Point Target Research Articles

Rapid beamforming of ultrasound chirp signals in frequency domain using the chirp scaling algorithm

Linear frequency-modulated (chirp) transmits have been used successfully in the past to increase penetration depth of ultrasound signals in tissue and to improve the signal-to-noise ratio (SNR) in the resulting ultrasound images. However, beamforming chirp signals using delay-and-sum (DAS) can be slow on systems without a GPU. We propose using the chirp scaling algorithm (CSA), originally developed for synthetic aperture radar, as a faster alternative to DAS on CPU that results in similar image quality, especially at larger depths. To perform preliminary comparisons of the beamforming methods, we simulated in FIELD II monostatic synthetic aperture data containing point targets up to 300 mm in depth. The simulation accounted for average signal attenuation in soft tissue of 0.5 dB per MHz per cm. We analyzed the point spread functions and the runtimes of the methods in MATLAB with a single CPU. Beamformed point targets above 80 mm depth had almost identical sidelobe levels and full-width-at-half-maximum between the two methods, while the median runtime for CSA was 9.3 times faster than for DAS. We further extend CSA to multistatic acquisitions with chirp transmits and with curvilinear arrays for more clinical applicability and improved image quality.

Rapid refocusing of ultrasound images in frequency domain using range-Doppler algorithm

The ability to maintain image uniformity and high resolution over large depths is important for several clinical applications of ultrasound, including deep abdominal imaging in patients with high BMI. One way to improve image quality in such cases is to use retrospective transmit focusing, which involves combining received data from different focused transmits to improve image resolution and SNR outside of transmit focal zone. Retrospective transmit is typically accomplished using the delay-and-sum (DAS) beamforming, which can be slow on systems without a GPU. As a faster alternative, we treat beamsummed signals from focused transmits as monostatic synthetic aperture data from virtual sources, and apply a frequency-domain beamformer such as RDA to rapidly refocus an RF image. We demonstrate the concept using FIELD II simulated ultrasound signals from a point target phantom over 200 mm depth. The RDA-refocused image shows a uniform point spread function, and the reduction in full-width at half maximum by a factor of 2.5 compared to the original image from focused transmits at f-number 10. RDA-based refocusing also achieves a speedup by a factor of 14 relative to the DAS-based retrospective beamformer.

InSAR Displacement with High-Resolution Optical Remote Sensing for the Early Detection and Deformation Analysis of Active Landslides in the Upper Yellow River

Frequent landslides and other geological disasters pose a serious threat to human life and infrastructure in the Upper Yellow River. Detecting active landslides and ascertaining their impact necessitate the determination of deformation characteristics. In this study, we developed an integrated method combining interferometric synthetic aperture radar and high-resolution optical satellite remote sensing to detect active landslides in the Upper Yellow River region from Longyang Gorge to Lijia Gorge. Sentinel-1 satellite data from January 2019 to April 2021 with ascending and descending orbits were adopted to obtain deformation using the STACKING and interferometric point target analysis techniques. A 97.08% overlap rate in the detected results from the two InSAR technologies confirmed the suitability of both approaches. The missing detection rates (6.79% & 8.73%) from single line-of-sight (LOS) InSAR results indicate the necessity of different orbit direction data. Slight deformation rate changes (<4 mm/month) before and after rainy seasons of the Lijia Gorge landslide group indicate that precipitation exerted little impact on slope activity. This study supports the feasibility of integrated methods for the detection and analysis of active landslides in the Upper Yellow River and other regions.

Accuracy and efficiency using frameless transient fiducial registration in stereoelectroencephalography and deep brain stimulation.

