Target Motion Research Articles

Research on Target Position and Motion Parameters Based on Azimuth Wave Number of Echo Signal

Abstract The circular synthetic aperture radar (CSAR) system by a small rotor unmanned aerial vehicle, being capable of three-dimensional target imaging, is one of the research hotspots in SAR imaging. In this paper, for the frequency modulated continuous wave CSAR system, the geometric structure and wavenumber spectrum are derived, and then the relationship between the azimuth wavenumber and system parameters as well as flight motion conditions is analyzed. Through simulation analysis, it is verified that the azimuth wavenumber of the echo signal is affected by the position and motion parameters of the imaging target, laying a theoretical foundation for the development and application of the wavenumber domain imaging algorithm of CSAR.

A Target Collision Algorithm of Autonomous Mobile Robot for Two-player Competition

Abstract A variety of robot competitions are held to train and attract students everywhere. This paper studies the algorithm of autonomous robot with two-player competition. The autonomous robots in a two-player competition takes the scoring form of collision and wins first. The impact object is the enemy target. The key algorithms in a two-player competition are target detection, target path decision, and collision target motion control. First, the vehicle target data set was established and enhanced, the lightweight detection algorithm model was screened and trained, the model pruning optimization experiment and the model deployment optimization experiment were carried out, and then the TensorRT framework was used for inter-layer fusion and data quantification processing. After hitting the target path decision algorithm after detecting the target target, it finally integrates the decision in simple and complex situations. The collision target motion control is the scheme of more intelligent and self-adjustable parameters of fuzzy Radial Basis Function (RBF) network Proportional Integral Derivative (PID) controller. Finally, the collision target implementation algorithm is tested in a real environment, and the results show that the task requirements can be met.

Optimising PTV margins in oesophageal cancer radiotherapy: Amodern radiotherapy planning, treatment delivery and verification approach - A single institutional analysis.

Radiotherapy plays a key role in the multidisciplinary management of oesophageal cancers across neoadjuvant, definitive and palliative settings. Improved precision in radiotherapy planning and delivery techniques have allowed treating disease with tighter margins reducing toxicity. In this study, we examine the appropriateness of current practice in defining the planning target volume (PTV). This is a single institutional retrospective study of patients who received radiotherapy for oesophageal cancers using volumetric modulated arc therapy (VMAT) during 2020. All cone-beam computerised tomography (CBCT) scans were reviewed to assess whether PTV expansions appropriately accounted for tumour motion and interfractional variation. Of the 27 patients, 2 (7%), 5 (19%) and 20 (74%) had cervical, thoracic and distal/gastro-oesophageal junction (GOJ) disease, respectively. 16 (59%) had adenocarcinoma and 9 (33%) had squamous cell carcinoma. 9 of 20 distal/GOJ patients were planned and treated according to the institutional stomach filling protocol. 521/528 (98.7%) CBCTs demonstrated adequate target coverage. Cervical, thoracic and GOJ regions demonstrated adequate target coverage in 57/58, 96/97 and 368/373 CBCTs with median PTV expansions of 5, 7 and 7 mm, respectively. In four patients with GOJ disease, CBCT review identified five episodes where the target volume was insufficiently covered during the treatment course. In this single institutional retrospective study, for the vast majority of patients, our institutional practice of defining PTV margins achieved satisfactory target treatment. The interfractional variations observed in patients with GOJ tumours due to target motion and variable gastric volume, highlights the role of further refinements to motion management techniques in this cohort.

A Multi Moving Target Localization in Agricultural Farmlands by Employing Optimized Cooperative Unmanned Aerial Vehicle Swarm

The paper proposes an original method for employing optimised cooperative swarms of Unmanned Aerial Vehicles (UAVs) to localise multiple moving objects in agricultural farmlands. Crop Monitoring (CM), targeted fertilizer distribution, and Livestock Management (LM) are some of the Smart Farming (SF) applications of UAVs. However, the ever-changing nature of agricultural settings makes it challenging to set up UAV swarms. Detecting multiple evolving objectives in dynamic environments is complicated, and conventional methods are regularly optimized for single objectives, such as area or reduced Energy Consumption (EC), which is unsuitable. This research recommends a Multi-Objective Evolutionary Algorithm (MOEA) as a model for UAV swarms to balance task service, communication, and EC during the investigation. The approach paves the method for innovation in the agricultural sector by optimizing tasks in real-time, addressing unpredictable targets, boosting productivity, and reducing costs. The study’s findings present optimism for smart farm management and accurate SF by improving UAV systems’ response time and scalability.

