Minimum radar cross section bounds for passive radar responsive tags

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.

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 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.

In recent years, increasing deployment of large wind-turbine farms has become an issue of growing concern for the radar community. The large radar cross section (RCS) presented by wind turbines interferes with radar operation, and the Doppler shift caused by blade rotation causes problems identifying and tracking moving targets. Each new wind-turbine farm installation must be carefully evaluated for potential disruption of radar operation for air defense, air traffic control, weather sensing, and other applications. Several approaches currently exist to minimize conflict between wind-turbine farms and radar installations, including procedural adjustments, radar upgrades, and proper choice of low-impact wind-farm sites, but each has problems with limited effectiveness or prohibitive cost. An alternative approach, heretofore not technically feasible, is to reduce the RCS of wind turbines to the extent that they can be installed near existing radar installations. This report summarizes efforts to reduce wind-turbine RCS, with a particular emphasis on the blades. The report begins with a survey of the wind-turbine RCS-reduction literature to establish a baseline for comparison. The following topics are then addressed: electromagnetic model development and validation, novel material development, integration into wind-turbine fabrication processes, integrated-absorber design, and wind-turbine RCS modeling. Related topics of interest, including alternative mitigation techniques (procedural, at-the-radar, etc.), an introduction to RCS and electromagnetic scattering, and RCS-reduction modeling techniques, can be found in a previous report.

Abstract. As a result of increasing use of wind energy as a sustainable source of electricity, large Wind Parks with numerous Wind Turbines have been constructed. Wind turbines are extremely tall objects consisting of stationary and moving parts. The presence of wind turbines in the vicinity of weather radar systems can significantly impact their performance, leading to false alarms and errors in radar measurements. Accurate weather forecasting is challenging in this circumstance. Large Radar Cross Section (RCS) of wind turbines results in interference, also known asWind Turbine Clutter (WTC) orWind Turbine Interference (WTI), within and beyond the radar main beam, Multipath Interference (MPI), and phenomena referred to as ”shadowing effects” behind the wind turbines. These effects vary significantly in both time and space as a result of various wind turbine operations and meteorological conditions. It can often be difficult to distinguish wind turbine returns from weather-like signals. For the assessment of WTC or WTI, it is essential to understand the scattering properties of these wind turbines. In this paper, the bistatic scattering characteristics of a wind park turbine using a Unmanned Aerial Vehicle (UAV)-mounted receiver recording C-band weather radar signals were investigated by determining the average received power (PRxAvg (θs)) and RCS of wind turbine as a function of the scattering angle. For this purpose, the measurements and data provided by the German Meteorological Service (DWD, DeutscherWetterdienst) were utilised. The average received power as a function of scattering angle (θs) was calculated by using I-Q (In-phase and Quadrature) signals. Forward, back and side scattering of the calculated average received power were analysed separately. Moreover, Front-to-Back ratio, Front-to-Right side ratio and Front-to-Left side ratio were calculated and compared using forward, back and side scatter values. RCS values were also calculated depending on the scattering angle (θs) of the wind turbine.

Knowing the statistical characteristics of the radar cross-section (RCS) of man-made, or cultural clutter, is crucial to the success of clutter mitigation, radar target detection algorithms, and radar system requirements in urban environments. Open literature studies regarding the statistical nature of cultural clutter focus primarily on radar probability models or limited experimental data analysis of specific locations and frequencies. This paper seeks to expand the existing body of work on cultural clutter RCS statistics at Ku-band for ground moving target indication (GMTI) and synthetic aperture radar (SAR) applications. We examine the normalized RCS probability distributions of cultural clutter in several urban scenes, across aspect and elevation angle, for vertical transmit/receive (VV) polarizations, and at diverse resolutions, using experimental data collected at Ku-band. We further describe frequency and RCS strength statistics of clutter discretes per unit area to understand system demands on radars operating in urban environments in this band.

Radar Cross Section(RCS) is a measure of how detectable an object is with a radar. A larger RCS indicates that an object is more easily detected. Informally, the RCS of an object is the cross-sectional area of a perfectly reflecting sphere that would produce the same amount of reflection strength as the object in question would. In order to estimate RCS of aircraft weapons the external surface is modeled as a collection of simple shape elements. And the overall RCS is estimated as a vector sum of configuring elements' cross-sections which are well known given by analytic formulae. A RCS estimation code is developed for a typical shape of Air-To-Surface bombs and missiles. Size of weapons and location of fins are implemented in the code in addition to the presence of canards. The ability to predict radar return from flying vehicles becomes a critical technology issue in the development of stealth configurations. This simplified method of RCS estimation is known to be fast and accurate enough in an optical region of high frequency incident radio wave.

The authors carried out calibration experiments of the C-band synthetic aperture radar (SAR) of the first European Remote-Sensing Satellite (ERS-1) with its three passes over Japan in June, July, and August 1993, employing an active radar calibrator (ARC) and a corner reflector (CR) both located in the site of Kashima Space Research Center (140/spl deg/40'E, 35/spl deg/57'N) of the Communications Research Laboratory. The ARC has a large radar cross section (RCS) of over 60 dBm/sup 2/, with a function to shift the frequency of signals when retransmits that makes the position of the ARC shift in the SAR image. Frequency shifts of the ARC were set to 0, 200, 400 Hz for the three passes each, so that the ARC echo should appear at 0, 640, 1280 m shifted in the azimuth direction from its actual location in each SAR image. Received power by the ARC was recorded during the overpasses, which shows an antenna pattern of the SAR. The authors also used a corner reflector of 1.1 m square trihedral (RCS=42.4 dBm/sup 2/) as a landmark, as well as another calibration target. Obtained SAR image data processed by National Space Development Agency of Japan (NASDA), the authors have examined image amplitude (data numbers) and locations of both of the ARC and the CR. It is seen in the SAR images, as expected, that the positions of the ARC are shifted depending on the shift of frequency. >

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.