Stereotactic surgical methods continue to advance technologically. Frameless transient fiducial registration (FTFR) systems have been developed and avoid the need to move or position a patient in a frame after already receiving registration imaging. One such system, Neurolocate, has recently become available as a robotic attachment for the Neuromate stereotactic robot. This study is the largest in the literature to evaluate the accuracy of frameless registration using Neurolocate versus frame-based registration (FBR) methods in both deep brain stimulation (DBS) and stereoelectroencephalography (SEEG). Additionally, the authors sought to reevaluate factors affecting accuracy in both procedures. This study was a retrospective chart and imaging review of 88 consecutive procedures (involving 621 electrodes) implanting either DBS or SEEG at the authors' institution over a 5-year period from March 2015 to March 2020. Registration duration, radial target entry point, and Euclidean target implantation accuracies, as well as factors affecting accuracy, were recorded for each patient. SEEG procedures included 38 patients and 525 implanted electrodes (294 using FBR and 231 using FTFR). DBS procedures included 50 patients and 96 implanted electrodes (65 using FBR and 31 using FTFR). Overall, FTFR registration was significantly more accurate (median 0.1 mm, IQR 0-0.4 mm) compared with FBR (median 1.3 mm, IQR 0.9-1.5 mm; p = 0.04). Likewise, FTFR had a significantly shorter duration of registration (median 84 minutes, IQR 77.3-95.3 minutes) when compared with FBR (median 110.5 minutes, IQR 107.3-138 minutes; p = 0.02). No significant differences were found when examining the radial entry point and Euclidean target implantation errors of each method. FTFR with the Neurolocate system represents a technique that may decrease operative time while maintaining the high accuracy previously demonstrated by other stereotactic methods, despite an initial surgeon learning curve. It should be investigated in future studies to continue to improve stereotactic accuracies in neurosurgery.

Single-Arm Trial of a Flexible Multicomponent Commercial Digital Weight Management Program

Background Hunger and food cravings predict poor outcomes in lifestyle interventions for weight management. For this reason, flexible weight management programs, as opposed to restrictive weight management programs, are needed. WW (formerly Weight Watchers)—a widely available, commercial weight management and wellness program—includes an approach that allows participants to obtain a personalized zero-point food (ZPF) list, which includes foods that do not need to be weighed, measured, or tracked. With over 300 potential options, ZPFs can include fruits, vegetables, legumes, whole grains, nonfat dairy, and lean sources of protein. Participants are assigned an individualized daily and weekly point target and can use ZPFs to help budget their points throughout the day, which can nudge participants toward a healthier overall dietary pattern. Objective In a 6-month, single-arm trial, we examined the efficacy of WW when delivered via multimodal digital tools, including a mobile app for assisting with point tracking, weekly virtual workshops, weekly 5-minute wellness check-ins, and a Facebook group in which participants could socialize and support each other. Methods The outcomes included weight change from baseline, as measured by the Bluetooth scales provided to each participant; hunger (visual analogue scale); food cravings (Food Craving Inventory); the intake of fruits and vegetables (The Five Factor Screener); physical activity (Global Physical Activity Questionnaire); and overall well-being (WHO-5 Well-Being Index). Results Of the 153 participants, 70% were female, and 66% were White. Participants’ mean age was 41.09 (SD 13.78) years, and they had a mean BMI of 31.8 (SD 5.0) kg/m2. Retention was high, as 91.5% provided 6-month follow-up data. Participants lost an average of 5.1% of body weight from baseline to 6 months (mean −4.4, SD 4.87 kg; P<.01), with 51% losing clinically significant weight (≥5%). Hunger significantly declined over 6 months (mean percent change −14.74%, SD 64.28%; P<.01), as did food cravings (mean percent change −16.99%, SD 19.98%). The intake of fruits (mean percent change 65.95%, SD 188.78%; P<.01), vegetables (mean percent change 68.29%, SD 172.61%; P<.05), and salad (mean percent change 127.43%, SD 250.82%; P<.001) significantly increased. Engagement in moderate physical activity increased by an average of 32 (SD 133) minutes per day (P<.01), and sedentary time decreased by 90 (SD 24.5) minutes per day (P<.001). Finally, well-being significantly increased (mean change 17.77%, SD 46.21%; P<.01). Conclusions This program, which used a less restrictive method of food tracking and provided personalized ZPFs, resulted in significant weight loss and an increase in fruits, vegetables, and exercise, while also reducing hunger and food cravings. Future research should compare the effectiveness of these approaches to traditional programs that require the self-monitoring of all foods and beverages. Trial Registration ClinicalTrials.gov NCT04302389; https://clinicaltrials.gov/ct2/show/NCT04302389 Conflicts of Interest None declared.

Infrared Small and Moving Target Detection on Account of the Minimization of Non-Convex Spatial-Temporal Tensor Low-Rank Approximation under the Complex Background

Infrared point-target detection is one of the key technologies in infrared guidance systems. Due to the long observation distance, the point target is often submerged in the background clutter and large noise in the process of atmospheric transmission and scattering, and the signal-to-noise ratio is low. On the other hand, the target in the image appears in the form of fuzzy points, so that the target has no obvious features and texture information. Therefore, scholars have proposed many object detection methods for dimming infrared images, which has become a hot research topic on account of the flow-rank model based on the image patch. However, the result has a high false alarm rate because the most low-rank models based on the image patch do not consider the spatial-temporal characteristics of the infrared sequences. Therefore, we introduce 3D total variation (3D-TV) to regularize the foreground on account of the non-convex rank approximation minimization method, so as to consider the spatial-temporal continuity of the target and effectively suppress the interference caused by dynamic background and target movement on the foreground extraction. Finally, this paper proposes the minimization of the non-convex spatial-temporal tensor low-rank approximation algorithm (MNSTLA) by studying the related algorithms of the point infrared target detection, and the experimental results show strong robustness and a low false alarm rate for the proposed method compared with other advanced algorithms, such as NARM, RIPT, and WSNMSTIPT.

