Chapter 17

Keywords

Through-the-wall radar; Urban sensing; Compressive sensing; Multipath exploitation; Clutter mitigation; Moving target indication; Beamforming; Backprojection

2.17.1 Introduction

The field of remote sensing has developed a range of interesting imaging approaches for a variety of applications. Through-the-wall sensing is a relatively new area that addresses the desire to see inside buildings for various purposes, including determining the room layouts, discerning the building intent and nature of activities, locating the occupants, and even identifying and classifying objects within the building. Through-the-wall sensing is highly desired by police, fire and rescue, emergency relief workers, and military operations. Accurate sensing and imaging can allow a police force to obtain an accurate description of a building in a hostage crisis, or allow firefighters to locate people trapped inside a burning structure. In essence, the goals of through-the-wall sensing technology are to provide vision into otherwise obscured areas [1].

Each remote sensing application area has driven different sensing modalities and imaging algorithm development based upon propagation characteristics, sensor positioning, and safety issues. Traditional optical, radar, and sonar image processing all begin with basic wave physics equations to provide focusing to individual points. In many radar applications, for example, data sampled from many sensors are mathematically integrated to provide equivalent focusing using free-space propagation assumptions. Free-space imaging is commonly seen in synthetic aperture radar (SAR) techniques since atmospheric distortions are often negligible and can be safely ignored in first-order calculations [2].

Conventional imaging approaches exploit the wave equation to compute the expected phase at each point in space and time over which the data are collected. The complex phase front is similar to the spatial representation of the wavefront captured by a hologram [3]. The complex returns can then be compared against the predicted returns from points in the imaging target space to focus on each point in that space. The focusing is analogous to image reconstruction in holography, where a spatial pattern is projected back into the originating target image space. In true free-space conditions, this focusing approach represents a mathematically accurate way to perform imaging. More sophisticated approaches extend beyond free-space assumptions to allow for more complicated propagation effects, such as adaptive optics [4], atmospheric correction for radar [2], and matched field processing for sonar [5]. Correction approaches range from simple wavefront calibration to more sophisticated volumetric propagation corrections.

Free-space propagation does not apply to several applications where transmission through scattering media is encountered, including many modern imaging approaches such as geophysical sensing, medical imaging, and more recently through-the-wall imaging and sensing. In these applications, propagating signals diffract through a volume. As examples, geophysical imaging techniques generally measure seismic propagation through the earth to look for discontinuities that are often indicators of oil, gas, water, or mineral deposits [6]. In medical imaging, ultrasound tomographic approaches account for propagation through different tissue classes [7].

Non-freespace scattering applications are more representative of the through-the-wall sensing problem, albeit each has its own distinct challenges and approaches [1]. In geophysical and medical applications, the propagation medium is discontinuous but still respectively fills the sensing volume of the earth and tissue. To better propagate into the volume, sensors are placed in direct contact with the medium (e.g., seismometers for geophysical sensing, ultrasound transducers for medical imaging). In through-the-wall sensing, there are many air-material interfaces that dramatically change the wavefront. Through-the-wall sensors may be placed against the front walls or located some distance away from the structure. In either case, attenuation is largely seen only in the building materials and contents rather than in the large volumes of air that occupy most of the space outside or within a building. The rich through- the-wall scattering environment makes volumetric tomographic imaging approaches most relevant for through-the-wall sensing. Rather than using free-space focusing assumptions, correcting for propagation effects may greatly improve the imaging solution [8–10].

Through-the-wall sensing is best motivated by examining the applications primarily driving its development. Through-the-wall sensing grew from the application of ground-penetrating radar systems to walls, with specific applications documented in the literature since the late 1990s showing abilities to sense beyond a single wall from near range [11–25]. Applications can be divided based upon whether information is sought on motions within a structure or on imaging the structure and its stationary contents.

