Principles of modern radar / *Vol. 1*@Basic priciples

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His research in radar technology, signal processing techniques, and related subjects has resulted in over 75 technical papers, research papers, and book chapters. Holm holds a Ph. Principles of Modern Radar : Basic Principles. Mark A. Richards , William A. Holm , Jim Scheer. Principles of Modern Radar focuses on four key areas: Basic concepts, such as the the radar range equation and threshold detection Radar signal phenomenology, such as radar cross section models, clutter, atmospheric effects, and Doppler effects Descriptions of all major subsystems of modern radars, such as the antenna, transmitter, receiver, including modern architectural elements such as exciters, and advanced signal processors Signal and data processing basics, from digital signal processing DSP fundamentals, through detection, Doppler processing, waveforms and pulse compression, basic imaging concepts, and tracking fundamentals.

Scheer , William L. Melvin , Mark A. Richards , William L. They attempt to detect, locate, characterize, and possibly track aircraft, missiles, ships, satellites, personnel, metallic objects, moving ground vehicles, buried objectseven mold growing within building walls. With such a wide variety of radar platforms and targets, the process of taxonomizing specic radars and their goals is a daunting task.

However, considering two primary radar super modes is often general enough to cover most radar objectives. The techniques in this text correspond to one or both of these modes: Moving target indication MTI : the detection, location, characterization, and tracking of moving objects, such as missiles, aircraft, ground vehicles, and personnel so-called dismounts.

Imaging radar: the high-resolution estimation of the electromagnetic backscatter from stationary or moving objects that yields a spatial image of the target in one, two, or even higher dimensions. One-dimensional images are called high-range resolution HRR proles, whereas two-dimensional views are called synthetic aperture radar SAR images.

When the radar is stationary and the target is moving or when both platforms are moving, the corresponding imaging mode is usually called inverse synthetic aperture radar ISAR. In the MTI mode, dots on a display are the primary radar product.

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Figure is an example of ground target detections on a topographical map obtained via a ground moving target indication GMTI airborne radar mode. The quality of each dot is a result of the system design and signal processing applied to the received reections from target and clutter as well as the systems ability to mitigate radio frequency interference RFI. Radar detection is based on two models, or hypotheses: the null hypothesis, H0 ; and the alternative hypothesis, H1.

The null hypothesis presumes the target is not present in the chosen radar data, whereas the alternative hypothesis corresponds to the case of target signal embedded in interfering signals consistent with the null hypothesis viz. Each of the signals under the null hypothesis case is stochastic: the complex envelope of the return is derived from a particular statistical distribution and follows a certain temporal behavior.

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For example, the return from a given clutter patch is commonly assumed to have a complex envelope drawn from a Rayleigh distribution complex Gaussian voltage and a voltage response that decorrelates over time according to a Billingsley model [2] for an overland collection or Gaussian correlation model over water [3]. Likewise, the target response is stochastic.

The corresponding H1 distribution typically appears. The overlap between the null and alternative hypothesis distributions leads to ambiguity in the decision-making process: a decision region determined by a threshold setting corresponding to one model may also lead to a false declaration of the opposite model. These false declarations are either false alarms the alternative hypothesis is chosen when in fact no target is present or missed detections the null hypothesis is chosen when in fact a target is present.

The optimal detector follows from the likelihood ratio test LRT and involves operations on collected radar signals usually after some preprocessing ; a sufcient statistic, x , is a canonical detector formulation [4, 5]. Identifying the region where sufcient statistic outputs likely correspond to the alternative versus null hypotheses with a specied Type I error false alarm rate requires knowledge of the joint probability distributions under both hypotheses: p x H0 is the probability density function PDF for the null hypothesis, and p x H1 is the PDF for the alternative hypothesis.

Figure depicts the detection process. The area under p x H1 to the right of gives the probability of detection PD , whereas the area under p x H0 to the right of gives the probability of false alarm PF A. As seen from this depiction, the two distributions overlap, and the only way to increase PD is to lower and accept a higher PF A. Alternately, one might ask if there is a strategy to increase the separation between the null and alternative hypothesis distributions. Generally, this increased separation can be achieved via the appropriate exploitation of the radar measurement space, or degrees of freedom DoFs , and advanced processing methods like ADBF and STAP.

The objective in exploiting DoFs is to identify a measurement space where the target and interference e. For example, spatial and fast-time DoFs are used to efciently mitigate the impact of wideband noise jamming on the detection of a target located in proximity to the jammer, but still at a slightly different angle of arrival. Advanced processing methods combine the measurement DoFs in the most effective manner possible. This gure depicts H0 and H1 probability density functions for the sufcient decision statistic, along with threshold setting,.

The probability of false alarm, PF A , is the area under the null hypothesis distribution curve to the right of the threshold, whereas the probability of detection is the area under the alternative hypothesis curve to the right of. The net objective of DoF selection and advanced processing methods in MTI radar is to increase the separation of the two distributions in Figure Major sections of this text are devoted to examining these sophisticated techniques of critical importance to modern radar functionality.

Principles of Modern Radar

The imaging radar mode typically involves moving the radar through angle while viewing a stationary target [6, 7]. In the HRR case, a wideband waveform is used to characterize the target range response at that particular viewing angle. As the radar moves through angle, the range between each of the various scatterers comprising the scene will vary in a manner consistent with the changing geometry.