Groundwater over-exploitation driven ground subsidence in the himalayan piedmont zone: Implication for aquifer health due to urbanization

Unsustainable groundwater exploitation often triggers inelastic aquifer compaction, endangering future groundwater potential. The Himalayan piedmont fan regions are undergoing fast urbanization and experiencing rapid groundwater depletion and water table drop. The piedmont fans have laterally varying aquifer geometry with slope-dominated aquifer water flow and experience variable spatiotemporal changes in response to groundwater depletion. We estimated the ground deformation over piedmont fans around Chandigarh (India), a growing urban centre, during 2014–2022 using satellite-based Interferometric Point Target Analysis (IPTA). We used both ascending and descending Differential Interferometric Synthetic Aperture Radar (DInSAR) acquisition modes to derive the vertical ground deformation by combining their line of sight (LOS) velocities. The region is experiencing vertical subsidence up to ∼60 mm/yr with prominent hotspots, where the temporal subsidence is compared with the groundwater cone of depression and urbanization over decades. Comparing the subsidence with the aquifer parameters from the bore wells suggests that the clay confining aquifer level III and semiconfined level II in the distal fan are experiencing the highest subsidence. The stress-strain relationship of these hotspot regions reveals the inelastic compaction of the aquifers producing severe subsidence. The piedmont zones of the Himalayas with identical aquifer geometry and population growth are facing similar challenges, and the combined DInSAR-IPTA and observational groundwater data modeling could provide a robust assessment for effective groundwater-aquifer health monitoring and management.

A Parameter-Adjusting Auto-Registration Overlapped Subaperture Algorithm for Video Synthetic Aperture Radar Imaging

Abstract—Auto-registration video synthetic aperture radar (ViSAR), which requires real time pixel index unifying and resolution matching, is of great significance due to its applicability in multi-aspect observation and continuous monitoring. The phase error induced by the wavefront planar assumption, however, varies with different ViSAR frames, which limits the size of auto-registration imaging scene. To enlarge the auto-registration imaging swath, a parameter-adjusting auto-registration overlapped subaperture algorithm (PAAR-OSA) is proposed in this paper. By collaboratively designing the subapertures within each frame and among different frames in the stabilized-scene coordinate and cooperatively compensating the phase error of all frames, auto-registration with larger imaging swath can be achieved. Both the point targets and distributed targets validation results verify the superiority of the proposed method compared with existing algorithms.

Analysis of Interpolation Methods in the Validation of Backscattering Coefficient Products

Validation is the basis of synthetic aperture radar (SAR) image quantification applications. Based on the point target of the field site, the radiation characteristics of the backscattering coefficient image can be used to optimize the SAR imaging, and the product production system can be more closely targeted, to ensure the image product accuracy in the actual quantification application. In this study, the validation of the backscattering coefficient image was examined using calibrators, and the radiometric properties of the image were evaluated by extracting the radar cross-section of each point target. Bilinear interpolation and fast Fourier transform (FFT) interpolation methods were introduced for the local area interpolation of point targets, and the two methods were compared from the perspective of response function imaging and validation accuracy. The results show that the FFT interpolation method is more favorable for validating the backscattering coefficient.

An Extended Frequency Scaling Algorithm for Bistatic SAR With High Squint Angle

Bistatic synthetic aperture radar (BiSAR) is an extended and complementary observation method to conventional monostatic SAR remote sensing. When high range and azimuth resolution are required in BiSAR, the transmitting platform, i.e., the LEO satellite, should work in the spotlight or sliding spotlight mode, which means a wide bandwidth Chirp signal is transmitted, and the targets are illuminated for a long enough time as well. In order to reduce the sampling frequency in the receiving system and improve the efficiency of the focusing procedure for this BiSAR configuration, the echoes should be dechirp received. A bistatic extended frequency scaling algorithm (Bi-EFSA) is proposed in this paper to deal with the dechirp received bistatic SAR echoes. The first step of Bi-EFSA is the range walk correction in the 2-D time domain, which reduces the space variance of the range curvatures and removals the serious coupling between range and azimuth caused by the high squint angle. After that, the range compression is achieved by secondary range compression (SRC) and bulk shift. Then, the space variance of the azimuth frequency modulation rate, introduced by the range walk correction step, is corrected by implementing the azimuth nonlinear chirp scaling (ANCS) operation. Following this, the focused results in the range frequency and azimuth time domain are obtained by the azimuth compression. The point targets and extended-scene simulations validate the effectiveness of the proposed algorithm.

