This article gives a brief overview of the first phased array radar systems that were realised in Germany: The MAMMUT, at the end of the Second World War (1944), and the ELRA, from 1972 onwards. Finally, we take a brief look at the early industrial developments of phased array radar in Germany.

SAR imaging using compressive sensing methods has been widely discussed in the literature. In this paper, we focus on the application of a recently developed compressive sensing (CS) method, turbo shrinkage-thresholding (TST), which is a refinement of algorithms of the type Iterative Shrinkage-Tresholding Algorithm (ISTA). It promises fast convergence and accurate results. We will demonstrate the SAR application of TST to measurements made with the airborne experimental radar (AER) system of Fraunhofer FHR, including extrapolation of the spectrum associated with finer resolution. In addition, we will investigate TST processing of multichannel SAR (MSAR) with subapertures along the track in a low PRF situation, which is a well-known method for obtaining highresolution wide-swath modes (HRWS).

This paper presents an innovative solution to tackle challenges associated with elevated range sidelobes and computational complexity in the context of Code Division Multiple Access (CDMA) and Multiple-Input Multiple-Output (MIMO) radar systems. The solution involves the incorporation of Sparse Randomized Projections (SRP) as an efficient approach for 2D Compressive Sensing (CS) MIMO radar.

AbstractThe authors focus on the waveform design for Code Division Multiple Access Multiple Input Multiple Output (CDMA‐MIMO) radar systems, with a specific emphasis on Compressed Sensing (CS) based target estimation. The selection of an appropriate waveform is a critical determinant in the effectiveness of estimation algorithms. Recent studies show the possibilities of optimising waveform parameters to improve the efficiency of CS based estimation. The authors introduce an optimisation framework designed to modify the phase components of code sequences used in CS‐CDMA MIMO radar systems. The objective of this optimisation is to minimise the l∞ norm of off‐diagonal elements within the Gramian matrix of the underlying sensing matrix, focusing on phase modulation of the waveform. Solving this optimisation problem requires dealing with a non‐convex, combinatorial and non‐linear scenario. Simulated Annealing is employed as the solution technique. To assess the effectiveness of the proposed optimisation approach, the resulting optimised sequence is rigorously compared against well‐established Hadamard and Gold sequences across various performance metrics. These metrics encompass correlation properties, ambiguity function behaviour, recovery percentage and recovery error. The study demonstrates that the generated poly‐phase sequences outperform existing sequences, leading to significantly improved target reconstruction results in the context of CDMA‐MIMO radar systems with CS‐based estimation.

In this paper we consider a blind de-factorization problem for linear measurements of an unknown sparse vector that are distorted by a multiplicative interference signal that is also unknown. This scenario applies, for example, to SAR measurements in the slow time domain that are corrupted by uncompensated motion errors. We will show that lifting into a matrix space leads to the question of how to reconstruct a row-sparse low-rank matrix from linear measurements, and to a solution based on the minimization of a cost function containing both the nuclear norm and a mixed $\ell_{2} / \ell_{1}$ term.

Single-pass bistatic Synthetic Aperture Radar (SAR) Tomography, which involves a space-borne transmitter and a ground-based multi-channel receiver, presents an alternative to traditional TomoSAR. Typically, spectral-based reconstruction methods that either, require a large number of receiving elements or a specific configuration for a limited number of elements are considered, for achieving a desired resolution. However, these algorithms perform poorly for closed target scenarios.In this study, we propose a novel approach for estimating the elevation profile using a reduced number of channels, without the need for a specific array configuration. We treat the problem as an underdetermined system of equations and employ Compressed Sensing (CS) algorithms to solve it. The performance of the proposed approach is compared against the spectral-based approach using Monte Carlo simulations and demonstrated on real data measurements.

SAR Tomography (TomoSAR) leverages the utilization of multiple baselines to gather data about an area, enabling the acquisition of information not only in the range-azimuth direction but also in the elevation direction. This additional information in the elevation direction primarily consists of point scatterers, with one or more scatterers typically present within a single resolution cell. This research paper examines two distinct methodologies for detecting multiple scatterers in TomoSAR: the conventional Generalized Likelihood Ratio Test (GLRT) technique and a compressive sensing (CS) approach known as the Iterative Soft-Thresholding Algorithm (ISTA). The results are examined for a simulated case of two scatterers and a real case using the TerraSAR-X dataset.

Existing compressed sensing algorithms fail when applied to radar target detection in the presence of a large gap in the frequency band, i.e., presence of signals in separate, discontinuous bands. A new algorithm based on a subdivision-fusion scheme is proposed to solve this problem. The main goal is to use a structured sensing matrix based on radar signals to an advantage and obtain a good range resolution in spite of high coherence. Parameters influencing the performance of the algorithm are discussed. Simulative examples and results based on real measurement data are presented. The results show superior performance of the proposed method in the presence of band gaps.

