gains achievable with JDO SSPARC as compared with non-optimized approaches.

GMTI STAP in target-rich environments: site-specific analysis

We address the problem of training data corruption in space-time adaptive processing (STAP) for ground moving target indication (GMTI) radar scenarios characterized by high densities of ground targets. A site-specific clutter simulation is used to demonstrate the impact that target signals in the training data have on STAP performance. Measured MCARM data results are presented that reveal similar performance trends as those observed in the simulations. A strategy for mitigating the deleterious effects of targets in the training data using a priori knowledge of the radar environment (e.g., locations of roads) to edit the training data is presented.

Design of adaptive detection algorithms for surveillance radar

Multidimensional adaptive signal processing algorithms are vital components of advanced radar systems, improving detection and throughput performance by exploiting signal diversity. Our focus in this paper centers on some implementation issues facing adaptive radar algorithms. To this end, we mainly discuss challenges associated with clutter heterogeneity and nonstationarity. Furthermore, we overview some algorithm-based solutions to improve detection performance.

Optimal transmission pulse shape for detection and identification with uncertain target aspect

This paper investigates the optimization of the transmission radar pulse shape to maximize either target detection or identity discrimination between the T-72 and M1 tanks under conditions of aspect uncertainty. Significant performance improvements in detection and identification are obtained using optimized transmission waveforms over that of standard chirped pulses.

Physics-based airborne GMTI radar signal processing

The Knowledge-Aided Sensor Signal Processing and Expert Reasoning (KASSPER) program aims to improve airborne ground moving target indicator (GMTI) radar performance by taking into account all available prior knowledge. One powerful piece of information is that the radar return signal is a superposition of near-ideal plane-waves. A plane-wave signal and clutter model or sampled GMTI radar data can be used to calibrate the receive array, suppress clutter, and detect moving targets. Each range gate is processed independently. Sample covariance matrices are unnecessary. The synthetic KASSPER challenge datacube is processed to demonstrate performance.

A knowledge-aided GMTI detection architecture [radar signal processing]

Space-time adaptive processing (STAP) plays an important role in ground moving target indication (GMTI). Heterogeneous clutter environments prevent STAP from achieving its theoretical performance bounds. The incorporation of a priori knowledge into the signal processing architecture holds the potential to greatly enhance detection performance by mitigating heterogeneous clutter effects. In this paper we propose one possible knowledge-aided STAP approach comprised of the following elements: a knowledge-aided prediction/estimation filter, a discrete matched filter, and a partially adaptive STAP applied to the clutter residual, assisted by knowledge-aided training. We focus our discussion on justifying the aforementioned elements and independently characterizing their performance potential. Using both measured and simulated data, we find the potential for substantial performance improvement.

Theory and application of optimum and adaptive MIMO radar

This paper develops the theory of both optimum and adaptive MIMO radar. It is shown that the optimum MIMO transmit-receiver pair consists of a coupled multidimensional eigensystem and whitening matched filter. Also introduced, is an design procedure for the clutter dominant case. After illustrating theoretical performance gains relative to conventional radar, we address the practical problem of adapting the transmit-receiver pair on-the-fly when the optimization must address multiple possible targets and/or signal (input) dependent noise (e.g., clutter). The resulting architecture combines the orthogonal transmit waveform MIMO radar with the aforementioned coupled transmit-receiver design pair with adaptive estimates for the radar channel.