An Image-Domain Signal Model for Azimuth Multichannel Reconstruction and Its Applications

High-resolution wide-swath (HRWS) synthetic aperture radar (SAR) may benefit from reconstructing signal in image domain, for taking local characteristics of the scene into account. An image-domain signal model for HRWS SAR to reconstruct non-ambiguous image is presented in this paper. This model makes the idea of controlling the regional reconstruction performance and taking the advantage of nonuniform scatter distribution possible. Resolution is also preserved perfectly by the proposed model despite of whatever tradeoff is made between azimuth-ambiguity-to-signal-ratio (AASR) and signal-to-noise-ratio (SNR). The model is derived on back projection (BP) image and can deal with different squint angles and beam steering strategies thanks to BP algorithm. Although the reconstruction is done pixel by pixel, computational burden, which depends on beam steering and the specific constraints in reconstruction, may not severely increase. Two methods for Spotlight SAR and two methods for Stripmap SAR are also presented based on the proposed model to verify the model and demonstrate the potential of it. Simulations on both point target and extended target with different squint angles are carried out to verify the proposed methods, which also verify the proposed model indirectly.

Resolving Doppler Ambiguity for Fast-moving Targets with FDA-MIMO Radar

Although the problem of range ambiguity is solved with the frequency diverse array (FDA)-multiple-input multiple-output (MIMO) radar by utilizing the degree-of-freedom (DOFs) in the range domain, the phenomenon of Doppler ambiguity occurs for fast-moving target. In this article, an offset frequency alignment (OFA) method is proposed to solve the problem of Doppler ambiguity in FDA-MIMO radar. At the design stage, the Doppler frequency offsets, corresponding to different transmit pulses, are aligned by eliminating the residual phases. In this regard, the point targets can be focused in the transmit spatial frequency domain. Furthermore, the Doppler ambiguity index is estimated after principal velocity compensation resorting to maximum likelihood (ML) criterion, where both the cases of known and unknown range ambiguity indexes are investigated respectively. Moreover, in order to ensure the estimation performance, the frequency offset across the transmit array elements is designed by enlarging the term related to the Doppler frequency offset. At the analysis stage, the performance of Doppler ambiguity index estimation in terms of root mean square error (RMSE) is assessed in comparison with the Cramer-Rao bound (CRB). Numerical results are provided to verify the effectiveness of the proposed method to resolve the Doppler ambiguity.

Focusing of Wide-Swath Range Sweep SAR With Extended Wide Nonlinear Chirp Scaling Algorithm

Multi-channel, DBF-based high-resolution wide swath SAR (HRWS-SAR) is dedicated to acquiring large observation width and high resolution in a single pass. However, the existing HRWS-SAR imaging modes are difficult to cover effectively for the coastline area not parallel to the satellite orbit. Combined with the beam direction changing, variable PRI and distance-oriented DBF techniques, the wide-swath range sweep SAR (WRS-SAR) can image the wide coastline area not parallel to the satellite orbit at high resolution in a single-pass. In this study, an extended wide nonlinear chirp scaling (E-WLNCS) algorithm is proposed, which mainly consists of: a time-domain blind range sample reconstruction algorithm combined with phase variation estimation, a non-uniform resampling algorithm combined with Doppler variation correction, and the effectiveness of the proposed modified E-WLNCS algorithm is demonstrated through point target and surface target simulations.