This paper addresses four-dimensional ground moving target indication (GMTI) using a multi-channel wide band (WB) synthetic aperture radar (SAR) system. With no acceleration attached to the target the motion of the target is related to two ground and two speed coordinates. However, four other dimensions are used in WB SAR GMTI processing during the detection phase: azimuth, range, bearing, and the relative speed between the object and the SAR platform. In the detection phase, blind hypotheses are used, and the discretization steps between the hypotheses are a trade-off between the number of hypotheses tested and detectability. As the integration angle increases, the step size in the image dimensions and in the relative speed has to be reduced. In this paper we determine the discretization step in all four dimensions for moving target detection, and relate it to radar system parameters. The discretization is derived from the moving target impulse response, assuming independency between the dimensions. In the paper the number of hypotheses per square meter is given for an airborne low frequency and a microwave GMTI WB SAR system.

The detection and tracking of targets in highly cluttered environments often poses a difficult engineering challenge as the strong clutter backscatter contained in the scene makes it difficult to distinguish the target of interest. A design concept to aide in standoff detection and tracking of large metallic objects in a warehouse setting is presented. This includes the design and implementation of an active retro-reflective array system that is able to determine the precise location and identification of an object. To accomplish this, a unique series fed grounded coplanar waveguide patch antenna is designed and implemented with minimal cross coupling among elements. This compact array has a relatively large radar cross-section (RCS) while maintaining the desired retro-reflectivity. Additionally, a tilted beam is incorporated in the linear series-fed array to isolate the large RCS of the planar array structure at boresight from the desired modulated, retro-reflective RCS. This method for enhanced detection is implemented at 26 GHz. The incorporation of the high-speed PIN switches into the array structure provide the tag with a unique identification. Measurement results for the modulated RCS from such an array in a cluttered environment are presented.

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.

We present a new assignment-based algorithm for data association in tracking ground targets employing evasive move-stop-move maneuvers using ground moving target indicator (GMTI) reports obtained from an airborne sensor. To avoid detection by the GMTI sensor, the targets deliberately stop for some time before moving again. The sensor does not detect a target when the latter's radial velocity (along the line-of-sight from the sensor) falls below a certain minimum detectable velocity (MDV). Even in the absence of move-stop-move maneuvers, the detection has a less-than-unity probability (P/sub D/<1) due to obscuration and thresholding. Then, it is of interest, when a target is not detected, to develop a systematic technique that can distinguish between lack of detection due to P/sub D/<1 and lack of detection due to a stop (or a near stop). Previously, this problem was solved using a variable structure interacting multiple model (VS-IMM) estimator with a stopped target model (VS-IMM-ST) without explicitly addressing data association. We develop a novel assignment approach for move-stop-move targets that considers both the problem of data association as well as filtering. Typically, in assignment-based data association a measurement is used to denote the nondetection event. The use of the standard single-dummy assignment, which does not handle move-stop-move motion explicitly, can result in broken tracks. The new algorithm proposed here handles the evasive move-stop-move motion by introducing a second dummy measurement to represent nondetection due to the MDV. We also present a likelihood-ratio-based track deletion scheme for move-stop-move targets. Using this two-dummy data association algorithm, the track corresponding to a move-stop-move target is kept alive' during missed detections both due to MDV and due to P/sub D/<1. In addition, one can obtain reductions in both rms estimation errors as well as the total number of track breakages.

The problem of path planning for unmanned combat aerial vehicles (UCAVs) in the presence of radar-guided surface-to-air missiles is treated. The problem is formulated in the framework of the interaction between three subsystems: the aircraft, the radar, and the missile. The main features of this integrated model are as follows. The aircraft radar cross section (RCS) depends explicitly on both the aspect and bank angles; hence, the RCS and aircraft dynamics are coupled. The probabilistic nature of radar tracking is accounted for, namely, the probability that the aircraft has been continuously tracked depends on the aircraft RCS and range. Finally, the requirement to maintain tracking before missile launch and during missile flyout are also modeled. Based on this model, the problem of UCAV path planning is formulated as a minimax optimal control problem, with the aircraft lateral acceleration serving as control. Necessary conditions of optimality for this minimax problem are derived and used as a basis for an efficient numerical solution. Illustrative examples are considered that confirm the standard flying tactics of “denying range, aspect, and aim,” by yielding flight paths that weave to avoid long exposures of aspects with large RCS. HIS paper is devoted to the problem of automated path planning for unmanned combat aerial vehicles (UCAVs) in the presence of radar-guided surface-to-air missiles (SAMs). This problem features the interaction between three subsystems: the aircraft and its characteristics, the radar and its capabilities, and the missile and its lethality. Therefore, the solution of the UCAV path-planning problem requires realistic models of these three subsystems. Although the current literature offers models for each of them separately, there is no approach that integrates models of the three subsystems in a unified framework. The purpose of this paper is to propose such an integrated model and present results on its use.

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.