A Multiple Targets ISAR Imaging Method with Removal of Micro-Motion Connection Based on Joint Constraints

Combining multiple data sources, Digital Earth is an integrated observation platform based on air–space–ground–sea monitoring systems. Among these data sources, the Inverse Synthetic Aperture Radar (ISAR) is a crucial observation method. ISAR is typically utilized to monitor both military and civilian ships due to its all-day and all-weather superiority. However, in complex scenarios, multiple targets may exist within the same radar antenna beam, resulting in severe defocusing due to different motion conditions. Therefore, this paper proposes a multiple-target ISAR imaging method with the removal of micro-motion connections based on the integration of joint constraints. The fully motion-compensated targets exhibit low rank and local similarity in the high-resolution range profile (HRRP) domain, while the micro-motion components possess sparsity. Additionally, targets display sparsity in the image domain. Inspired by this, we formulate a novel optimization by promoting the low-rank, the Laplacian, and the sparsity constraints of targets and the sparsity constraints of the micro-motion components. This optimization problem is solved by the linearized alternative direction method with adaptive penalty (LADMAP). Furthermore, the different motions of various targets degrade their inherent characteristics. Therefore, we integrate motion compensation transformation into the optimization, accordingly achieving the separation of rigid bodies and the micro-motion components of different targets. Experiments based on simulated data demonstrate the effectiveness of the proposed method.

Optimizing the Effectiveness of Moving Target Defense in a Probabilistic Attack Graph: A Deep Reinforcement Learning Approach

Moving target defense (MTD) technology baffles potential attacks by dynamically changing the software in use and/or its configuration while maintaining the application’s running states. But it incurs a deployment cost and various performance overheads, degrading performance. An attack graph is capable of evaluating the balance between the effectiveness and cost of an MTD deployment. In this study, we consider a network scenario in which each node in the attack graph can deploy MTD technology. We aim to achieve MTD deployment effectiveness optimization (MTD-DO) in terms of minimizing the network security loss under a limited budget. The existing related works either considered only a single node for deploying an MTD or they ignored the deployment cost. We first establish a non-linear MTD-DO formulation. Then, two deep reinforcement learning-based algorithms are developed, namely, deep Q-learning (DQN) and proximal policy optimization (PPO). Moreover, two metrics are defined in order to effectively evaluate MTD-DO algorithms with varying network scales and budgets. The experimental results indicate that both PPO- and DQN-based algorithms perform better than Q-learning-based and random algorithms. The DQN-based algorithm converges more quickly and performs, in terms of reward, marginally better than the PPO-based algorithm.

Unveiling the power of Haar frequency domain: Advancing small target motion detection in dim light

Visual small target motion detection finds successful applications in varied scenarios. However, dim-light conditions, such as the tunnel scenes and nighttime environments, present significant challenges to existing detection methods which mainly operate within the spatiotemporal domain. This is because the transmission of small target motion information suffers from the inevitable interference of image noise caused by dim light in the spatiotemporal domain, resulting in the detriment of extracting essential spatiotemporal features of the target motion. Given the significant obstacles posed by dim-light imaging to small target motion detection within the spatiotemporal domain, the exploration of an alternative observation domain for small target motion, alongside the development of a corresponding detection method, emerges as a viable solution. To address this, in this paper, we discovered the remarkable potential of the Haar frequency domain in characterizing the small target motion in dim light. To investigate the advantages of integrating Haar frequency processing in small target motion detection, we introduce a Haar-windowed summation mechanism into an existing bio-inspired small target motion detection model. The proposed mechanism integrates visual information in spatiotemporal windows regulated by frequency parameters of Haar wavelets and effectively discriminates the small target motion from the disturbance of random noise caused by dim light. Theoretical analysis and numerical experiments confirm the superior performance of integrating the Haar frequency processing. This study provides a new vision of small target motion detection through the lens of the frequency domain and extends the limits of existing bio-inspired models for practical applications in dim light.