The need to detect motion is highly desired in situations like firefighters finding a child in a burning building or law enforcement officers locating hostages and their captors. Such applications can resort to Doppler discrimination of movement from background clutter [26–31]. Motion detection and localization can be decomposed into zero (0-D), one (1-D), two (2-D), or three-dimensional (3-D) systems. A 0-D system is simply a motion detector and will report any motion in the scene. Such systems may be useful to detect whether a room is occupied or not, often useful in monitoring applications such as security systems or intruder detection. Interior motion may be ample information for a firefighter to decide whether to enter a room or building. Since range or angle is not required, 0-D systems may use continuous wave (CW) tones as waveforms. While such systems have exquisite sensitivity, it is difficult to confine the sensitivity to desired areas, so care must be taken to prevent undesired detections beyond the intended range or even from the system operator himself due to multipath reflections. 0-D systems are not particularly useful in cases where other moving individuals may be within the sensor field of view.

One-dimensional systems provide a range to a target but not an angle. The extra dimension provides the ability to separate and possibly discriminate multiple targets. The range information can help bound whether a detection is in the adjacent room, or perhaps deeper within the building. The systems can obviously gate out detections from ranges beyond the ranges of interest, and may more easily discriminate desired target motion from the motion of the operator. The systems often consist of a single receiving antenna in addition to a transmitting antenna (although sometimes a single antenna can be used for both functions). Motion of the operator can be gated out in range, or a reference antenna pointed towards the radar operator can be used for cancellation. Ranging is historically one of the principle reasons for the development of radar technology, but the short stand-off ranges of most through-the-wall systems provide some particular challenges. Three methods are often employed to obtain target range, namely, ultrawideband pulses [22,32–36], dual frequencies [28,37–39], or stepped CW [11,20].

Two-dimensional systems provide a slice through the scene in the range and angle dimensions [11,22,23]. The 2-D representation provides better localization of the mover, at the expense of a larger antenna array whose elements are collinear. 2-D systems will be subject to layover of objects, meaning that objects out of plane will appear to be rotated to the imaging plane, which can lead to distortion of the field of view. 3-D systems attempt to represent a volumetric representation of the field of view in the range, azimuth, and elevation dimensions [40]. The third dimension avoids the layover issue of targets being projected into a 2-D plane; at the expense of a 2-D array and higher processing requirements. Potentially, the third dimension can provide additional localization for identification of targets. Height information may allow discrimination between people and animals such as household pets, since radar cross-section alone can be unreliable in the presence of through-the-wall multipath and other issues. The ultimate goal is higher resolution for even better moving target classification. Both 2-D and 3-D processing techniques have generally used either multilateration [8,17] or SAR techniques [41,42] using either ultrawideband pulses or stepped CW radars to localize features in the scene.

Imaging of structural features and contents of buildings requires at least 2-D (and preferably 3-D) systems. It cannot rely on Doppler processing for separation of desired features, so multilateration or SAR approaches have been the most commonly used approaches. The general idea behind multilateration is to correlate range measurements from multiple sensors to specific points in the image. With sufficient spatial diversity from a large set of transmit/receive combinations, specific reflection points will start to integrate above the background interference. However, ambiguities will arise as the number of reflection points increases which can provide an over-determined system relative to the transmit/receive signal pairs which can detract from image quality. SAR-based systems can be thought of as a coherent extension of the multilateration concept. Instead of incoherent combinations of range returns from multiple transmit/receive pairs, coherent algorithms are used to provide a complex matched filter to specific points in the target space. SAR algorithms are well established for stand-off imaging applications [2]. Stand-off applications generally assume free-space propagation to each point in the target scene, although platform motion compensation and atmospheric effects are removed with autofocusing algorithms.

Most of the SAR and multilateration techniques usually neglect propagation distortions such as those encountered by signals passing through walls and objects. Distortions degrade the performance and can lead to ambiguities in target and wall localizations. Free-space assumptions no longer apply after the electromagnetic waves propagate through the first wall. As a result, free-space approximations may carry imaging systems through to the first wall, but propagation effects will then affect further imaging results. Shadowing, attenuation, multipath, reflection, refraction, diffraction, and dispersion, all play a role in how the signals will propagate after the first interface. Without factoring in propagation effects, imaging of contents within buildings will be severely impacted. As such, image formation methods, array processing techniques, target detection, image sharpening, and clutter and multipath identification and rejection paradigms must work in concert and be reexamined in view of the nature and specificities of the underlying sensing problem.

