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

In this paper, a robust statistical method called projection statistics (PS) is developed into an TV-dimensional complex form required to detect strong nonhomogeneities, or TV-dimensional outlier vectors (i.e., outliers), in space-time adaptive processing (STAP) radar training data, where N is the number of degrees of freedom (DOF) associated with the STAP scenario. The PS technique does not require the estimation of a covariance matrix, as is the case with the generalized inner product (GIP) test. Rather, it uses robust estimators of location (the median) and scale (the median absolute deviation from the median (MAD)) to identify and then excise strong nonhomogeneities in STAP training data that are capable of rendering traditional nonhomogeneity detection (NHD) techniques, such as the GIP, ineffective. Shown in the results section of this paper is sample matrix inversion (SMI) STAP performance using three separate NHD techniques: 1) a power test for high power outlier snapshots, 2) a GIP test, and 3) a PS-based test. The performance when not using any form of NHD is shown for comparison. It is clear from the results that the PS NHD method provides an SMI-STAP processor the best training data selection in the multi channel airborne radar measurements (MCARM) [B.N.S Babu et al., 1996] based radar data scenario studied here, and does so across all range cells of the scenario.

Space-time adaptive processing (STAP) is a powerful technique for detecting slowly moving targets in strong clutter. However, STAP performance is limited when used on radars with small apertures. Past research suggests polarization's potential to effectively discriminate between targets and clutter, thereby improving detection performance. We evaluate two methods of incorporating polarization into STAP, modeled after the polarimetric matched filter (PMF) and the polarimetric whitening filter (PWF). Our analysis serves to unify various proposed approaches to combining STAP and polarization, and benchmarks the benefits of polarization to GMTI detection. Using measured clutter and target characteristics from published sources as a baseline, we found that the PMF technique offers up to 6-dB improvement over STAP. This significant advantage is realized in the center of the clutter spectrum, and the impact on minimum detectable velocity (MDV) is modest. However, the PWF offers increased performance over the entire Doppler spectrum, and can be easily implemented as a STAP post-processor.

Power efficiency - performance relative to power - is one of the most important concerns when designing RADAR processing systems. This paper analyzes power and performance trade-offs for a typical Space Time Adaptive Processing (STAP) application. We study STAP implementations for CUDA and OpenMP on two architectures, Intel Haswell Core I7-4770TE and NVIDIA Kayla with a GK208 GPU. We analyze the power and performance of STAP's computationally intensive kernels across the two hardware testbeds. We discuss an efficient parallel implementation for the Haswell CPU architecture. We also show the impact and trade-offs of GPU optimization techniques. The GPU architecture is able to process large size data sets without increase in power requirement. The use of shared memory has a significant impact on the power requirement for the GPU. Finally, we show that a balance between the use of shared memory and main memory access leads to an improved performance in a typical STAP application.

The ground moving-target indication (GMTI) technique can detect civil and military moving targets, which means that this technique has received much attention. Strong clutter background suppression is one of the critical problems in this application. However, the detection performance in heterogeneous environment can be degraded due to the inaccurate estimation of the clutter covariance matrix (CCM). In this paper, we propose a robust GMTI method using a spaceborne dual-platform synthetic aperture radar (SAR) system, which can obtain highly accurate CCM in nonuniform clutter. Firstly, the accurate CCM is estimated based on the SAR image obtained by the former platform. Then, space–time adaptive processing (STAP) is carried out using the obtained the CCM. Finally, the detection threshold is set according to the estimated CCM and detection is executed accurately. Compared with the traditional CCM estimation method in STAP using the clutter nearby the cell under test, this method directly estimates the CCM using the clutter of the cell under test, which can avoid CCM estimation mistakes in heterogeneous clutter environment. The clutter can be whitened and depressed more effectively. Additionally, with the accurate threshold acquired from the CCM, the detection probability can be effectively improved under a certain false-alarm criterion. Based on simulation data, GMTI experiments in a heterogeneous environment such as clutter with strong pollution, junction zone of hot and cold clutter, and clutter with nonuniform power are carried out; the results show that the moving targets can be effectively detected with the proposed method.

We study the performance of space-time adaptive processing (STAP) applied to coherent multistatic radar. The radars we consider are passive (receive only) but are synchronized in order to allow coherent processing of the data among the different radars. One of the major benefit of multistatism is that the full velocity vectors of the targets can be measured. Furthermore, we study the feasibility of estimating the clutter covariance matrix, required to compute the STAP filter. We show that range-dependence effects cause the usual sample matrix inversion (SMI) scheme to fail and we propose a method able to cope with arbitrary multistatic geometries.

: A study of space-time adaptive processing in nonhomogeneous environments is presented. The results produced are logically organized as investigations into three different but related topics. The first topic can be summarized as an investigation of the performance of partially adaptive space-time adaptive processing schemes using measured (MCARM) data. The second topic considers the development of a new set of space-time processing algorithms, which use prior knowledge to reduce the number of parameters, which need to be adapted. The new schemes were tested using MCARM data. The final topic provides analytical realistic cases where the reference or training data may have different statistics from the data in the cell under test. Such equations have not yet appeared in the literature and they are useful for understanding the performance of space-time adaptive processing (STAP) schemes when they are tested with MCARM data.

