Multimode adaptive signal processing: a new approach to GMTI radar

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.

Ground moving target indication (GMTI) plays an important role in surveillance and reconnaissance applications, but it is a difficult problem due to the impacts of stationary clutter. This paper proposes a multichannel synthetic aperture radar (SAR) using waveform diversity, namely, multiple-input multiple-output (MIMO) SAR and distinct carrier frequency for GMTI applications. The proposed approach employs MIMO configuration with multiple antennas in azimuth and orthogonal waveform diversity. In doing so, multiple independent transmit-receive channels are formulated at the receiver. Since each antenna uses both waveform diversity and distinct carrier frequency, after clutter cancellation and noise suppression by the displaced phase center antenna (DPCA) and along-track interferometry (ATI) combined processing algorithm, the cross-track velocity is estimated through double-interferometry processing. Next, the Doppler parameters of moving targets are estimated with the fractional Fourier transform algorithm and then GMTI is obtained by a joint signal processing algorithm. Finally, the moving targets are focused with a general chirp scaling imaging algorithm. All the proposed methods are verified by simulation results.

Multidimensional waveform encoding is a new multiple-input multiple-output (MIMO) technique for remote sensing. The orthogonality is acquired by digital beamforming in elevation. It has a potential to enhance ground moving target indication (GMTI) performance due to the larger along-track virtual array of the radar system. This paper proposed two modes for GMTI based on multidimensional waveform encoding: intrapulse azimuth beam scanning and intrapulse azimuth encoding. Compared to the phased-array GMTI systems, the mode with intrapulse azimuth beam scanning has improved area search rates, and the mode with intrapulse azimuth encoding has improved minimal detectable velocity performance. Theoretical analysis and simulation results demonstrate that the proposed modes have better performance than the phased-array GMTI systems.

In this paper, we present the results of a modeling study to examine the interference effect of microDopplers caused by offshore wind farms on airborne sensors operating in the synthetic aperture radar (SAR) and ground moving target indicator (GMTI) modes. The modeling is carried out by generating CAD instantiations of the dynamic wind turbine and using the high-frequency electromagnetic code Xpatch to perform the scattering calculations. Artifacts in the resulting SAR and GMTI signatures are evaluated for interference with tracking of boats in coastal waters. Results of signal filtering algorithms to reduce the dynamic turbine clutter in both SAR images and GMTI displays are presented.

The understanding of maneuvering forces is invaluable to the warfighter, as it enhances understanding of enemy force structure and disposition, provides cues to potential enemy actions, and expedites targeting of time critical targets. Airborne ground moving target indicator (GMTI) radars are a class of highly-effective, all-weather, wide-area senors that aid in the surveillance of these moving ground vehicles. Unfortunately conventional GMTI radars are incapable of identifying individual vehicles, and techniques for exploiting information imbedded within GMTI radar reports are limited. The Defense Advanced Research Projects Agency (DARPA) Moving Target Exploitation (MTE) program is working to mitigate these deficiencies by developing, integrating, and evaluating a suite of automated and semi-automated technologies to classify moving targets and units, and to provide indications of their activities. These techniques include: aid in the interpretation of GMTI data to provide moving force structure analysis, automatic tracking of thousands of moving ground vehicles, 1-D target classification based upon high-range- resolution (HRR) radar profiles, and 2-D target classification based upon moving target imaging (MTIm) synthetic aperture radar (SAR). This paper shall present the MTE concept and motivation and provide an overview of results to date.

The tasks of clutter suppression and ground moving target indication are a typical and also very challenging problem for airborne radar systems for both defence and civilian applications. Nowadays, research aims at applying these tasks for passive radar systems on moving platforms. This chapter addresses the problem of clutter cancellation and slowly moving target detection in orthogonal-frequency-division-multiplex-based passive radar systems mounted on moving platforms. Conventional signal processing approaches which exploit multiple receiving channels can be ineffective for the considered application due to the impossibility to control the employed waveform of opportunity. Therefore, a processing scheme, which aims at ground moving target indication needs to address this issue. The space-time adaptive processing scheme, proposed in this Chapter, exploits the benefits of the reciprocal filtering strategy applied at a range compression stage together with a flexible displaced phase center antenna approach. The effectiveness of the proposed scheme is demonstrated via application to a simulated dataset as well as on experimental data collected by a multichannel passive radar on an airborne moving platform.

