In this paper, an orthogonal frequency-division multiplexing (OFDM) waveform radar sensing approach is demonstrated based on field measurements at C-band. We demonstrate a concept that is based on the exploitation of typical wireless communication transmissions to perform passive, distributed radar sensing. Our concept is based on an OFDM radar that operates in the modulation symbol domain and can be well integrated into existing OFDM receivers. The measurement setup and the signal processing steps for the OFDM radar are described. The results show that the passive, distributed radar sensing approach can be used to detect and localize moving cars and even pedestrians in automotive intersection scenarios.

Air target recognition is a critical step in the radar processing chain and reliable features are necessary to make a decision. The number and position of jet engines are useful features to perform a pre-classification and give a list of possible targets. To extract these features, a sparse decomposition framework for inverse synthetic aperture radar (ISAR) images is presented. With this framework different components of the target can be detected, if signal models for these parts are available. To use it for the detection of jet engines, a review of a signal model for air intakes, which was developed by Borden, is given. This model is based on the common assumption that the propagation of electromagnetic waves inside jet engines has the same dispersive behavior as inside waveguides. With this model a decomposition of a real ISAR image, measured with the tracking and imaging radar system of Fraunhofer FHR, into point-like scattering centers and jet engines is presented.

In this paper, we propose an effective waveform diversity scheme that can be applicable to synthetic aperture radar (SAR) operations affected by interfering signals. A novel approach is taken to achieve fully adaptive SAR waveform diversity that generates sufficient number of orthogonal signals with modest performance trade-off. To this purpose, multiple phased-code waveforms are arbitrarily generated with mutually low cross-correlations. They exhibit a highly flexible characteristic as their code lengths are not limited and Doppler tolerance is well preserved throughout SAR imaging. Various SAR jamming simulations are carried out to demonstrate that the proposed waveform diversity has a good potential for electronic counter-countermeasures applications.

In order to counter active jamming, an adaptive polarization cancellation method based on dual-polarization radar is proposed in the paper. First, respectively being obtained by vertical and horizontal polarization channels of dual-polarization radar, the signals from the real target and active jamming are analyzed in composition and characteristic. Then, the weight coefficient is timely derived in practical scene to adjust difference of amplitude and phase of active jamming signals respectively acquired by the two orthogonal polarization channels, so the method can completely eliminate active jamming, but just a little power change for the real target signal. Furthermore, a compensating method for the target signal is developed to improve the angle measuring accuracy of monopulse radars. And the influence factors of adaptive polarization cancellation are analyzed in detail by simulation. Finally, the experimental results prove that it is very effective on both active blanket jamming and active deception jamming.

Continuous wave radars usually use separate transmit and receive antennas that are close to each other. In such a configuration, the antenna crosstalk is the strongest echo acquired by the radar surveillance channel. Sidelobes of the strongest echo are spread over all range cells, limiting the dynamic range of the radar. In this paper, a mismatched filter approach is presented that substitutes classic correlation in order to suppress correlation sidelobes. Both simulations and measurements are presented.

High-resolution imaging with an airborne synthetic aperture radar (SAR) calls for precise trajectory measurements that can hardly be achieved with common navigation systems. In this paper, an efficient method called the local-quadratic map-drift autofocus is developed for the estimation of residual (uncompensated) motion errors directly from the received radar data. The map-drift autofocus is applied locally on short time intervals to estimate the cross-track components of the aircraft acceleration. The estimated acceleration is then integrated to evaluate the residual trajectory errors on the whole data frame interval. The method has been successfully tested with an X-band airborne SAR system.

Null steering has been a challenge in radar communications for the past few decades. In this paper, a novel cognitive null steering technique in frequency diverse array radars using frequency offset selection is presented. The proposed system is a complete implementable framework that provides precise and deep null placement in the range and angle locations of the interference source. The proposed system is cognitive such that the transmitter and receiver are connected via a feedback loop. System extracts interference source location parameters from the radar scene using Multiple Signal Classification, a super resolution direction of arrival estimation technique. Neural networks known for minimum computation time, and good non-linear and non-parametric approximation have been utilized for prediction of next location of the interference source. Simulation results validate the proposed frequency offset selection by demonstrating precise and deep nulls at the desired locations.