The changing range results in a time-varying phase that multiplies a complex gain term proportional to the square root of the scatterers radar cross section RCS. Each resolvable scattering cell in the unambiguous region of interest exhibits a unique phase history. Figure depicts a SAR collection geometry, where L S A R is the synthetic aperture length, ro is the range from the aperture reference point to scene center, r t is the time-varying range to a scatterer of interest, v p is the platform velocity in the x-direction, t is the independent variable time, and c t is the time-varying cone angle measured from the platform velocity vector aligned with the x-axis.


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From this gure, the reader can envision the time variation of r t or c t as the platform moves along the synthetic aperture baseline. FIGURE SAR collection geometry showing platform moving along x-axis with velocity, v p , and a stationary point target passing through the gray illumination beam with time-varying range, r t. The platform is shown at the origin of the collection, and time varies from 0. The received radar signal is the summation of the returns from multiple, resolvable scatterers within the scene. Unresolvable scatterers within each cell add coherently, yielding an effect known as speckle where some distributed scatterer responses appear brighter than others.

A matched lter designed to the phase history of a specied scattering cell, appropriately normalized and projected into the ground plane, yields an estimate of the corresponding RCS. Figure is an example of a 1 m spotlight SAR image collected at the Mojave Desert Airport in California, USA; the reader will notice features corresponding to tarmac, aircraft on the tarmac bright spots on top of the darker regions , aircraft hangars, and fence lines.

This image is plotted in the ground plane, where the x-axis corresponds to cross-range and the y-axis is downrange. Precisely constructing the matched lter for each scatterer is reliant on perfect knowledge of the scene geometry, platform attitude, and hardware characteristics as well as correct assumptions on the scattering behavior viz.

Errors in this knowledge lead to degraded image quality. Additionally, applying the precise matched lter can prove computationally burdensome. SAR algorithms focus on compensating for certain types of collection errors and approximating the matched lter to mitigate computational loading. Additional SAR goals can involve extracting additional information, such as the target height. The theory of imaging radar and important processing techniques and approaches to enhance image quality are discussed extensively in this text.

Most elded radar systems are monostatic: the transmitter and receiver are colocated, with the scattering phenomenology uniquely dependent on the angle of incidence and reection being equal.

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In some cases, there may be the appearance of signicant separation between. Over-the-horizon radar OTHR is an example of this case. Also, when the transmitter and receiver are located on different parts of an aircraft, this is considered monostatic. In the bistatic radar topology [9], the transmitter and receiver are separated a considerable distance such that scattering phenomenology differs from the monostatic case.

For aerospace bistatic systems, the ground clutter spectral characteristics also appear much more complicated than in the monostatic conguration. Bistatic radars also may be cooperative or noncooperative. A cooperative bistatic radar controls, manages, or selects its source of illumination. In contrast, a noncooperative bistatic radar, sometimes called a passive bistatic radar, employs transmit sources of opportunity, such as cell towers, television and radio transmitters, and other radar systems. While the bistatic radar may not control its source of illumination, modern radar technology still allows these systems to apply coherent signal processing methods.

Radar: Technical Principles (Reel 1) - Part 2 - Mechanics

Multistatic radar involves multiple receivers and possibly transmitters. Multistatic radar provides a diversity of spatial measurements, which can be used to minimize target fading, improve target geolocation [10], and possibly enhance target recognition. Because the multistatic radar can use multiple transmitters and receivers, it is sometimes considered a multi-input, multi-output MIMO conguration.

However, the typical MIMO conguration is usually monostatic in nature and involves transmitting different, ideally uncorrelated, waveforms from each antenna subaperture. The ability to coherently transmit different waveforms from each subaperture leads to spatial diversity on transmit, which effectively leads to a secondary phase modulation on the received target signal that can potentially improve target location performance.

MIMO radar may also have some advantages for sparse arraysdealing with timing and position uncertainty and possibly mitigating spatial ambiguityand enhancing SAR coverage rates. Fully adaptive MIMO provides the opportunity for improved detection by attempting to match the illumination waveform to the target class of interest. MIMO is an area of current, active research within the radar community, and its benets are still being benchmarked.

This text considers monostatic, bistatic, and MIMO radar congurations. Advances in processing technology and techniques are key enablers for bistatic and MIMO radar topologies and are also central to signicant improvements in monostatic radar performance.

Low probability of intercept is another consideration. A number of modulations are available and are intended to provide the most appropriate ambiguity function for the application at hand.

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The ambiguity function characterizes the waveform range impulse response and its sensitivity to Doppler modulation. The waveform. Achieving high resolution within receiver bandwidth and other hardware constraints is yet another driving factor. These waveforms include stretch processing, stepped chirped, and stepped frequency. Stretch processing essentially starts the radar signal processing chain within the analog receive hardware, beating the incoming waveform with a modulation that converts range delay to spatial frequency.

The digital processing stage applies an ADC operating at a lower sample rate, but fully covering the lower bandwidth spectrum corresponding to a particular range swath of interest, and a Fourier transform to pulse compress the data.

In this case, swath width is traded for the higher resolution corresponding to the transmit bandwidth. Stepped chirp is a coherent waveform using a series of chirps of modest bandwidth and pulse width at offset transmit frequencies.