Nonlinear Suppression of Range Ambiguity in Pulse Doppler Radar

Coherent pulse train processing is most commonly used in airborne pulse Doppler radar, achieving adequate transmitter/receiver isolation and excellent resolution properties while inherently inducing ambiguities in Doppler and range. First introduced by Palermo in 1962 using two conjugate LFM pulses, the primary nonlinear suppression objective involves reducing range ambiguity, given the waveform is nominally unambiguous in Doppler, by using interpulse and intrapulse coding (pulse compression) to discriminate received ambiguous pulse responses. By introducing a nonlinear operation on compressed (undesired) pulse responses within individual channels, ambiguous energy levels are reduced in channel outputs. This research expands the NLS concept using discrete coding and processing. A general theory is developed showing how NLS accomplishes ambiguity surface volume removal without requiring orthogonal coding. Useful NLS code sets are generated using combinatorial, simulated annealing optimization techniques - a general algorithm is developed to extended family size, code length, and number of phases (polyphase coding). An adaptive reserved code thresholding scheme is introduced to efficiently and effectively track the matched filter response of a target field over a wide dynamic range, such as normally experienced in airborne radar systems. An evaluation model for characterizing NLS clutter suppression performance is developed - NLS performance is characterized using measured clutter data with analysis indicating the proposed technique performs relatively well even when large clutter cells exist

Airborne Radar Interference Suppression Using Adaptive Three-Dimensional Techniques

This research advances adaptive interference suppression techniques for airborne radar, addressing the problem of target detection within severe interference environments characterized by high ground clutter levels, levels, noise jammer infiltration, and strong discrete interferers. Two-dimensional (2D) Space-Time Adaptive Processing (STAP) concepts are extended into three-dimensions (3D) by casting each major 2D STAP research area into a 3D framework. The work first develops an appropriate 3D data model with provisions for range ambiguous clutter returns. Adaptive 3D development begins with two factored approaches, 3D Factored Time-Space (3D-FTS) and Elevation-Joint Domain Localized (Elev-JDL). The 3D adaptive development continues with optimal techniques, i.e., joint domain methods. First, the 3D matched Filter (3D-MF) is derived followed by a 3D Adaptive Matched Filter (3D-AMF) discussion focusing on well-established practical limitations consistent with the 2D case. Finally, a 3D-JDL method is introduced. Proposed 3D Hybrid methods extend current state-of-the-art 2D hybrid methods. The initial 3D hybrid, a functional extension of the 2D technique, exhibits distinct performance advantages in heterogeneous clutter. The final 3D hybrid method is virtually impervious to discrete interference

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

Bandpass filters for unconstrained target recognition and their implementation in coherent optical correlators

An up-dateable correlator is simulated which is based on the non-degenerate four wave mixing (NDFWM) interaction in the photorefractive material bismuth silicon oxide (Bi12SiO20). Specifically, it is shown that variable bandpass filters can be implemented directly in the correlator by adjusting the relative strengths of the signal and reference beams used to write the Fourier transform hologram into the photorefractive. The synthetic discriminant function (SDF) method of grey-level multiplexing is reviewed. A bandpass modification of this technique is used in the design of a multiplexed filter for the recognition of an industrial test component from a limited number of known stable state orientations when viewed from an overhead camera position. Its performance in this task when implemented in the up-dateable correlator is assessed through simulation. The conclusion of this work is that filter multiplexing must be used judiciously for orientation invariant recognition. Only a limited number of images, typically under ten, may be multiplexed into each filter since correlation peak heights and peak-to-sidelobe ratios inevitably progressively deteriorate as images are added to the filter. The effect of severe amplitude disruptions in the frequency plane on correlation peak localisation is examined. In two or higher dimensions simulations show the localisation is very robust to this disruption; an analysis is developed to indicate the reason for this. The effect is exploited by the implementation of an algorithm that locally removes the spatial frequencies that exhibit close phase matching between intra- and inter-class images. The inter-class response can be forced to zero while simultaneously improving the intra-class tolerance to orientation changes. The technique is assessed through simulation with images of two types of motor vehicle, in a variety of orientations, and shown to be effective in improving discrimination and intra-class tolerance for examples in which these were initially very poor. Bandpass filters are experimentally implemented in a joint transform correlator (JTC) based on a NDFWM interaction in Bi12SiO20. The JTC is described and its full bandwidth performance initially assessed. As anticipated from the previous considerations, inter-class discrimination was high but the intra-class tolerance very poor due to the high sensitivity of the filter. The difference of Gaussian approximation to a Laplacian of a Gaussian filter is described and its experimental implementation in the JTC detailed. Experimental results are presented for the orientation independent recognition of a car while maintaining discrimination against another car. An intra-class to inter-class correlation ratio of 7.5 dB was obtained as a best case and 3.6 dB as a worst case, the intra-class variation being at 11 ° increments in orientation at zero elevation angle. The results are extrapolated to estimate that approximately 80 filters would be required for a full 2 steradian orientation coverage. The implementation of the frequency removal technique and the Wiener filter in the JTC is briefly considered in conclusion to this work

Electronic scan weather radar: scan strategy and signal processing for volume targets