Two-Stage Time-Varying Vibration Compensation for Coherent LiDAR Based on the Adaptive Differential Evolution Method

Coherent LiDAR, which is based on triangular frequency modulated continuous wave (T-FMCW), is a promising technology for high-resolution imaging, because it can achieve long-range and high-precision measurements using low power. However, owing to the short wavelength of laser, the system is extremely sensitive to time-varying vibration. Most vibration compensation methods perform well in the scenes that contain strong scatterers using enough data. However, they lose validity in scenes without strong scatterers and sufficient observation time. To address this problem, we develop a second-order vibration error model for coherent LiDAR that divides the Doppler frequency shift caused by time-varying vibrations into quadratic and primary errors. Based on the vibration error model, we propose a two-stage time-varying vibration compensation method using the adaptive differential evolution (ADE) algorithm. In the first stage, we iteratively determine the optimal quadratic vibration coefficients that minimize the spectral entropy of the compensated dechirp signals using the ADE algorithm. Subsequently, we compensate for the quadratic errors using the estimated quadratic vibration coefficients. In the second stage, we use the frequency spectrum cross-correlation method to estimate the primary vibration coefficients and compensate for the primary errors. We perform experiments in the scene containing a point target and the scenes containing multi-component targets and distributed targets without strong scatterers. The results indicate that the proposed method can effectively compensate for time-varying vibration errors using only one-period T-FMCW data in different scenes. Further, the results of the proposed method are more accurate and stable than the Doppler frequency shift method.

Target Therapy vs the Immune Check Point Inhibitors in Lung Cancer: Costs and Caps Platform

The immune check point inhibitors (ICI) and target therapy (TT) Osimertinib (Osi) prolonged survival in advanced/metastatic non-small cell lung cancer (a/m-NSCLC). Costs of ICI were previously investigated (ESMED, July 2022) while TT being overlooked. In 2022, insulin monthly cost was capped at $35 for Medicare patients. We aimed to 1- Attach a $ amount to results of the major relevant TT and ICI clinical studies and weigh their relative costs 2- Reason that utilization threshold caps are necessary to contain cost of extended therapy Methods: In this prospective observational study, annual costs of the approved and widely used TT were calculated as the monthly optimal dose x 12. Costs of the 5-approved ICI in 1-st-line a/m NSCLC were calculated as mg/m2 or per 80 kg x price x number of cycles. Results: Median annual 5-TT cost was $228,000 vs 5-ICI of $134,786 at 1.69 ratio. At 10%, estimated coverage of pharmacy and nursing costs, ratio dropped to 1.52. The 1-3-year Osi costs were $248,372-$745,116, Crizotinib $226,308 -$678,924 and Larotrectinib $399,372-$1,198,116. Pembrolizumab were $134,796-$404,388, Atezolizumab $124,761-$374,283 and Cemiplimab $125,108-$375,324. Applying $500,000 caps, the ICI 3-year costs were all below threshold. TT medium 3-year cost was $684,000, exceeding cap by $184,000, Osi by $245,116 and Crizotinib by $178,924. Larotrectinib 2-3-year costs were higher by $298,744 - $698,116. We reasoned that if 1,000 US patients treated with TT at the annual median, cost mounts to $684,000,000. In Europe, 2,000 patients’ cost would be $1,368,000,000. Conclusions: The median TT/ICI was more costly at 1.52 ratio. Drug costs were determined by the number of re-purchases, the 1st-buy, if followed, was considered a down payment. Cap implementations are necessary to contain costs of extended therapy.

Intelligent Reflecting Surface Enabled Sensing: Cramér-Rao Bound Optimization

This paper investigates intelligent reflecting surface (IRS) enabled non-line-of-sight (NLoS) wireless sensing, in which an IRS is dedicatedly deployed to assist an access point (AP) to sense a target at its NLoS region. It is assumed that the AP is equipped with multiple antennas and the IRS is equipped with a uniform linear array. We consider two types of target models, namely the point and extended targets, for which the AP aims to estimate the target's direction-of-arrival (DoA) and the target response matrix with respect to the IRS, respectively, based on the echo signals from the AP-IRS-target-IRS-AP link. Under this setup, we jointly design the transmit beamforming at the AP and the reflective beamforming at the IRS to minimize the Cramér-Rao bound (CRB) on the estimation error. Towards this end, we first obtain the CRB expressions for the two target models in closed form. It is shown that in the point target scenario, the CRB for estimating the DoA depends on both the transmit and reflective beamformers; while in the extended target scenario, the CRB for estimating the target response matrix only depends on the transmit beamformers. Next, for the point target scenario, we optimize the joint beamforming design to minimize the CRB, via alternating optimization, semi-definite relaxation, and successive convex approximation. For the extended target scenario, we obtain the optimal transmit beamforming solution to minimize the CRB in closed form. Finally, numerical results show that for both cases, the proposed designs based on CRB minimization achieve improved sensing performance in terms of mean squared error, as compared to other traditional schemes.