A common occurrence of incorrect localization is objects outside the building being illuminated by the reflection off the first wall and subsequently creating an ambiguous image visible inside the building. Moreover, the strong front wall reflection causes nearby indoor weak targets to go undetected. Multipath propagation introduces ghosts or false targets in the image. Uncompensated refraction through walls can lead to localization or focusing errors, leading to offsets and blurring of imaged targets [41,43–45]. Bragg scattering off repeating structural elements, such as rebar in concrete walls or repetitive voids in concrete block walls, can cause image ambiguities and modulation of subsequent wavefronts. Some of the wall propagation effects can be compensated for using image-focusing techniques incorporating proper wavefront corrections [41,44]. SAR techniques and tomographic algorithms, specifically tailored for through-the-wall imaging, are capable of making some of the adjustments for wave propagation through solid materials. While such approaches are well suited for shadowing, attenuation, and refraction effects, they do not account for multipath, Bragg scattering, as well as strong reflections from the front wall.

In this chapter, we consider the recent algorithmic advances that address some of the aforementioned unique challenges in through-the-wall sensing operations. More specifically, Section 2.17.2 deals with techniques for the mitigation of the wall clutter. Front wall reflections are often stronger than target reflections, and they tend to persist over a long duration of time. Therefore, weak and close by targets behind walls become obscured and invisible in the image. Approaches based on both electromagnetic modeling and signal processing are advocated to significantly mitigate the front wall clutter. Section 2.17.3 presents an approach to exploit the rich indoor multipath environment for improved target detection. It uses a ray tracing model to implement a multipath exploitation algorithm, which maps each multipath ghost to its corresponding true target location. In doing so, the algorithm improves the radar system performance by aiding in ameliorating the false positives in the original SAR image as well as increasing the SNR at the target locations, culminating in enhanced behind the wall target detection and localization. Section 2.17.4 discusses a change detection approach to moving target indication for through-the-wall applications. Change detection is used to mitigate the heavy clutter that is caused by strong reflections from exterior and interior walls. Both coherent and noncoherent change detection techniques are examined and their performance is compared using real data collected in a semi-controlled laboratory environment. Section 2.17.5 deals with fast data acquisition schemes to provide timely actionable intelligence in through-the-wall operations. The demand for high degree of situational awareness to be provided by through-the-wall radar systems requires the use of wideband signals and large antenna arrays. As a result, large amounts of data are generated, which presents challenges in both data acquisition and processing. The emerging field of compressive sensing is employed to provide the means to circumvent possible logistic difficulties in collecting measurements in time and space and provide fast data acquisition and scene reconstruction for moving target indication.

It is noted that the chapter provides a signal processing perspective to through-the-wall radar imaging. Outside the scope of this chapter, there has been significant work performed for TWRI applications in the areas of antenna and system design [36,46–48], wall attenuation and dispersion [49–51], electromagnetic modeling [52–55], inverse scattering approaches [24,56,57], and polarization exploitation [58–60].

The progress reported in this chapter is substantial and noteworthy. However, many challenging scenarios and situations remain unresolved using the current techniques and, as such, further research and development is required. However, with the advent of technology that brings about better hardware and improved system architectures, opportunities for handling more complex building scenarios will definitely increase.

2.17.2 Wall clutter mitigation

Scattering from the exterior front wall is typically stronger than that from targets of interest, such as a human or a rifle, which have relatively small radar cross sections (RCS). This makes imaging of stationary targets behind walls difficult and challenging. The problem is further compounded when targets are in close vicinity of walls of either a high dielectric constant or with layered structures. In particular, hollow cinder block walls contain an air-gap void within the cinder block with disparate dielectric constants. This establishes a periodic structure resonance cavity that traps electromagnetic (EM) modes. The consequence of this layered composite structure on radar target imaging is to introduce long time constant relaxations on target detections in radar range profiles. Therefore, weak and close by targets behind walls become obscured and sometimes totally invisible in the image. Thus, wall reflections should be suppressed, or significantly mitigated, prior to applying image formation methods. One of the simple but effective methods is based on background subtraction. If the received signals can be approximated as the superposition of the wall and the target reflections, then subtracting the raw complex data without target (empty scene) from that with the target would remove the wall contributions and eliminate its potential overwhelming signature in the image. Availability of the empty scene, however, is not possible in many applications, and one must resort to other means to deal with strong and persistent wall reflections.