A system of co-located narrow-band radars operating in disjoint frequency bands may be viewed as a gapped-band system, which can then be cast into a compressed sensing (CS) formulation for improved scene resolution. However, such a gap increases the coherence of the sensing matrix, causing existing CS algorithms to fail. A subdivision-fusion algorithm is discussed to tackle this problem, along with its super-resolution capabilities for different band-gaps. Test results on real radar data are presented. Effects of horizontal random projection of such a gapped-sensing matrix are also discussed.

Compressed Sensing (CS) has been proven to be an effective technique to handle computational loads of multiple input multiple output (MIMO) radar systems; additionally, when considering Code Division Multiple Access (CDMA) MIMO radars high sidelobes, which are artefacts of the waveform, can be mitigated. However, the question as to how the correlation properties of these sequences or the choice of array geometry contribute towards performance of CS algorithms, individually or jointly, has not been analysed. In this paper, we present a study investigating waveform orthogonality and array geometry as parameters influencing the CS algorithms reconstruction performance. The numerical simulations have been carried out for a 2 dimensional range-angle (RA) scene with multiple targets, reconstructed by a CS-CDMA MIMO system, transmitting different code sequences in combination with different array geometries. The results validate how the right combination of the array configuration and transmission waveform leads to lower estimation errors and an increased probability of success.

The resolution requirements of modern radar applications are increasing rapidly and cannot be fulfilled by the limited number of wide-band radar systems. Many approaches have been explored to solve this problem under the topic of super-resolution. In this paper, we propose a hybrid algorithm for resolution improvement, where we aim to combine the adaptability of deep neural networks with the reliability and expertise of traditional domain-specific SAR processing.

In this paper, an optimization methodology for re-positioning antenna elements of a collocated Compressed Sensing (CS) based Multiple Input Multiple Output (MIMO) radar, to improve target detection performance, by minimizing the mutual coherence of the associated sensing matrix has been suggested. We initialize the problem as a mutual coherence of the sensing matrix resulting from a simple 3Tx/4Rx Uniform Linear Array (ULA) restricted by an array aperture of specified size, and then reposition the elements within the restricted aperture such that the value of mutual coherence reduces. The optimization problem is formulated as minimizing the l ∞ norm of the Gramian of the associated sensing matrix, the global optimization solver simulated annealing is considered to solve the nonconvex problem. The optimized array’s performance is evaluated against a ULA, Co-prime array, and Sparse array by comparing metrics such as the probability of perfect reconstruction (Recovery percentage) and Recovery error (root mean square error (rmse)) for scenes with multiple targets and different SNR values, using Monte Carlo simulations. The study demonstrates the methodology to generate a random array, which results in low mutual coherence of its respective sensing matrix, which consequently results in improved performance of the CS-MIMO radar.

Recognition and Identification of targets are crucial steps in the radar signal processing chain. Due to their high resolution, imaging radars are a well suitable choice for these tasks. This paper presents a framework, which is based on sparse decomposition of radar images, to identify different kinds of scattering mechanisms. As application example, the separation of echoes from jet engines and isotropic scattering centers is used. To model the echoes from jet engines a waveguide model is used and the separation of the echoes is done with an algorithm called morphological component analysis. To evaluate the ability of the algorithm to separate engine echoes and isotropic point scattering centers, a simulation with these two types of echoes is performed. Finally, a real data set of the Tracking and Imaging Radar (TIRA) of Fraunhofer FHR is used to show results with a real radar system.

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.

A new algorithm has been developed which integrates range alignment and autofocus for Inverse Synthetic Aperture Radar (ISAR) images to the framework of Compressed Sensing. This is accomplished by inserting a joint range alignment and phase correction step to the iteration loop of an Iterative Shrinkage and Thresholding Algorithm (ISTA). Results for focusing alone and for superresolution are presented which demonstrate the power of the algorithm.

Chapter Contents: 8.1 Introduction 8.2 State of the art in scattering identification 8.3 Waveguide scattering model 8.3.1 Propagation of electromagnetic waves in waveguides 8.3.2 Example of LFM pulse propagation inside a waveguide 8.3.3 Jet engine scattering model 8.3.4 Scattering of an LFM waveform 8.4 Sparse decomposition framework 8.4.1 Terminology and problem description 8.4.2 Morphological component analysis 8.5 Results 8.5.1 Simulation of objects with different shapes 8.5.2 Recognition of jet engines in ISAR images 8.6 Conclusion and future work References

Due to the frequency constraints imposed by the necessary coexistence of radar and communications, the increasing range resolution requirements of modern radar systems can only be achieved by fusing multiple frequency bands. There are a variety of published approaches to solve this task. In this paper we will present two algorithms, the first one based on a high resolution spectral estimation method and the second one based on a compressive sensing algorithm.