Gaussian Process Upper Confidence Bounds in Distributed Point Target Tracking Over Wireless Sensor Networks

Uncertainty quantification plays a key role in the development of autonomous systems, decision-making, and tracking over wireless sensor networks (WSNs). However, there is a need of providing uncertainty confidence bounds, especially for distributed machine learning-based tracking, dealing with different volumes of data collected by sensors. This paper aims to fill in this gap and proposes a distributed Gaussian process (DGP) approach for point target tracking and derives upper confidence bounds (UCBs) of the state estimates. A unique contribution of this paper includes the derived theoretical guarantees on the proposed approach and its maximum accuracy for tracking with and without clutter measurements. Particularly, the developed approaches with uncertainty bounds are generic and can provide trustworthy solutions with an increased level of reliability. A novel hybrid Bayesian filtering method is proposed to enhance the DGP approach by adopting a Poisson measurement likelihood model. The proposed approaches are validated over a WSN case study, where sensors have limited sensing ranges. Numerical results demonstrate the tracking accuracy and robustness of the proposed approaches. The derived UCBs constitute a tool for trustworthiness evaluation of DGP approaches. The simulation results reveal that the proposed UCBs successfully encompass the true target states with 88% and 42% higher probability in X and Y coordinates, respectively, when compared to the confidence interval-based method.

Target-independent dynamic wavefront sensing method based on distorted grating and deep learning

A real-time wavefront sensing method for arbitrary targets is proposed, which provides an effective way for diversified wavefront sensing application scenarios. By using a distorted grating, the positive and negative defocus images are simultaneously acquired on a single detector. A fine feature, which is independent of the target itself but corresponding to the wavefront aberration, is defined. A lightweight and efficient network combined with an attention mechanism (AM-EffNet) is proposed to establish an accurate mapping between the features and the incident wavefronts. Comparison results show that the proposed method has superior performance compared to other methods and can achieve high-accuracy wavefront sensing in varied target scenes only by using the point target dataset to train the network well.

A Radar Waveform Design for Deceiving Noncooperative Bistatic Radars

This letter presents a radar waveform design for deceiving noncooperative bistatic radars (NCBRs). The principle of deceiving the noncooperative bistatic radar is proposed according to some imperfections in the engineering realization of NCBRs whose direct wave parameters are selected from the template library to reconstruct the reference signal. These imperfections are either unavoidable, such as phase asynchrony, or hard to find, such as time errors. For the first time, a radar waveform design method based on joint modulation of the initial phase and the pulse repetition interval (PRI) is proposed to achieve range migration–velocity deception for NCBRs. When our host radar illuminates our key target, the noncooperative bistatic radar will detect our key target. The proposed waveform will mislead the target detection results of the noncooperative bistatic radar in range–time (R–T) maps and range-Doppler (R-D) maps. It can not only be used to disguise a moving target as a stationary point target to avoid moving target detection (MTD) of NCBRs, but also modulate the target echo velocity into the opposite direction of the real motion to prevent our key moving targets from being tracked. Both the simulation data and the measured results show that the deception method proposed in this letter fulfills the requirements of NCBR countermeasures

Modeling the Effects of Oscillator Phase Noise and Synchronization on Multistatic SAR Tomography

Recent results have highlighted the potential ability of bi- and multistatic synthetic aperture radar (SAR) tomographers to measure vegetation structure and surface topography. However, the quality of SAR tomographic measurements with multiple platforms is impacted by the phase instability in each platform’s oscillator. The phase noise, if uncompensated, may lead to degradation in the SAR data products such as increased sidelobe levels, reduced peak amplitude of the impulse response, and a low frequency phase modulation, among others. In this work, we model and examine the effects of oscillator phase noise on tomographic SAR signals for spaceborne missions flying in formation. A synchronization process is also adopted to help mitigate oscillator phase errors by measuring and predicting relative phase offsets at prescribed temporal intervals. A simulation tool was developed to examine the point target response as seen by realistic satellite constellations in low Earth orbit using different quality oscillators, radar configurations, and synchronization configurations. A first analysis of a multi-platform tomographic SAR mission suggests that a system without a dedicated physical link with minimal effects on the point target response may be achievable using current oscillators. Our analysis also shows that phase noise has differing effects on multistatic radar modes. Tomograms formed with a system operating in SIMO mode are the most affected by oscillator phase noise error, followed by MIMO, with negligible effects on the SAR-SISO mode. These trade studies and the simulation tool can be used to help inform the design of future multistatic radar missions.