Space-time Adaptive Processing Applications Research Articles

Slow-Time FDA-MIMO Technique With Application to STAP Radar

Unlike the conventional phased array, the frequency-diverse array (FDA) employs a tiny frequency increment across the array elements, which is capable of providing range–angle–time-dependent beampattern, and thereby offers potential benefits in target localization and interference mitigation. Reported literature on FDA mostly focuses on its intrapulse (fast-time) property and ignores the interpulse (slow-time) feature induced by frequency offset. In this article, incorporating the neglected inherent slow-time coding feature, a novel slow-time FDA multiple-input multiple-output (FDA-MIMO) technique is proposed for the space-time adaptive processing (STAP) radar. The new coding scheme can separate transmitting (Tx) signals via slow-time Doppler filtering, and the problem of signal aliasing is tackled by appropriately designing the slow-time codes. At the receiving (Rx) side, two Rx schemes are devised for recovering range-dependent Tx degrees-of-freedom. Moreover, the relevant clutter rank and the rule for code design are derived in detail. The outstanding merits of the proposed slow-time FDA-MIMO STAP radar consist of the following: There is no specific restriction on the probing waveform; the negative effect of time-variant pattern is perfectly eliminated; and both the target localization accuracy and clutter suppression performance are superior over the state-of-the-art FDA radar systems. Numerical results corroborate the superiorities of the proposed waveform strategy for STAP application.

Robust STAP Detection Based on Volume Cross-Correlation Function in Heterogeneous Environments

The performance of moving target detection in heterogeneous environments with the traditional space-time adaptive processing (STAP) may degrade when the real clutter environments deviate from the prior assumption on the clutter distribution. In this letter, a new detector for STAP applications based on volume cross-correlation function (VCF), namely VCF-STAP, is proposed to achieve robust performance of moving target detection in heterogeneous environments. In the new VCF-STAP, the VCF is used to form a distance measure between the sample signal subspace and the target subspace without modeling the clutter distribution. Then, a new robust STAP detection statistic is constructed using this distance measure. Simulation and experimental results show that the proposed VCF-STAP achieves robust performance of moving target detection in heterogeneous environments, especially it achieves much superior detection performance compared with existing STAP methods when the real clutter environments do not satisfy their prior assumptions. Besides, it is also shown that VCF-STAP has the constant false alarm rate (CFAR) property.

Ground Moving Target Imaging via SDAP-ISAR Processing: Review and New Trends.

Ground moving target imaging finds its main applications in both military and homeland security applications, with examples in operations of intelligence, surveillance and reconnaissance (ISR) as well as border surveillance. When such an operation is performed from the air looking down towards the ground, the clutter return may be comparable or even stronger than the target’s, making the latter hard to be detected and imaged. In order to solve this problem, multichannel radar systems are used that are able to remove the ground clutter and effectively detect and image moving targets. In this feature paper, the latest findings in the area of Ground Moving Target Imaging are revisited that see the joint application of Space-Time Adaptive Processing and Inverse Synthetic Aperture Radar Imaging. The theoretical aspects analysed in this paper are supported by practical evidence and followed by application-oriented discussions.

Theoretical Performance of Low Rank Adaptive Filters in the Large Dimensional Regime

This paper proposes a new approximation of the theoretical signal to interference plus noise ratio (SINR) loss of the low-rank (LR) adaptive filter built on the eigenvalue decomposition of the sample covariance matrix. This new result is based on an analysis in the large dimensional regime, i.e., when the size and the number of data tend to infinity at the same rate. Compared to previous works, this new derivation allows us to measure the quality of the adaptive filter near the LR contribution. Moreover, we propose a new LR adaptive filter and we also derive its SINR loss approximation in a large dimensional regime. We validate these results on a jamming application and test their robustness in a multiple input multiple output space time adaptive processing application where the data size is large.