Polarimetric weather radars, such as the Weather Surveillance Radar-1988 Doppler (WSR-88D), improve weather forecasts and provide valuable data for operational and scientific applications. The polarimetric capability adds additional insight into storm microphysics and greatly improves precipitation estimates. Nevertheless, fast-evolving weather events require high-temporal resolution data, which conventional radar systems (mechanical and dish-based) cannot provide. Phased array radar (PAR) offers superior observation capabilities with electronic beam steering and enhanced scanning agility. Furthermore, digital PAR enables 1-D (space and time) processing and overcomes limitations in clutter mitigation compared with the traditional radar systems that only use Doppler processing. Doppler processing is traditionally used to effectively filtering out ground clutter with zero mean velocity. In contrast, space-time processing (STP) enhances clutter mitigation to filter out both stationary and moving clutter through the joint spatial and temporal spectrum. This study aims to apply STP (nonadaptive) and space-time adaptive processing (STAP) to weather radar data and explore their benefits to improve Doppler velocity estimation of meteorological returns. Furthermore, the performance of STP and STAP for different digital PAR back ends, including fully digital and subarray systems, is investigated. Preliminary findings underscore the critical role of radar scanning parameters and environmental conditions, such as sample quantity, clutter-to-signal ratio (CSR), and signal-to-noise ratio (SNR), in the Doppler velocity estimation. Data collected with the recently completed Horus radar system are evaluated using STP and STAP. Results demonstrate the potential for improving data quality, particularly in Doppler velocity estimation within cluttered environments, through the application of STP and STAP techniques. The filtering algorithm with STAP demonstrates a substantial reduction in error within the Doppler velocity estimation, achieving approximately an eightfold improvement compared with the estimation derived from STP with filtering.

This chapter is concerned with the study of signal processing techniques and operational strategies for passive radar systems mounted onboard moving platforms and aimed at ground moving target indication (GMTI) applications. Specifically, the attention is focused on space-time adaptive processing (STAP) methodologies for systems equipped with multiple channels on receive. The spatial degrees of freedom are adaptively exploited for space-time clutter cancellation and slow-moving target detection and direction of arrival estimation. Specific solutions are devised and integrated into the typical signal processing architecture of passive radar to address the main limitations deriving from the passive bistatic framework.

Many anti-jamming techniques have been proposed such as adaptive temporal filtering (ATF), spatial adaptive processing (SAP), space-time adaptive processing (STAP), power spectrum excision, and etc. The performance of each specific anti-jamming technique is often evaluated in terms of tracking thresholds not in terms of range or position error. And several techniques can be combined together to provide better anti-jamming performance. How to judge the anti-jamming performance of these algorithms has become a new problem. In order to solve this problem, the anti-jamming performance of SAP and STAP based on linear constraint minimum variance (LCMV) criterion will be evaluated and compared through many indicators in this paper. Simulation results show that STAP with L constraints brings more degrees of freedom to constrain the weight coefficients and thus improve C/N0, especially when many jammers impinge on the array.

For a general conformal array, the angle-Doppler spectrum of surface clutter in monostatic radar systems is range dependent This results in a mismatch of the clutter covariance matrix (computed from a secondary set of range-cell data) relative to the clutter covariance matrix of a test cell, with attendant degradation of space-time adaptive processing (STAP) performance. Unlike for a line array, the clutter covariance matrix of a range cell depends on both its elevation and azimuth angles. This work develops a least-squares method for estimating the system configuration parameters from the secondary data. The method uses a multiple complex sinusoid model whose parameters are related to the system configuration parameters which are then optimized to match the radar return spatial-temporal data in a least-squares sense. The estimated configuration parameters are then used to predict the clutter covariance matrix in the test cell which is then used with STAP methods. Computer simulation results are presented to show the improved STAP performance compared to standard compensation methods.

The short-range clutter has serious range dependence for forward looking airborne radar (FLAR). As a result, when a radar detects moving targets on far range gates, the ambiguous short-range clutter degrades the performance of ground moving target indication (GMTI) obtained by spacetime adaptive processing (STAP). The elevation three dimensional adaptive processing can well mitigate the problem, but, the computational load of the method is too heavy to be applied in real scenarios. A robust elevation pre-filtering algorithm is proposed. This method does not need adaptive processing on the elevation elements in a planar-array antenna. Therefore, the computational load can be reduced greatly. This method suppresses the ambiguous short-range clutter by multi-elevation angles restriction and the cancellation weight on the clutter has a deep and wide notch. Moreover, the method is robust to the elevation angle errors. Simulation results show the validity of the method.

An adaptive coding-angle-Doppler clutter suppression approach with extended azimuth phase coding array

Linear prediction of range-dependent inverse covariance matrix (PICM) sequences

Utilizing digital beam forming (DBF) on transmit and receive, the multiple-input multiple-output (MIMO) radar can be applied to ground moving target indication (GMTI) and can provide an efficient way to improve the GMTI performance. The operating pattern is very significant for the MIMO radar GMTI. For the traditional GMTI pattern, there is a conflict of the wider swath and narrower clutter bandwidth. The observation area can be broadened with MIMO techniques by scanning. This paper designs an effective pattern with the simultaneous transmission of multi-phase-centre multi-sub-band signals as well as beam scanning in each pulse, which provide an efficient way to settle the conflict. By beam scanning in pulse, the new pattern has wider azimuth swath, shorter revisiting period, larger coherent processing interval (CPI) number and higher SCNR. By MIMO technology, more equivalent phase centres and longer efficient baselines can be formed, so the performance of GMTI can be improved. The principle of space-time-frequency adaptive processing (STFAP) is analysed, simulation results are presented to prove the superiority of MIMO radar in clutter suppression and GMTI. Analysis of the results indicates improved detection performance with the new pattern compared to the conventional approach.

The effects of array errors on the clutter spectra are discussed. It is pointed out that, just like that in pattern recognition problems, the training method has significant effects on the performance of space-time adaptive processing (STAP) techniques. A reasonable training method is given, which exhibits strong generalizing ability. Simulation results based on both the steady-state clutter covariance matrix and the high fidelity radar clutter data confirm the above conclusions.