Waveform Diversity and Range-Coupled Adaptive Radar Signal Processing

Waveform diversity may offer several benefits to radar systems though often at the cost of reduced sensitivity. Multi-dimensional processing schemes are known to offer many degrees of freedom, which can be exploited to suppress the ambiguity inherent to pulse compression, array processing, and Doppler frequency estimation. Spatial waveform diversity can be achieved by transmitting different but correlated waveforms from each element of an antenna array. A simple yet effective scheme is employed to transmit different waveforms in different spatial directions. A new reiterative minimum mean squared error approach entitled Space-Range Adaptive Processing, which adapts simultaneously in range and angle, is derived and shown in simulation to offer enhanced performance when spatial waveform diversity is employed relative to both conventional matched filtering and sequentially adapting in angle and then range. The same mathematical framework is utilized to develop Time-Range Adaptive Processing (TRAP) algorithm which is capable of simultaneously adapting in Doppler frequency and range. TRAP is useful when pulse-to-pulse changing of the center frequency or waveform coding is used to achieve enhanced range resolution or unambiguous ranging, respectively. The inherent computational complexity of the new multi-dimensional algorithms is addressed by segmenting the full-dimension cost functions, yielding a reduced-dimensional variants of each. Finally, a non-adaptive approach based on the multi-dimensional TRAP signal model is utilized to develop an efficient clutter cancellation technique capable of suppressing multiple range intervals of clutter when waveform diversity is applied to pulse-Doppler radar

Adaptive multibeam antennas for spacelab. Phase A: Feasibility study

The feasibility was studied of using adaptive multibeam multi-frequency antennas on the spacelab, and to define the experiment configuration and program plan needed for a demonstration to prove the concept. Three applications missions were selected, and requirements were defined for an L band communications experiment, an L band radiometer experiment, and a Ku band communications experiment. Reflector, passive lens, and phased array antenna systems were considered, and the Adaptive Multibeam Phased Array (AMPA) was chosen. Array configuration and beamforming network tradeoffs resulted in a single 3m x 3m L band array with 576 elements for high radiometer beam efficiency. Separate 0.4m x 0.4 m arrays are used to transmit and receive at Ku band with either 576 elements or thinned apertures. Each array has two independently steerable 5 deg beams, which are adaptively controlled

Autonomous time-frequency cropping and feature-extraction algorithms for classification of LPI radar modulations

Three autonomous cropping and feature extraction algorithms are examined that can be used for classification of low probability of intercept radar modulations using time-frequency (T-F) images. The first approach, Erosion Dilation Adaptive Binarization (EDAB), uses erosion and a new adaptive threshold binarization algorithm embedded within a recursive dilation process to determine the modulation energy centroid (radar's carrier frequency) and properly place a fixed-width cropping window. The second approach, Marginal Frequency Adaptive Binarization (MFAB), uses the marginal frequency distribution and the adaptive threshold binarization algorithm to determine the start and stop frequencies of the modulation energy to locate and adapt the size of the cropping window. The third approach, Fast Image Filtering, uses the fast Fourier transform and a Gaussian lowpass filter to isolate the modulation energy. The modulation is then cropped from the original T-F image and the adaptive binarization algorithm is used again to compute a binary feature vector for input into a classification network. The binary feature vector allows the image detail to be preserved without overwhelming the classification network that follows. A multi-layer perceptron and a radial basis function network are used for classification and the results are compared. Classification results for nine simulated radar modulations are shown to demonstrate the three feature-extraction approaches and quantify the performance of the algorithms. It is shown that the best results are obtained using the Choi-Williams distribution followed by the MFAB algorithm and a multi-layer perceptron. This setup produced an overall percent correct classification (Pcc) of 87.2% for testing with noise variation and 77.8% for testing with modulation variation. In an operational context, the ability to process and classify LPI signals autonomously allows the operator in the field to receive real-time results.http://archive.org/details/autonomoustimefr10945270