In the past few years, a number of approaches have been proposed to mitigate the front wall returns and effectively increase the signal-to-clutter ratio. For example, the wall reflections can be gated out, the corresponding parameters can be estimated, and then used to model and subtract the wall contributions from the received data [44]. This wall-dependent technique is effective, but its performance is subject to wall estimation and modeling errors. Another method is proposed in [42], which employs three parallel antenna arrays at different heights. The difference between the received signals at two different arrays is used for imaging. Due to the receiver symmetry with respect to the transmitter, a simple subtraction of the radar returns leads to wall reflection attenuation and image improvement. However, this technique requires two additional arrays and the effect of the subtraction operation on the target reflection is unknown and cannot be controlled. In [54], the walls reflections are eliminated by operating the imaging radar in cross-polarization as the cross-polarization returns from a planar interface such as the wall surface are theoretically zero, unlike the returns from humans. However, the cross-polarization returns from the targets behind walls are often very weak compared to their co-polarization counterparts, and as such, the radar performance may be limited by noise.

An eigen-structure technique is developed in [61] to decompose the received radar signal into three subspaces: clutter (including the front wall), target, and noise. Singular value decomposition (SVD is applied on the data matrix to extract the target signatures. SVD has also been used as a wall clutter reduction method for TWRI in [62,63]. Another approach for wall clutter mitigation is based on spatial filtering. It utilizes the strong similarity between wall EM responses, as viewed by different antennas along the entire physical or synthesized array aperture, is proposed in [64]. The idea is that under monostatic radar operation, the wall returns approximately assume equal values and identical signal characteristics across the array elements, provided that the extent of the wall is much greater than the antenna beamwidth. On the other hand, returns from targets with limited spatial extent, such as humans, vary from sensor to sensor. The dc component corresponding to the constant-type return, which is typical of walls, can be significantly reduced while preserving the target returns by applying an appropriate spatial filter along the array axis. However, care must be exercised in the choice of the spatial filter so that its characteristics cause minimum distortion to the target returns. The spatial-domain preprocessing scheme is analogous, in its objective, to the moving target indication (MTI) clutter filter operation in the time domain.

In this section, we present in details two of the aforementioned methods, namely, the wall parameter estimation method and the spatial filtering technique. For the spatial filtering technique, we consider two different types of filters, namely, moving average subtraction filter and the infinite impulse response notch filter, and compare their effects on the target image.

2.17.3 Multipath exploitation

The existence of targets inside a room or in an enclosed structure introduces multipath in the radar return, which results in false targets or ghosts in the radar images. These ghosts lie on or near the vicinity of the back and side walls, leading to a cluttered image with several false positives. Without a reasonable through-the-wall multipath model, it becomes difficult to associate a ghost to a particular target. Identifying the nature of the targets in the image and tagging the ghosts with their respective target according to a developed multipath model, although important to reduce false alarms, is not the final goal of a high-performance through-the-wall imaging system. Since each multipath provides some information about the target, it becomes prudent to utilize the multipath rather than ignoring it, once identified. The utilization or exploitation of the multipath to one’s advantage represents a paradigm shift when compared to the classical approach of either ignoring or mitigating it.

For through-the-wall radar imaging applications, the existence of multipath has been recently demonstrated for stationary targets in [52,72,73]. In [72], the authors use distributed fusion to remove the false targets caused either from multipath or target interactions for stationary scenes after suitable image registration. In [52], time reversal techniques are used to alleviate ghosts and clutter from the target scene and in [73], a synthetic aperture radar (SAR) based image of a human inside a room is shown along with possible multipath ghosts. However, no more rigorous multipath modeling and analysis are presented in the aforementioned references [52,72,73] and the references therein.