To provide short-range super-resolution ultra-wideband (UWB) radar under multi-scattering conditions, a superior wavefront extraction algorithm is proposed in this paper. Conventional correlation-based pulse separation methods based on SAGE, CLEAN or the previously introduced superior dynamic correlation method (DCM) are revised, validated, and compared. In this paper, the DCM is improved significantly by applying the Pauli scattering matrix decomposition onto the radar data. This novel wavefront extraction algorithm is called polarimetric dynamic correlation method (PDCM) and is suitable to resolve several overlapping pulses, which consist of both strong echoes and weak echoes which are masked by the strong ones. The performance of the PDCM and the comparison with alternative algorithms is carried out by a subsequent feature extraction algorithm for visual verification. Experimental validations are performed with two complex test objects, a maximum length sequence radar device (4.5–13.5 GHz) and compact dual-polarized UWB antennas.

The radar micro-Doppler signature of a target is determined by parts of the target moving or rotating in addition to the main body motion. The relative motion of these parts is characteristic for different classes of targets, e.g. the flapping motion of a bird's wings versus the spinning of propeller blades. In the present study, the micro-Doppler signature is exploited to discriminate birds and small unmanned aerial vehicles (UAVs). Emphasis is on micro-Doppler features that can be extracted from spectrograms and cepstrograms, enabling the human eye or indeed automatic classification algorithms to make a quick distinction between man-made objects and bio-life. In addition, in case of man-made objects, it is desired to further characterize the type of mini-UAV to aid the threat assessment. Also this characterization is done on the basis of micro-Doppler features.

Pedestrian Collision Mitigation Systems (PCMS) are already in the market for some years. Due to continuously evolving EuroNCAP regulations their presence will increase. Visual sensors, already capable of pedestrian classification, provide functional benefits, because the reaction behavior can be optimized when the imminent collision object is recognized as pedestrian or cyclist. Nevertheless their performance will suffer under adverse environmental conditions like darkness, fog, rain or backlight. Even in such unfavorable situations the performance of radar sensors is not significantly deteriorated. Enabling classification capability to automotive radar will further improve road safety and will lower PCMS's overall costs. In this paper, a multi-reflection-point pedestrian target model based on motion analysis is presented. Together with an appropriate sensor model, pedestrian radar signal responses can be provided for a wide range of accident scenarios. Additionally velocity separation requirements that are needed for classification of pedestrians are derived from the simulations. Besides determination of classification features, the model discloses the limits of classical radar signal processing and further offers the opportunity to evaluate parametric spectral analysis. Based on simulated and measured baseband radar signals of pedestrians one of these techniques is deeper analyzed and its enhancement especially on the velocity separation capability is evaluated.

In this paper, we present a method for multiple target two-dimensional tracking using multiple sensors with very low azimuth resolution. Due to the scarce azimuth resolution, the data association is performed using range and Doppler information only from four radars. The proposed algorithm is tailored to perform in maritime scenarios. In order to test the algorithm performance, sea clutter data have been taken under consideration when simulating data.

It is difficult to detect a stationary object in practice, especially in an unknown indoor environment, because (a) there is no distinct speed difference between the targets and the background; (b) responses of the targets are contaminated by dense unknown clutter; (c) a priori knowledge of the background is not always available for some scenarios. In this paper, a set of ultra-wideband sensors are used to detect a stationary target with a planar diffuse surface. It is shown that, the relative spectrum-shifts of the data after data-projection operation, are closely connected to the illumination-angle differences. Based on this, a detector is designed, and the location and the orientation of the target are determined. In order to mitigate the influence of clutters, a “time-shift & accumulation” scheme is designed to enhance the signal. As a consequence, the signal-to-interference-and-noise ratio is increased. In addition, results from measurement in a realistic indoor environment are provided.

This paper presents the technical realization of the multiple-input multiple-output (MIMO) radar MIRA-CLE Ka (MIMO radar-configurable in Ka-band). This system is a stationary imaging radar without any mechanical moving parts. It is highly portable, ready to use in only a couple of minutes, and it is able to process one radar image per second during continuous radar measurements in the current stage of development. In addition to the image processing, it is possible to detect changes in range of 0.1 mm in the illuminated scene. The MIMO system operates in Ka band and consists of 16 receive and 16 transmit elements, so that 256 virtual elements are generated during signal processing. The size of the antenna's frontend is about 80 cm in width. Owing to its compact size, flexibility, and realtime capability, the system offers a wide field of applications.