Innovative Adaptive Techniques for Multi Channel Spaceborne SAR Systems

Synthetic Aperture Radar (SAR) is a well-known technology which allows to coherently combine multiple returns from (typically) ground-based targets from a moving radar mounted either on an airborne or on a space-borne vehicle. The relative motion between the targets on ground and the platform causes a Doppler effect, which is exploited to discriminate along-track positions of targets themselves. In addition, as most of conventional radar, a pulsed wide-band waveform is transmitted periodically, thus allowing even a radar discrimination capability in the range direction (i.e. in distance). For side-looking acquisition geometries, the along-track and the range directions are almost orthogonal, so that the two dimensional target discrimination capabiliy results in the possibility to produce images of the illuminated area on ground. A side-looking geometry consists in the radar antenna to be, either mechanically or electronically, oriented perpendicular to the observed area. Nowadays technology allows discrimination capability (also referred to as resolution) in both alongtrack and range directions in the order of few tenths of centimeters. Since the SAR is a microwave active sensor, this technology assure the possibility to produce images of the terrain independently of the sunlight illumination and/or weather conditions. This makes the SAR a very useful instrument for monitoring and mapping both the natural and the artificial activities over the Earth’s surface. Among all the limitations of a single-channel SAR system, this work focuses over some of them which are briefly listed below: a) the performance achievable in terms of resolution are usually paid in terms of system complexity, dimension, mass and cost; b) since the SAR is a coherent active sensor, it is vulnerable to both intentionally and unintentionally radio-frequency interferences which might limit normal system operability; c) since the Doppler effect it is used to discriminate targets (assumed to be stationary) on the ground, this causes an intrinsic ambiguity in the interpretation of backscattered returns from moving targets. These drawbacks can be easily overcome by resorting to a Multi-cannel SAR (M-SAR) system

Innovative Adaptive Techniques for Multi Channel Spaceborne SAR Systems

Synthetic Aperture Radar (SAR) is a well-known technology which allows to coherently combine multiple returns from (typically) ground-based targets from a moving radar mounted either on an airborne or on a space-borne vehicle. The relative motion between the targets on ground and the platform causes a Doppler effect, which is exploited to discriminate along-track positions of targets themselves. In addition, as most of conventional radar, a pulsed wide-band waveform is transmitted periodically, thus allowing even a radar discrimination capability in the range direction (i.e. in distance). For side-looking acquisition geometries, the along-track and the range directions are almost orthogonal, so that the two dimensional target discrimination capabiliy results in the possibility to produce images of the illuminated area on ground. A side-looking geometry consists in the radar antenna to be, either mechanically or electronically, oriented perpendicular to the observed area. Nowadays technology allows discrimination capability (also referred to as resolution) in both alongtrack and range directions in the order of few tenths of centimeters. Since the SAR is a microwave active sensor, this technology assure the possibility to produce images of the terrain independently of the sunlight illumination and/or weather conditions. This makes the SAR a very useful instrument for monitoring and mapping both the natural and the artificial activities over the Earth’s surface. Among all the limitations of a single-channel SAR system, this work focuses over some of them which are briefly listed below: a) the performance achievable in terms of resolution are usually paid in terms of system complexity, dimension, mass and cost; b) since the SAR is a coherent active sensor, it is vulnerable to both intentionally and unintentionally radio-frequency interferences which might limit normal system operability; c) since the Doppler effect it is used to discriminate targets (assumed to be stationary) on the ground, this causes an intrinsic ambiguity in the interpretation of backscattered returns from moving targets. These drawbacks can be easily overcome by resorting to a Multi-cannel SAR (M-SAR) system

Space-time reduced rank methods and CFAR signal detection algorithms with applications to HPRF radar