Trackers for Ground Moving Target Indicator (GMTI) radar have to be benchmarked in order to yield good results. For many benchmarking purposes, the usage of real data may become expensive and in-feasible. It may also lack of knowledge of the ground truth. Simulating GMTI situations is a less-expensive alternative to real measurements. In order to get a realistic scenario, the motion and radar cross section (RCS) of ground targets, the motion of sensor platforms and the properties of GMTI sensors have to be simulated. In this paper, considerations for a realistic motion and sensor model are presented. Then, an implementation of a framework for simulation is shown, together with first experiences in tracker benchmarking using this framework.

Multiple-input multiple-output synthetic aperture radar (MIMO SAR) system has drawn considerable attention for its wider detected swath without decreasing PRF compared to the traditional multichannel synthetic aperture radar (MC-SAR) system. For MIMO SAR system, how to extract the matched signal or suppress the unmatched interferences is the foremost task. Using the OFDM chirp signals as the transmitted signals, the robust principal component analysis (RPCA) based method can be employed for ground moving target indication (GMTI) with no need for separating the matched signal and unmatched interferences. The simulation demonstrates the effectiveness of RPCA based method for GMTI in MIMO SAR system.

A novel signal for waveform agile SAR is investigated, which is based on the variable rate of frequency jump burst (FJB) chirp waveform in fast time and slow time domain. We introduce the signal model for simulating waveform agile SAR Ground Moving Target Indication (GMTI) data and the method for image formation. And discussions and simulation experiments about the imaging performance and GMTI are given.

Ground moving target indicator (GMTI) RADAR system frequently incorporates space-time adaptive processing for successful moving targets discrimination in a highly cluttered environment. The minimum detectable velocity (MDV) of the target gets affected due to the added clutter impairments such as foliage penetration and angle independent channel mismatch. A novel clutter model is postulated in this work that includes assessment enacted in view of both angle and Doppler clutter impairments at a time instead of their individual analysis as in the reported literature. The performance of the GMTI RADAR in terms of the signal-to-interference-plus-noise ratio loss is evaluated using the extrusive beamforming methods namely sample matrix inversion and principal component canceller. In high decorrelated clutter environment, the later outperforms in terms of the MDV, making use of a lesser number of training snapshots. The approach is validated with mathematical modelling and numerical simulations.

With the rapid development of lightweight unmanned aerial vehicles (UAVs), the combination of UAVs and ground moving target indication (GMTI) radar systems has received great interest. In GMTI, moving target relocation is an essential requirement, because the positions of the moving targets are usually displaced. For a multichannel radar system, the position of moving targets can be accurately obtained by estimating their interferometric phase. However, the high position accuracy requirements of antennas and the computational resource requirements of algorithms limit the applications of relocation algorithms in UAV-borne GMTI radar systems. In addition, the clutter’s interferometric phase can be severely affected by the undesired phase error in the site. To overcome these issues, we propose an improved knowledge-based (KB) algorithm. In the algorithm, moving targets can be relocated by comparing their interferometric phase with the clutter’s phase. As for the undesired phase error, the algorithm first employs a random sample consensus (RANSAC) algorithm to iteratively filter the outliers. Compared with other classic relocation algorithms, the proposed algorithm shows better relocation accuracy and can be applied in real-time applications. The performance of the proposed improved KB algorithm was evaluated using both simulated and real experimental data.

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.

Moving targets localization is an important challenge in current synthetic aperture radar (SAR) — ground moving target indication (GMTI) system. The along track interferometry (ATI) method is usually used for GMTI in SAR, however, which suffers from ambiguous parameters estimation due to wrapped phase and less robust to estimation errors (of system parameters and motion parameters of the moving targets). Focusing on these, we propose a new neural network based method to improve the moving targets localization performance. After analyzing the main error source of the conventional ATI method, we utilize an error back propagation (BP) neural network to replace the processing after range compression in the ATI method. And the proposed method is proved effective by the experiments, and it performs much better than the conventional ATI method in localization accuracy, efficiency and robustness.

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.

A novel algorithm is presented for SAR ground moving target indication (GMTI). This algorithm uses improved DPCA for a coarse indication and reformative ATI for a fine indication. First, a DPCA method is used to suppress the weak clutter, and it also involves the registration, compensation and subtraction of the image. Then, a finer ATI method is applied to detect the moving objects out of the pixels picked out by DPCA, and it involves the interferometry, and the suppression of the strong clutter. Facts prove that this algorithm can detect even the weak moving target well. In addition, this algorithm is automatic, that is, all thresholds are acquired from the signal. A series of test results from real field data of Institute of Electronics, Chinese Academy of Sciences shows the effectiveness of this algorithm.