Peter Zulch

MIMO Phased-Array for SMTI Radar

Waveform diversity techniques for radar have gained considerable interest over the past several years. Novel radar waveforms have been proposed to improve detection performance and metric accuracy (i.e., angle estimation performance). This paper explores the potential for using a waveform diversity technique known as multiple input, multiple output (MIMO) radar to improve the detection performance of slow moving surface targets from a moving radar platform. The MIMO radar system achieves superior performance by transmitting unique uncorrelated waveforms from each antenna subaperture as opposed to the traditional approach of transmitting a single coherent waveform across the entire aperture. The results show that the radar system minimum detectable velocity (MDV) can be reduced by exploiting the ability of a MIMO system to effectively increase the radar antenna aperture.

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.

The Earth rotation effect on a LEO L-band GMTI SBR and mitigation strategies

Space based radars (SBR) have been used to accomplish a number of civilian and military missions. Most recently, SBR concepts have been considered to perform ground moving target indication (GMTI) radar modes. Unlike airborne surveillance platforms, SBR clutter returns are affected by the high satellite velocity and Earth rotation. The phenomenology of the Earths rotation, and its impact on clutter Doppler returns, are discussed for a Low Earth Orbit (LEO) L-band radar concept. The USA Air Forces Research Laboratory Space Time Adaptive Processing Tool (RLSTAP) high fidelity radar modeling tool is used to provide simulated data in order to demonstrate the Earth rotation effects, and resulting clutter rejection impact on slow moving target detection.

A new complementary waveform technique for radar signals

A new phase coding technique for radar signals is introduced which uses novel complementary waveforms constructed to have optimal sidelobe performance. The waveforms are constructed using a modification of the Prometheus orthonormal set (PONS) technique. An advantage of a PONS matrix is that it allows for many complementary pairs of waveforms to choose from as well as allowing for multiple pairs to be used simultaneously. It is shown that sets of waveforms which are complementary in quartets can also be applied for more flexibility. Results showing improved ambiguity properties versus other radar waveform coding techniques are given.

RLSTAP algorithm development tool for analysis of advanced signal processing techniques

Space time adaptive processing (STAP) has been identified as a key enabling technology for the detection of small targets in the presence of severe clutter and jamming. It is therefore important to develop simulation and analysis tools which accurately model advanced STAP architectures in realistic operational environments and to evaluate evolving system technologies prior to large development programs. This paper describes a user-friendly simulation capability, developed under the ARPA Mountaintop program, which can be used to assess the performance of evolving adaptive processing technologies for advanced airborne surveillance systems.

A multistage nonhomogeneity detector

This paper introduces a new space-time adaptive processing architecture for nonhomogeneous radar environments that exploits the multiresolution analysis of the multistage Weiner filter by interleaving the generalized inner-product. The simplest form of this architecture results in performing the generalized inner-product within the generalized sidelobe canceller. In general, the new architecture provides a signal-dependent generalized inner-product which intends to detect only those inhomogeneities that degrade the estimation of the colored noise and interference that passes through the steering vector. The filter structure and interpretation of the multistage Weiner filter provide the framework for this innovation. Nonhomogeneity detection and adaptive processing performance are assessed by using the Monte Carlo trials of simulated data with known inhomogeneities.

A new constrained joint-domain localized approach for airborne radars

Critical issues associated with the application of multi-dimensional adaptive filtering including space-time adaptive processing (STAP) to real-world radar systems are considered. In particular, the design of transform domain localized techniques are examined from the perspective of receiver beam position and Doppler filter selection relative to mainlobe clutter as well as target returns. In particular, asymmetry in the selection of auxiliary beams and the effects of spatial tapering are shown to offer dramatic improvements in signal to interference ratio for targets with low Doppler.

Performance Metric Issues for Space Time Adaptive Processing Methods

Space time adaptive processing was introduced to the radar community in the 1970s as a multi-dimensional filtering method to discriminate moving targets from clutter when observed from an air-borne/space-borne multi-channel phased array radar. Numerous variants of this filtering technique have been developed and are often compared to some baseline performance with respect to output signal to interference plus noise ratio or minimum mean square error. Under clairvoyant conditions these metrics have a direct relation to probability of detection. However under non-clairvoyant conditions, such as with recorded data, probability of detection is not measured directly and secondary metrics, such as signal to interference plus noise ratio and minimum mean square error, are used to measure performance. This paper attempts to show a case where using a metric such as minimum mean square error can indicate erroneous favorable performance.

Independent sample mean squared error for adaptive detection statistics

This paper introduces the independent sample mean square error (ISMSE) as an important factor in computing the adaptive matched filter (AMF) constant false-alarm rate (CFAR) detection statistic. It has been shown that the ISMSE is a cross-validation metric that is useful for determining the optimum rank of the multistage Wiener filter (MWF) subspace. This is because it gives a more accurate estimate of the true ensemble mean squared error (MSE) provided by a Wiener filter, as compared to the standard least squares sample mean squared error (SMSE). The innovation described herein directly exploits this improved MSE estimator within the AMF calculation. Together with the reduced rank MWF solution, this results in improved detection performance in low sample support environments. Receiver operating characteristic (ROC) curves are generated using Monte Carlo simulations and used to assess performance against the full rank approach

Multi-spectral detection and tracking in cluttered urban environments

Automatic detection and tracking of moving targets in full motion video (FMV) from aerial imaging systems has significant interest in the defense and security community. However often times performance is degraded in a given spectral band due to environmental conditions and poor target response in a given band. The overall goal of this work is to increase the probability of detection and track association in cluttered urban environments while simultaneously suppressing false alarms by fusing the detection results and features from different spectral bands. We use a Gaussian mixture model (GMM) to detect background pixels, and define potential targets as being in regions that are found to be non-background. Detections from each spectral band are fused to form multi-spectral target candidates. Detected target candidates are associated with targets from a tracking database by matching features from the scale-invariant feature transform (SIFT). We create tracking profiles consisting of location history and vector velocity history for all targets in the scene. This algorithm was evaluated with synthetically generated datasets from the Digital Imaging and Remote Sensing Image Generation (DIRSIG) software model producing visible, near infrared, mid-wave infrared and long-wave infrared FMV that include moving vehicles in an urban environment. The proposed fusion algorithm provides a detection rate over 82%, while decreasing incorrect associations in cluttered areas such as intersections or partial occlusions where a portion of the vehicle is hidden from sensor view. This paper will describe the approach and demonstrate the performance with simulated DIRSIG FMV data.