In radar applications, the statistical properties (covariance matrix) of the interference are typically unknown a priori and are estimated from a dataset with limited sample support. Often, the limited sample support leads to numerically ill-conditioned radar detectors. Under such circumstances, classical interference cancellation methods such as sample matrix inversion (SMI) do not perform satisfactorily. In these cases, innovative reduced-rank space-time adaptive processing (STAP) techniques outperform full-rank techniques. The high pulse repetition frequency (HPRF) radar problem is analyzed and it is shown that it is in the class of adaptive radar with limited sample support. Reduced-rank methods are studied for the HPRF radar problem. In particular, the method known as diagonally loaded covariance matrix SMI (L-SMI) is closely investigated. Diagonal loading improves the numerical conditioning of the estimated covariance matrix, and hence, is well suited to be applied in a limited sample support environment. The performance of L-SMI is obtained through a theoretical distribution of the output conditioned signal-to-noise ratio of the space-time array. Reduced-rank techniques are extended to constant false alarm rate (CFAR) detectors based on the generalized likelihood ratio test (GLRT). Two new modified CFAR GLRT detectors are considered and analyzed. The first is a subspace-based GLRT detector where subspace-based transformations are applied to the data prior to detection. A subspace transformation adds statistical stability which tends to improve performance at the expense of an additional SNR loss. The second detector is a modified GLRT detector that incorporates a diagonally loaded covariance matrix. Both detectors show improved performance over the traditional GLRT

Space/time/frequency methods in adaptive radar

Radar systems may be processed with various space, time and frequency techniques. Advanced radar systems are required to detect targets in the presence of jamming and clutter. This work studies the application of two types of radar systems. It is well known that targets moving along-track within a Synthetic Aperture Radar field of view are imaged as defocused objects. The SAR stripmap mode is tuned to stationary ground targets and the mismatch between the SAR processing parameters and the target motion parameters causes the energy to spill over to adjacent image pixels, thus hindering target feature extraction and reducing the probability of detection. The problem can be remedied by generating the image using a filter matched to the actual target motion parameters, effectively focusing the SAR image on the target. For a fixed rate of motion the target velocity can be estimated from the slope of the Doppler frequency characteristic. The problem is similar to the classical problem of estimating the instantaneous frequency of a linear FM signal (chirp). The Wigner-Ville distribution, the Gabor expansion, the Short-Time Fourier transform and the Continuous Wavelet Transform are compared with respect to their performance in noisy SAR data to estimate the instantaneous Doppler frequency of range compressed SAR data. It is shown that these methods exhibit sharp signal-to-noise threshold effects. The space-time radar problem is well suited to the application of techniques that take advantage of the low-rank property of the space-time covariance matrix. It is shown that reduced-rank methods outperform full-rank space-time adaptive processing when the space-time covariance matrix is estimated from a dataset with limited support. The utility of reduced-rank methods is demonstrated by theoretical analysis, simulations and analysis of real data. It is shown that reduced-rank processing has two effects on the performance: increased statistical stability which tends to improve performance, and introduction of a bias which lowers the signal-to-noise ratio. A method for evaluating the theoretical conditioned SNR for fixed reduced-rank transforms is also presented

Mismatched Processing for Radar Interference Cancellation

Matched processing is a fundamental filtering operation within radar signal processing to estimate scattering in the radar scene based on the transmit signal. Although matched processing maximizes the signal-to-noise ratio (SNR), the filtering operation is ineffective when interference is captured in the receive measurement. Adaptive interference mitigation combined with matched processing has proven to mitigate interference and estimate the radar scene. A known caveat of matched processing is the resulting sidelobes that may mask other scatterers. The sidelobes can be efficiently addressed by windowing but this approach also comes with limited suppression capabilities, loss in resolution, and loss in SNR. The recent emergence of mismatch processing has shown to optimally reduce sidelobes while maintaining nominal resolution and signal estimation performance. Throughout this work, re-iterative minimum-mean square error (RMMSE) adaptive and least-squares (LS) optimal mismatch processing are proposed for enhanced signal estimation in unison with adaptive interference mitigation for various radar applications including random pulse repetition interval (PRI) staggering pulse-Doppler radar, airborne ground moving target indication, and radar & communication spectrum sharing. Mismatch processing and adaptive interference cancellation each can be computationally complex for practical implementation. Sub-optimal RMMSE and LS approaches are also introduced to address computational limitations. The efficacy of these algorithms is presented using various high-fidelity Monte Carlo simulations and open-air experimental datasets