In this section, we derive a model for the multipath in an enclosed room of four walls. The model considers propagation through a front wall and specular reflections at interior walls. A SAR system is considered and stationary or slow moving targets are assumed. Although the multipath model presented deals with walls, reflections from the ceiling and floor can be readily handled. We demonstrate analytically that the multipath as seen by each sensor is displaced, and, therefore, we derive the actual focusing positions of the multipath ghosts in downrange and crossrange. The multipath model permits an implementation of a multipath exploitation algorithm, which associates as well as maps each target ghost back to its corresponding true target location. In so doing, the exploitation algorithm improves the radar system performance by ameliorating the false positives in the original SAR image as well as increasing the signal-to-clutter ratio (SCR) at the target locations, culminating in enhanced behind the wall target detection and localization. It is noted that the exploitation algorithm only maps back target-wall interactions; target-target interactions are left untreated. The multipath exploitation algorithm is inspired by the work in [74], wherein the shadowed regions of a target are revealed via its multipath returns. The difference, however, is that we are not striving to reveal the shadowed regions of the target. In our case, we deal with targets with arbitrary dielectric constants, and wish to associate and map each multipath to its true target location.

2.17.4 Change detection based MTI approach

Detection of humans is one of the most important objectives in urban sensing and through-the-wall radar technology [13,14,27,73,80–82]. Humans belong to the class of animate objects which are characterized by motion of the limbs, breathing, and heartbeat. These features separate animate and inanimate objects and allow the detection of targets of interest to proceed based on changes in the phase of the scattered radar signals over successive probing and data observations.

Change detection techniques have been recently used to detect moving targets in the presence of heavy clutter that is caused by strong reflections from exterior and interior walls. In the case of moving targets, the subtraction of two consecutive or non-consecutive data frames or images enjoys the same benefits as the background subtraction process for the case of stationary targets. That is, it results in effective removal of all wall returns and non-target clutter. The subtraction operation performed for either case of stationary or moving targets is referred to as Change Detection (CD). The length of the time period elapsing between the two datasets to be subtracted may differ for the two cases. While the stationary scene permits long time periods, the moving target case necessitates short periods. When operating on data frames, both operations can be described by what is known as delay line cancelers (DLC) [83].

It is known that moving target indication (MTI) processing applies clutter filters to remove radar returns scattered from stationary objects. Delay line cancelers can be designed such that their frequency responses place a notch at DC and concurrently meet other desirable passband and stopband filter characteristics [83]. Doppler filter banks typically follow the delay line canceler, and provide benefits of signal separations, radial velocity measurements, and noise reduction [83]. It is important to note that for urban sensing environments, changes in the backscattered signal phase due to motion do not necessarily lend themselves to Doppler frequency shifts. The human motion can be abrupt and highly nonstationary, producing a time-dependent phase whose rate of change may fail to translate into a single shift or multi-component sinusoids that can be captured by different Doppler filters. Instead, the corresponding wide spectrum of human motions becomes non-localizable and can span the entire radar frequency band. In lieu of Doppler filters, time-frequency processing can be applied to reveal the instantaneous frequency signatures, as it is the case with Human gait. Human gait classification and biometric radar have been discussed in [30,84–87]. However, apart from regularized motions, such as walking and running, time-frequency Doppler signal representations are very complex and difficult to interpret, especially when dealing with non-homogeneous walls. Therefore, the application of Doppler and Microdoppler filters for indoor target surveillance may not significantly add to target motion detection that is already achieved by delay line cancelers.