An Efficient and High-Speed Implementation of QRD-MGS Algorithm for STAP Application Based on Floating Point FPGAs

Space-Time Adaptive Processing (STAP) can harness the efficacy of interference and clutter significantly. Calculations of the STAP weights involve solving linear equations which require very intensive computations. In this paper, the QR decomposition (QRD) using the modified gram-schmidt (MGS) algorithm is parameterized with vector size to create a trade-off between the hardware resources utilization and computation time. To achieve an efficient floating point structure, the proposed architecture of QRD-MGS algorithm is simulated and implemented in two modes: single-vector and multi-vector. Results show that the multi-vector method can lead to a high-performance design with higher operating frequency, lower power consumption, and less resource utilization than the single-vector method. For example, Modelism simulations show that the decomposition of a [Formula: see text] matrix with vector size of 17 takes 7.86[Formula: see text][Formula: see text]s with the maximum clock frequency of 282[Formula: see text]MHz, for implementation on the Arria10 FPGA. In real STAP applications, the matrix sizes are too large to be fit on FPGAs and the update rate of the weights are high. Therefore, this method can fit any matrix in the contemporary FPGAs with an acceptable update rate.

A 2D wavenumber domain phase model for ground moving vehicles in synthetic aperture radar imagery

In this paper, fundamental phase characteristics of moving vehicles in synthetic aperture radar (SAR) data are reviewed. A 2D phase model for a moving point scatterer is expressed in terms of range and azimuth wavenumbers. The moving point scatterer impulse response is then the 2D Fourier transform of the associated complex sinusoid. Numerical computation of the 2D phase for arbitrary relative radar-point scatter motion is organized as a composition of functions expressing time, frequency and angle in terms of wavenumber vectors. An analytic model for the phase is subsequently derived in the special case that the Doppler cone angle is 90°. With that model it is observed that the map from velocity and acceleration to quadratic phase is not one-to-one and therefore the associated inverse problem is ill-posed. An example of moving vehicle Doppler energy dispersion and corresponding phase measured in clutter suppressed SAR image data is provided. Clutter suppression is achieved by application of spacetime adaptive processing.

The Study of Clutter Suppression techniques for Multi-channel SAR

The paper focus on the study of several typical clutter suppression techniques for multi-channel SAR: Displaced Phase Center Antenna (DPCA), Along Track Interferometry (ATI) and Space Time Adaptive Processing (STAP) techniques. In the paper, it has been discussed that application of DPCA and ATI in the complex image domain for moving target detection in airborne single-antenna SAR real data as well as application of STAP in the space-doppler domain. In the end, some experimental results have been presented.

Performance Analysis of Two-Dimensional Parametric STAP for Airborne Radar using KASSPER Data

We analyze the performance of a recently introduced class of two-dimensional (2-D) multivariate parametric models for space-time adaptive processing (STAP) in airborne radars on the DARPA airborne side-looking radar model known as KASSPER Dataset 1. Investigation of the impact of linear uniform antenna array errors on techniques that exploit spatial smoothing is demonstrated using a complementary phenomenological clutter model developed at the AFRL. Signal-to-interference-plus-noise ratio (SINR) degradation with respect to the optimal clairvoyant receiver is studied for different parametric models, antenna errors, and training sample volumes. We also analyze the impact of KASSPER training data inhomogeneity on STAP performance. For an extremely small number of training-data samples, we demonstrate that a properly selected parametric model and an accompanying covariance matrix estimation technique should achieve efficient performance for practical STAP applications.

Clutter modelling for space–time adaptive processing in airborne radar

The presence of clutter can be highly detrimental to detection of targets in airborne radar systems. Space–time adaptive processing (STAP) attempts to alleviate the problem by exploiting angle–Doppler inter-relationships to suppress clutter. The requirements for modelling of clutter data that are suitable for the application of STAP are considered in this study. Examples of clutter generated using the ELMBAARC program in bistatic and monostatic applications are provided. Modelling of internal clutter motion is considered, together with its effect on the clutter domain in angle–Doppler space. Modelling of non-homogeneous clutter is also considered.