2013 Fall.Includes bibliographical references.Following the success of the WSR-88D network, considerable effort has been directed toward searching for options for the next generation of weather radar technology. With its superior capability for rapidly scanning the atmosphere, electronically scanned phased array radar (PAR) is a potential candidate. A network of such radars has been recommended for consideration by the National Academies Committee on Weather Radar Technology beyond NEXRAD. While conventional weather radar uses a rotating parabolic antenna to form and direct the beam, a phased array radar superimposes outputs from an array of many similar radiating elements to yield a beam that is scanned electronically. An adaptive scan strategy and advanced signal designs and processing concepts are developed in this work to use PAR effectively for weather observation. An adaptive scan strategy for weather targets is developed based on the space-time variability of the storm under observation. Quickly evolving regions are scanned more often and spatial sampling resolution is matched to spatial scale. A model that includes the interaction between space and time is used to extract spatial and temporal scales of the medium and to define scanning regions. The temporal scale constrains the radar revisit time while the measurement accuracy controls the dwell time. These conditions are employed in a task scheduler that works on a ray-by-ray basis and is designed to balance task priority and radar resources. The scheduler algorithm also includes an optimization procedure for minimizing radar scan time. In this research, a signal model for polarimetric phased array weather radar (PAWR) is presented and analyzed. The electronic scan mechanism creates a complex coupling of horizontal and vertical polarizations that produce the bias in the polarimetric variables retrieval. Methods for bias correction for simultaneous and alternating transmission modes are proposed. It is shown that the bias can be effectively removed; however, data quality degradation occurs at far off boresight directions. The effective range for the bias correction methods is suggested by using radar simulation. The pulsing scheme used in PAWR requires a new ground clutter filtering method. The filter is designed to work with a signal covariance matrix in the time domain. The matrix size is set to match the data block size. The filter's design helps overcome limitations of spectral filtering methods and make efficient use of reducing ground clutter width in PAWR. Therefore, it works on modes with few samples. Additionally, the filter can be directly extended for staggered PRT waveforms. Filter implementation for polarimetric retrieval is also successfully developed and tested for simultaneous and alternating staggered PRT. The performance of these methods is discussed in detail. It is important to achieve high sensitivity for PAWR. The use of low-power solid state transmitters to keep costs down requires pulse compression technique. Wide-band pulse compression filters will partly reduce the system sensitivity performance. A system for sensitivity enhancement (SES) for pulse compression weather radar is developed to mitigate this issue. SES uses a dual-waveform transmission scheme and an adaptive pulse compression filter that is based on the self-consistency between signals of the two waveforms. Using SES, the system sensitivity can be improved by 8 to 10 dB

Windshear detection radar signal processing studies

This final report briefly summarizes research work at Clemson in the Radar Systems Laboratory under the NASA Langley Research Grant NAG-1-928 in support of the Antenna and Microwave Branch, Guidance and Control Division, program to develop airborne sensor technology for the detection of low altitude windshear. A bibliography of all publications generated by Clemson personnel is included. An appendix provides abstracts of all publications

Radar Technology

In this book “Radar Technology”, the chapters are divided into four main topic areas: Topic area 1: “Radar Systems” consists of chapters which treat whole radar systems, environment and target functional chain. Topic area 2: “Radar Applications” shows various applications of radar systems, including meteorological radars, ground penetrating radars and glaciology. Topic area 3: “Radar Functional Chain and Signal Processing” describes several aspects of the radar signal processing. From parameter extraction, target detection over tracking and classification technologies. Topic area 4: “Radar Subsystems and Components” consists of design technology of radar subsystem components like antenna design or waveform design

Waveform-Diverse Stretch Processing

Stretch processing with the use of a wideband LFM transmit waveform is a commonly used technique, and its popularity is in large part due to the large time-bandwidth product that provides fine range resolution capabilities for applications that require it. It allows pulse compression of echoes at a much lower sampling bandwidth without sacrificing any range resolution. Previously, this technique has been restrictive in terms of waveform diversity because the literature shows that the LFM is the only type of waveform that will result in a tone after stretch processing. However, there are also many examples in the literature that demonstrate an ability to compensate for distortions from an ideal LFM waveform structure caused by various hardware components in the transmitter and receiver. This idea of compensating for variations is borrowed here, and the use of nonlinear FM (NLFM) waveforms is proposed to facilitate more variety in wideband waveforms that are usable with stretch processing. A compensation transform that permits the use of these proposed NLFM waveforms replaces the final fast Fourier transform (FFT) stage of the stretch processing configuration, but the rest of the RF receive chain remains the same. This modification to the receive processing structure makes possible the use of waveform diversity for legacy radar systems that already employ stretch processing. Similarly, using the same concept of compensating for distortions to the LFM structure along with the notion that a Fourier transform is essentially the matched filter bank for an LFM waveform mixed with an LFM reference, a least-squares based mismatched filtering (MMF) scheme is proposed. This MMF could likewise be used to replace thefinal FFT stage, and can also facilitate the application of NLFM waveforms to legacy radar systems. The efficacy of these filtering approaches (compensation transform and least-squares based MMF) are demonstrated in simulation and experimentally using open-air measurements and are applied to different scenarios of NLFM waveform to assess the results and provide a means of comparison between the two techniques

Radar: Theory and Insight

Radar principles and design considerations are reviewed, using both analytical and heuristic methods. Emphasis is placed on achieving physical insight while maintaining technical rigor. Waveform properties and signal detectability maximization through matched filtering is discussed in detail. The Moving Target Indicator radar system is presented as an illustration of digital signal processing technology applied to modern radar systems