Change detection in through-the-wall radar imaging was first discussed concurrently in [88] and [89]. In [88], the Synchronous Impulse Reconstruction (SIRE) radar system, operating in the frequency band of 300 MHz–3 GHz and employing a multi transmit/receive design, was used for MTI. The change detection approach discussed in [88] subtracts the down range profiles over consecutive data frames, emulating a derivative operator. It was shown that the SIRE successfully detects a human target moving within an enclosed structure. On the other hand, change detection was applied in [89] in the context of background subtraction to detect stationary targets using data acquired during interrogations of a scene at two different time instants. Image intensity subtraction was employed and examples based on ray tracing data covering 0.7–3.1 GHz frequency range were provided. More recently [90], applied change detection for slow-moving target detection in through-the-wall radar imaging (TWRI) by subtracting the image intensity corresponding to different data frames, followed by a zero threshold operation to suppress the reference image. Examples of target detection in [90] included both EM modeling data and real experiments. Change detection using first- and second-order motion detection images for TWRI application were discussed in [91,92]. The former has better sensitivity to motion, whereas the latter provides better signal-to-noise ratio (SNR). Experimental data, reported in [91,92] using four antennas and a signal bandwidth of 500 MHz–1 GHz, demonstrated that change detection techniques are capable of detecting walking human and simulated human breathing using machine motion. Moving target indicator filtering was also used in [93] for detection of humans in motion inside buildings. An ultrawideband frequency-modulated continuous-wave radar with an extended frequency sweep from 0.5 to 8 GHz was used in the differential mode to track human motion behind a brick wall.

In this section, we examine change detection formulation and performance when clutter removal is performed either coherently or noncoherently. Rather than operating on successive pulses, delay line canceler equivalences are applied to different data frames for each range bin (or equivalently different complex amplitude images for each pixel) (coherent CD) or different intensity images for each pixel (noncoherent CD). The frames can be consecutive, dealing with targets exhibiting sudden short motions, or nonconsecutive with relatively long time difference, for the case in which the target changes its range gate position.

2.17.5 Compressive sensing approach to moving target indication for urban sensing

The most desirable goal of a through-the-wall radar system is to provide situational awareness in a fast and reliable manner. This goal is primarily challenged due to increasing demands on the radar system to deliver high resolution images in both range and cross-range, which requires use of wideband signals and large array apertures, respectively. In addition, the presence of multipath and clutter can significantly contaminate the radar data and compromise the main intent of providing enhanced system capabilities for imaging of building interiors and tracking of targets behind walls.

Most radar data acquisition systems acquire samples in frequency (or time) and space, and then apply compression to reduce the amount of stored information. This approach has three inherent inefficiencies. First, as the demands for high resolution and more accurate information increase, so does the number of data samples to be recorded, stored, and subsequently processed. Second, there are significant data redundancies not exploited by the traditional sampling process. Third, it is wasteful to acquire and process data samples that will be discarded later. Further, in radar imaging systems, whether using time or stepped-frequency based pulsing, along data collection leads to image degradation. This is because, unless all objects in the scene are stationary during the entire data collection operations, image smearing of targets would occur.

Compressive sensing (CS) is a very effective technique for scene reconstruction from a relatively small number of data samples without compromising the imaging quality [94–98]. In general, the minimum number of data samples or sampling rate that is required for scene image formation is governed by the Nyquist theorem. However, when the scene is sparse, compressed sensing provides very efficient sampling, thereby significantly decreasing the required volume of data collected. Towards the objective of providing persistent surveillance in urban environments, such techniques will yield reduced cost, simplified hardware, and efficient sensing operations that allow super-resolution imaging of sparse behind-the-wall scenes. CS has been successfully applied to TWRI of moving and stationary targets [97,99].

In this section, we exploit full benefits of the compressive sensing to aid in fast data acquisition in wideband through-the-wall radar imaging systems for moving target detection and localization. Change detection is first used for removal of stationary background (clutter and stationary targets). Since the removal of stationary background via change detection converts a populated scene into a sparse scene of moving targets, reduction in data volume is then pursued under the framework of compressive sensing.

2.17.6 Applications

Through-the-wall radar imaging has a variety of applications in both civil and military paradigms. It has been successfully sought out for surveillance and reconnaissance in urban environments, requiring not only the layout of the building, including types and locations of walls, but also detection and localization of both moving and stationary targets within enclosed structures [1,80,105]. This technology can also be used in rescue missions, searching for fire, earthquakes, and avalanche victims and survivors, behind-the-wall detection and surveillance of suspected criminals and outlaws, and by law enforcement officers for locating hostages and their captors [20,106].