Post-adaptive processing time-delay beamforming for clutter suppression in airborne radar

A novel beamforming architecture is presented in which time delay steering is applied after the application of space-time adaptive processing (STAP) in a phased array for airborne radar. Improved clutter suppression and slow moving target detection performance can be achieved over that associated with STAP and conventional time delay steering in wideband sideways looking applications. The architecture also provides a computationally efficient means of applying time-delay beamforming in conjunction with STAP in applications where multiple receive beams are required.

Adaptive cancellation method for geometry-induced nonstationary bistatic clutter environments

This paper describes and characterizes a new bistatic space-time adaptive processing (STAP) clutter mitigation method. The approach involves estimating and compensating aspects of the spatially varying bistatic clutter response in both angle and Doppler prior to adaptive clutter suppression. An important feature of the proposed method is its ability to extract requisite implementation information from the data itself, rather than rely on ancillary - and possibly erroneous or missing - system measurements. We justify the essence of the proposed method by showing its ability to align the dominant clutter subspaces of each range realization relative to a suitably chosen reference point as a means of homogenizing the space-time data set. Moreover, we numerically characterize performance using synthetic bistatic clutter data. For the examples considered herein, the proposed bistatic STAP method leads to maximum performance improvements between 17.25 dB and 20.75 dB relative to traditional STAP application, with average improvements of 6 dB to 10 dB.

Element‐localized Doppler STAP (Space Time Adaptive Processing) for clutter suppression in automotive forward‐looking RADAR

AbstractIn forward‐looking radars such as automotive radars, it is necessary to suppress the reflected undesirable signal (clutter) from such objects as the ground surface (especially hills), guard rails, and buildings, in order to detect moving targets ahead. When using a pulse‐Doppler filter (PDF), which is the conventional method for clutter suppression, separation is difficult when the velocity of the target relative to the clutter is small. In this paper, as a high performance clutter suppression method for such low‐velocity targets, we propose element‐localized Doppler space time adaptive processing (ELD‐STAP), which includes a PDF in the preprocessing, and which uses an array antenna. In the proposed ELD‐STAP, only the PDF output corresponding to the Doppler frequency breadth of the clutter, predicted from the speed of the vehicle on which it is mounted, is selected for applying STAP, and hence the dimension of the data vector, which is the greatest difficulty in the application of STAP, can be reduced. By reducing the dimension of the data vector, not only the computation load, but also the number of cells that must be referenced to estimate the clutter correlation matrix (called secondary cells), can be reduced. Computer simulation reveals that the proposed ELD‐STAP yields an improvement factor better than that provided by the combination of PDF and multibeam forming (MBF). © 2006 Wiley Periodicals, Inc. Electron Comm Jpn Pt 1, 90(1): 77–89, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecja.20221

Performance Evaluation of a Parallel Pipeline Computational Model for Space-Time Adaptive Processing

This paper presents further results on the design and implementation of various optimizations based on our earlier work of developing a parallel pipelined model for the computational intensive applications that have multiple processing tasks. Performance evaluation of this model was done by using a real-time airborne radar application that employs a Space-Time Adaptive Processing (STAP) algorithm. This paper focuses on the following four issues: (1) The tradeoffs between increasing the throughput and reducing the latency are examined in more detail when allocating processors among different processing tasks. (2) A multi-threaded design is incorporated into the pipeline model and implemented on a massively parallel computer with symmetric multi-processor nodes, which shows enhanced performance. (3) The disk I/O is incorporated into the parallel pipeline to study its effect on performance in which two I/O task designs have been implemented: embedding I/O in the pipeline or having a separate I/O task. By using a double buffering approach together with the asynchronous I/O, the overall pipeline performance scales well as the number of processors increases. (4) From the comparison of the two I/O implementations, it is discovered that the latency may be improved when merging multiple tasks into a single task. The effect of reorganizing the task structure of the pipeline is discussed in detail. All the performance results shown in this work demonstrate the linear scalability the parallel pipeline model can achieve using a production radar application. Although this paper focuses on the implementation of the parallel pipeline model and uses the results from a STAP application to support the claims of the discovered properties for this pipeline, this model is also applicable to many other types of applications with similar computational characteristics.