The popularity of small unmanned aerial vehicles (UAVs) is increasing. Therefore, the importance of security systems able to detect and classify them is increasing as well. In this paper, we propose a new approach for UAVs classification using continuous wave radar or high pulse repetition frequency (PRF) pulse radars. We consider all steps of processing required to make a decision out of the raw radar data. Before the classification, the micro-Doppler signature is filtered and aligned to compensate the Doppler shift caused by the target's body motion. Then, classification features are extracted from the micro-Doppler signature in order to represent information about class at a lower dimension space. Eigenpairs extracted from the correlation matrix of the signature are used as informative features for classification. The proposed approach is verified on real radar measurements collected with X-band radar. Planes, quadrocopter, helicopters, and stationary rotors as well as birds are considered for classification. Moreover, a possibility of distinguishing different number of rotors is considered. The obtained results show the effectiveness of the proposed approach. It provides the capability of correct classification with a probability of around 92%.

In this article, a novel motion model-based particle filter implementation is proposed to classify human motion and to estimate key state variables, such as motion type, i.e. running or walking, and the subject's height. Micro-Doppler spectrum is used as the observable information. The system and measurement models of human movements are built using three parameters (relative torso velocity, height of the body, and gait phase). The algorithm developed has been verified on simulated and experimental data.

Phase unwrapping (PU) is one of the key processing steps in reconstructing the digital elevation model (DEM) of a scene from interferometric synthetic aperture radar (InSAR) data. The PU problem entails the estimation of an absolute phase from observation of its noisy principal (wrapped) values. Recently, PU approaches based on Kalman filtering have proved their efficacy in tackling the PU problem even when strong discontinuities of the height profile and noisy data are involved. This paper presents a novel multichannel InSAR PU algorithm using several interferometric SAR images based on the extended Kalman filter. The proposed technique exploits the capability of the Kalman algorithm to simultaneously perform noise filtering, PU, and multi-sensor data fusion. The proposed method, even being a Bayesian estimator, optimally fuses height information coming from an additional maximum likelihood estimator (MLE) combining the benefits of both the Bayesian and the non-Bayesian approaches. The performance of the proposed algorithm has been tested on simulated interferometric images proving the effectiveness of the proposed method.

A fast imaging algorithm for real-time use in short-range (ultra-wideband) radar with synthetic or real-array aperture is proposed. The reflected field is presented here as a convolution of the target reflectivity and point spread function (PSF) of the imaging system. To obtain a focused 3D image, the proposed algorithm deconvolves the PSF out from the acquired data volume with high speed due to fast Fourier transform and implementation in frequency-wavenumber domain. Then the result is tested against two numerical criteria for efficiency, namely error and instability, whose optimal values can be obtained iteratively. Since the PSF differs with distance, the algorithm suits mainly applications with relatively small objects such as concealed weapon detection. Using several PSFs allows us to image a certain range of interest by their successive deconvolution from the same data. Performance of the algorithm has been evaluated experimentally and compared with that of Kirchhoff migration. Measurements were carried out by a 5–25 GHz synthetic aperture radar in the lab, and scenarios included a gun and a ceramic knife in free space, on a large metal plate, and a gun concealed on a dummy under a thick raincoat. The results demonstrate sufficient image quality obtained in a fraction of time.

In this paper, we are concerned on the receiver design and the different imaging possibilities for radar multiple input multiple output (MIMO) synthetic aperture radar (SAR). The transmission of pseudo-orthogonal waveforms gives many opportunities in reception on how to manage these different signals. We proposed to compare different resolutions and robustness for SAR measurements: single input single output (SISO) and MIMO. Moreover, we developed different SAR processings based on MIMO configuration by using the Stolt interpolation on simulations. On real data, these SAR processing will be adapted for the backprojection processing.

In this paper, problem of efficient representation of large database of target radar cross section (RCS) is investigated in order to minimize memory requirements and recognition search time, using a tree structured hierarchical wavelet representation. Synthetic RCS of large aircrafts, in the High Frequency (HF)–Very High Frequency (VHF) bands, are used as experimental data. Hierarchical trees are built using wavelet multiresolution representation and K-means clustering algorithm. Criteria used to define these hierarchical trees are described and the obtained performances are presented.

This paper presents the development of a reconfigurable receiver to undertake challenging signal processing tasks for a novel polarimetric radar system. The field-programmable gate arrays (FPGAs)-based digital receiver samples incoming signals at intermediate frequency (IF) and processes signals digitally instead of using conventional analog approaches. It offers more robust system stability and avoids unnecessary multichannel calibrations of analog circuits for a full polarimetric radar. Two kinds of dual-orthogonal signals together with corresponding processing algorithms have been investigated; the digital implementation architectures for all algorithms are then presented. Processing algorithms implemented in FPGA chips can be reconfigured adaptively regarding to different transmitted waveforms without modification of hardware. The successful development of such reconfigurable receiver extends our radar capacity and thus yields tremendous experimental flexibility for atmospheric remote sensing and polarimetric studies of ground-based targets.