2.17.7 Open issues and problems

There are many challenges still facing the TWRI technology. Imaging of stationary scenes using SAR is considered more difficult than MTI for moving targets. The difficulty arises from false positives and ghosts, both are products of multipath. Further, when targets are close to walls, imaging algorithms may not be able to separate targets from wall responses, as they both tend to merge. Another important TWRI goal is target classification. The relatively limited bandwidth does not lead to sufficient resolution of key scatterers, rendering the target image as a cluster of contagious high value pixels, a situation that does not lend itself to effective feature extraction. Classifications of Human gait take advantage of the motion of the limbs and their respective microDoppler signatures. When performed behind walls, these signatures blur and it can be difficult to achieve low classification errors. Complex buildings, made of multiple floors, each with populated scenes present the ultimate challenge of TWRI technology, specifically with long standoff distances whether it is associated with ground-based or airborne systems. The latter, due to covertness and operation logistics, is becoming the cornerstone of recent research and development in this area, especially for defense applications [107].

2.17.8 Data sets

The Center for Advanced Communications at Villanova University has conducted several through-the-wall imaging experiments and collected a variety of datasets in a laboratory environment. The datasets include full-polarization free-space and through-the-wall collections under semi-controlled conditions with a stepped-frequency radar system. Targets consist of both calibrated reflectors (dihedrals, trihedrals, and spheres) as well as a number of common indoor objects (phone, computer, tables, chair, filing cabinet). In addition, a jug of saline solution is also used as a target to crudely approximate a human. These datasets are available for download at the following website: http://www.villanova.edu/engineering/centers/cac/twri.htm .

2.17.9 Conclusion

In this chapter, we presented recent algorithmic advances in through-the-wall radar imaging. First, considering ground-based EM sensing, we discussed two methods, namely, spatial filtering and EM modeling based solution, for mitigating the front wall return prior to application of the image formation methods. The spatial filtering technique builds on the strong correlation of wall EM responses across antenna array elements to reduce constant-type return that is typical of walls in monostatic illuminations. The spatial filter, thus, allows the follow-on beamforming to unmask and image behind-the-wall targets that have limited spatial extent, such as humans, compared to walls without the need for a priori knowledge of the wall characteristics. A simple IIR notch filter with flexible design was compared with the fixed design MA subtraction filtering, commonly used in GPR. On the other hand, the EM modeling approach is wall-dependent and relies on accurate estimation of the wall parameters for suppression of the wall return. Extraction of the wall parameters and coherent subtraction of the modeled wall reflection was shown to significantly improve the signal-to- wall-clutter ratio.

Second, an approach to exploit the rich indoor multipath environment for improved target detection was described. A ray tracing approach was used to derive a multipath model, considering reflections inside an enclosed room comprising four homogeneous walls. Using the model, it was demonstrated analytically that the multipath corresponding to each sensor appeared on the wall but changes position from one sensor to another. Hence, a least squares technique was used to estimate its actual focusing location in both downrange and crossrange. The model was utilized to develop a multipath exploitation technique which associates multipath ghosts with their respective targets and maps them to their true target locations. This technique reduced the false positives in the original beamformed image as well as increased the signal-to-noise ratio at the true target locations.

Third, we discussed a change detection approach to moving target indication for through-the-wall applications. Change detection was used to mitigate the heavy clutter that is caused by strong reflections from exterior and interior walls. Both coherent and noncoherent change detection techniques were examined and their performance was compared under both consecutive and non-consecutive acquisitions. For non-consecutive acquisitions, the coherent CD scheme showed two sets of imaged targets corresponding to the positions of a single target at the two data acquisitions, whereas the noncoherent CD retained only one set of imaged targets, though with significantly more artifacts. For consecutive acquisitions, the coherent change detection provided better performance than the noncoherent CD, which removed most of the imaged target during the zero-thresholding step.

Finally, we identified and localized moving targets behind walls and inside enclosed structures using an approach that combines sparsity-driven radar imaging and moving target indication. The removal of stationary background via change detection resulted in a sparse scene of moving targets, thereby inviting application of compressed sensing techniques for fast data acquisition. Using stepped-frequency based radar imaging, it was demonstrated that a sizable reduction in the number of frequency samples was provided by compressive sensing without degradation in system performance.

Relevant Theory: Radar Signal Processing and Statistical Signal Processing

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