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Doppler velocity estimation in pulse-Doppler radar is done by evaluating the target returns of bursts of pulses. While this provides convenience and accuracy, it requires multiple pulses. In adaptive and cognitive radar systems, the ability to adapt on consecutive pulses, instead of bursts, brings potential performance benefits. Hence, with radar transceiver arrays growing increasingly larger in their number of elements over the years, it may be time to re-evaluate how Doppler velocity can be estimated when using large planar arrays. In this work, we present variance bounds on the estimation of velocity using the Doppler shift as it appears in the array model. We also propose an efficient method of performing the velocity estimation and we verify its performance using Monte Carlo simulations.

In the past decades, there has been an extensive research interest in the areas of both waveform diversity/design and advanced signal processing algorithms departing from the more classical solutions based on Linear Frequency Modulated (LFM) pulses and Matched Filters (MF). In the waveform diversity community, especially within the context of spectrum sharing, MIMO and cognitive radars, several waveform optimization and design methodologies have been studied, see [1], [2] and references therein. In parallel to waveform design, several signal processing techniques have also been proposed which exploit some kind of prior knowledge and/or iterative algorithms to improve the performance of the more classical MF, such as the Adaptive Pulse Compression(APC) [3], MUSIC [4], CLEAN [5] and Sparse Signal Processing (SSP) [6], [7]. In this paper we present some results and examples to show how the combination of waveform design with SSP can lead to improved performance in radar compared to the more classical approach.

Automotive engineers are repeatedly faced with the issue of choosing radars with the best angular resolution while having reduced processing times. In this paper, a novel extrapolation method is presented to estimate the amplitudes and phases for virtual antenna elements at the receiver periphery. This method has shown to resolve two targets where the (real) baseline receiver antenna cannot. Furthermore, a comparison study is provided which considers different extrapolation methods that can be applied to the system. Together with other techniques for improving resolution, such as Multiple-Input Multiple-Output (MIMO) radars, beamsteering for increased power distribution, and compressed sensing for optimising sampling requirements, a radar system based on the fusion of these techniques can provide a powerful new platform which can fast process and accurately detect targets.

In this article a novel waveform based on agile (or irregular) pulse intervals is presented as a potential approach to simultaneously resolve both range and Doppler ambiguities using a single burst in radar measurements containing very far away and/or very fast targets. The paper shows that, the combination of irregularly spaced transmitted pulses with Sparse Signal Processing (SSP) on receive can be used to resolve radar ambiguous measurements within a single burst, in contrast to the more conventional PRF Staggering approach which uses multiple bursts, each with a different pulse repetition frequency. In addition to reducing the total time needed to measure the target parameters unambiguously, the proposed waveform offers also some robustness against certain types of Electronic Counter Measures. The claims of the paper are validated by means of simulations as well as with a set of experimental measurements that were carried out at the University of Warsaw, which preliminarily confirm our findings.

Classification modes aimed for slow events, such as human gait, using micro-Doppler measurements are long, and therefore not suitable for use during time critical radar operation. When these modes are interruptible by other radar modes they might become more acceptable. In this paper, sparse signal processing, in particular the SL0-algorithm, is investigated for interpolating interrupted radar measurements which subsequently can be used for classification of human gaits. The performance of a k-NN classifier with PCA-based feature extraction was improved significantly when the data were interpolated as compared to using the interrupted data without interpolation.

In this paper we consider the problem of direction of arrival estimation from a set of beamformed measurements corrupted by correlated Gaussian noise. We investigate the performance of sparse signal processing (SSP) on correlated and pre-whitened data, focusing on accuracy and false alarm rate. We considered both a fixed-grid and a gridless method, and performed several simulations. From our analysis we conclude that SSP applied to tapered and beamformed data without prewhitening is robust, but results in a lower accuracy than in the pre-whitened case. Furthermore, by comparing the two methods, we also observe that, for the scenario considered, the fixed-grid method performs as good as the gridless one.

In this paper we present some preliminary results on antenna array extrapolation for Direction Of Arrival (DOA) estimation using Sparse Reconstruction (SR). The objective of this study is to establish wether it is possible to achieve with an array of a given physical length the performance (in terms of accuracy, resolution and sidelobe level) of an equivalent larger array by using SR. The difference between our work and previous publications on DOA estimation using SR lies in the fact that we do not use SR as a beamformer, but instead we use the sparse solution for aperture extrapolation. We adopt an approach similar to the one of Swingler and Walker in [1], where the extrapolated sensor data are first tapered and then beamformed to suppress sidelobes of strong interfering targets, resulting in improved detection of weak targets.

Synthetic Aperture Radar (SAR) systems are becoming increasingly important sensors in as well the military environment as in the civilian market. In this keynote presentation an overview will be given over more than 2 decades of SAR system∼ and SAR application development at TNO in the Netherlands. The overview will highlight some characteristics of early large scale active phased array SAR/GMTI systems as well as small scale, lightweight systems. Furthermore developments in signal processing will be assessed since they are equally important to the operational use of these radar systems. These developments come together in a lightweight digital array SAR system that is currently under development. This light weight SAR has been designed for short range tactical UAVs, and consists of a fully digital receive array, and a very compact active transmit antenna. This paper provides details of the design of this new SAR system.

In this paper some results are presented on detection performance of radar using Compressive Sensing. Compressive sensing is a recently developed theory which allows reconstruction of sparse signals with a number of measurements much lower than implied by the Nyquist rate. In this work the behavior of detection and false alarm performance of a CS based radar using a stepped frequency waveform is analyzed. The results are obtained via simulations with known noise levels for several different scenarios (e.g. single and multiple targets, varying compression factors and Signal to Noise Ratios (SNR). Performance of CS reconstruction and detection are analyzed by means of Receiver Operating Characteristic (ROC) curves and compared to conventional Matched Filtering

In this paper we investigate the use of Frequency Modulated Continuous Wave (FMCW) radars for detecting life-sign of people, i.e. breathing and heartbeat. An optimum frequency has been selected to observe life-sign, taking into consideration also design factors, such as bandwidth availability and interference with other systems. A new compact X-band FMCW radar has been built at TNO laboratories and experimental results are presented here, which demonstrate the ability of this new system to detect life-sign.

Importance sampling (IS) techniques are applied to space-time adaptive processing (STAP) radar detection algorithms for performance characterization via fast estimation of false alarm probabilities (FAP’s). The work here builds on and extends the initial thrust in this area provided in a recent paper, [8]. The theory of the normalized matched filter (NMF) and normalized adaptive MF (NAMF) detectors is briefly discussed and a new variant, the envelope-law NAMF detector, is presented. New IS biasing techniques, using rotation of data vectors and 2-dimensional biasing, are proposed and used for fast simulation. The envelope (or E-NAMF) detector is also analyzed and its detection probability is compared with that of the NAMF detector for fluctuating and non-fluctuating targets in homogeneous and nonhomogeneous backgrounds.