Statistical analysis of the non-homogeneity detector for STAP applications

We present a statistical analysis of the recently proposed non-homogeneity detector (NHD) for Gaussian interference statistics. Specifically, we show that a formal goodness-of-fit test can be constructed by accounting for the statistics of the generalized inner product (GIP) used as the NHD test statistic. The normalized-GIP follows a central-F distribution and admits a canonical representation in terms of two statistically independent chi-squared distributed random variables. Moments of the GIP can be readily calculated as a result. These facts are used to derive the goodness-of-fit tests, which facilitate intelligent training data selection. We then address the issue of space–time adaptive processing (STAP) algorithm performance using the NHD as a pre-processing step for training data selection. Performance of the adaptive matched filter (AMF) method is reported using simulated as well as measured data.

Implementation of STAP algorithms on IBM SP2 and on ADSP 21062 dual digital signal processor systems

Space–time adaptive processing (STAP) is a well-known technique in the area of air borne surveillance radars, which is used to detect weak target returns embedded in strong ground clutter, interference, and receiver noise. Data processing for STAP refers to a 2D adaptive iterative algorithm which attenuates unwanted signals by placing nulls in the frequency domain with respect to their direction of arrival and/or Doppler frequencies. Most STAP applications are highly compute-intensive and are required to operate in real-time. Parallel computing is a method used to satisfy the computational requirements of real-time STAP while increasing the flexibility and scalability of radar signal processing systems. However, efficient parallelization of STAP algorithm, which consists of several different algorithms such a fast Fourier transform, QR factorization and beam-forming requires lot of optimizations, both at code level and at execution time level. The STAP algorithm is highly signal processing oriented and hence executes efficiently on digital signal processor (DSP)-based systems. In this work, we developed a parallel STAP algorithm on an IBM SP2 parallel computing system and on ADSP 21062 Dual-DSP system. With eight processors, IBM SP2 system gives a speed-up of only 2.4. The low speed-up can be attributed to the large volume of data movement across the tasks and across the processors. We also implemented the STAP algorithm on a dual-DSP system. The dual-DSP system is based on ADSP 21062 processor that operates at 40 MHz clock frequency. Due to large internal and shared memories, the overhead due to data movement across the tasks and across processors is very low and the results show that the speed-up with two processors is 1.9.

A Genetic-Algorithm Approach to Scheduling Communications for Embedded Parallel Space–Time Adaptive Processing Algorithms

Computational efficiency is of great significance for high-performance embedded applications. The work here develops and evaluates a genetic-algorithm-based (GA-based) optimization technique for the scheduling of messages for a class of parallel embedded signal processing techniques known as space–time adaptive processing (STAP). The GA-based optimization is performed off-line, resulting in static schedules for the compute nodes of the parallel system. These static schedules are utilized for the on-line implementation of the parallel STAP application. The primary motivation and justification for devoting significant off-line effort to solving the formulated scheduling problem is the resulting reduction of hardware resources required for the actual on-line implementation. Numerical studies illustrate that reductions in hardware requirements of around 50% can be achieved by employing the results of the proposed scheduling techniques. This reduction in hardware requirement is of critical importance for STAP, which is typically an airborne application in which the size, weight, and power consumption of the computational platform are severely constrained.

Robust space–time adaptive processing (STAP) in non-Gaussian clutter environments

The problem of space–time adaptive processing (STAP) in non-Gaussian clutter is addressed. First, it is shown that actual ground clutter returns are heavy-tailed, and their statistics can be accurately characterized by means of alpha-stable distributions. Then, a new class of adaptive beamforming techniques is developed, based on fractional lower-order moment theory. The proposed STAP methods adjust the radar array response to a desired signal while discriminating against non-Gaussian heavy-tailed clutter modelled as a stable process. Experimental results with both simulated and actual clutter data show that the new class of STAP algorithms performs better than current gradient descent state-of-the-art methods, in localising a target both in space and Doppler, and thus offers the potential for improved airborne radar performance in STAP applications.