Spatio-temporal fusion with motion masks for the moving small target detection from remote-sensing videos

Moving small target detection is an eye-catching research topic, full of great challenges in object detection field. Its primary difficulties exist in (i) the very small size proportion of the target region in background images, (ii) the quite weak contrast to background and (iii) the perception of slight target motion. Currently-existing detection methods mainly utilize the image features from spatial domain. This kind of features is quite weak to small targets. More new feature kinds need to be concerned. In view of these, besides traditional image clues, motion features are currently attracting more and more attention in small target detection. In order to promote detection performance, this paper proposes a Spatio-Temporal Fusion Network (STFNet) with two parallel feature extraction branches for detecting moving small targets. In this network, one branch is designed to capture the traditional semantic features from spatial domain, and the other is devised to perceive the slight target motion features hidden in temporal domain. Meanwhile, to optimize the extraction of spatio-temporal features, a group of motion masks is specially designed to guide feature extractors to pay more precise attention to the positions where small targets appear. Moreover, we design a new bridging module to enhance the cross-domain and cross-scale fusion of spatio-temporal features. Through extensive comparison and ablation experiments on seven sub-datasets, it is proved that our new STFNet is effective and it is obviously superior to the compared ones in detecting moving small targets from satellite sequence images. It achieves a Precision of 0.93, an mAP50 of 71.10% and an F1 of 0.84, evidently higher than existing state-of-the-art methods. Our codes are available at https://github.com/UESTC-nnLab/STF.

Multimodal reaching-position prediction for ADL support using neural networks

This study aimed to develop daily living support robots for patients with hemiplegia and the elderly. To support the daily living activities using robots in ordinary households without imposing physical and mental burdens on users, the system must detect the actions of the user and move appropriately according to their motions. We propose a reaching-position prediction scheme that targets the motion of lifting the upper arm, which is burdensome for patients with hemiplegia and the elderly in daily living activities. For this motion, it is difficult to obtain effective features to create a prediction model in environments where large-scale sensor system installation is not feasible and the motion time is short. We performed motion-collection experiments, revealed the features of the target motion and built a prediction model using the multimodal motion features and deep learning. The proposed model achieved an accuracy of 93% macro average and F1-score of 0.69 for a 9-class classification prediction at 35% of the motion completion.

Comparative analysis of tracking and behavioral patterns between wild-type and genetically modified fruit flies using computer vision and statistical methods

Collective animal behavior occurs in groups and swarms at almost every biological scale, from single-celled organisms to the largest animals on Earth. The intriguing mysteries behind these group behaviors have attracted many scholars, and while it is known that models can reproduce qualitative features of such complex behaviors, this requires data from real animals to demonstrate, and obtaining data on the exact features of these groups is tricky. In this paper, we propose the Hidden Markov Unscented Tracker (HMUT), which combines the state prediction capability of HMM and the high-precision nonlinear processing capability of UKF. This prediction-driven tracking mechanism enables HMUT to quickly adjust tracking strategies when facing sudden changes in target motion direction or rapid changes in speed, reducing the risk of tracking loss. Videos of fruit fly swarm movement in an enclosed environment are captured using stereo cameras. For the captured fruit fly images, the thresholded AKAZE algorithm is first used to detect the positions of individual fruit flies in the images, and the motion of the fruit flies is modeled using a multidimensional hidden Markov model (HMM). Tracking is then performed using the Unscented Kalman Filter algorithm to obtain the flight trajectories of the fruit flies in two camera views. Finally, 3D reconstruction of the trajectories in both views is achieved through polar coordinate constraints, resulting in 3D motion data of the fruit flies. Additionally, the efficiency and accuracy of the proposed algorithm are evaluated by simulating fruit fly swarm movement using the Boids algorithm. Finally, based on the tracked fruit fly flight data, behavioral characteristics of the fruit flies are analyzed from two perspectives. The first is a statistical analysis of the differences between the two behaviors. The second dimension involves clustering trajectory similarity using the DTW method based on fruit fly flight trajectories, further analyzing the similarity within clusters and differences between clusters.

Coupled Motion Response Analysis for Dynamic Target Salvage under Wave Action

The strategic recovery of buoys is a critical task in executing deep-sea research missions, as nations extend their exploration of marine territories. This study primarily investigates the dynamics of remotely operated vehicle (ROV)-assisted salvage operations for floating bodies during the recovery of dynamic maritime targets. It focuses on the hydrodynamic coefficients of dual floating bodies in this salvage process. The interaction dynamics of the twin floats are examined using parameters such as the kinematic response amplitude operator (RAO), added mass, damping coefficient, and mean drift force. During the “berthing stage”, when the double floats are at Fr = 0.15–0.18, their roll and yaw Response Amplitude Operators are diminished, resulting in smoother motion. Thus, the optimal berthing speed range for this stage is Fr = 0.15–0.18. During the “side-by-side phase”, the spacing between the ROV and FLOAT under wave action should be approximately 0.4 L to 0.5 L. The coupled motion of twin floating bodies under the influence of following waves can further enhance their stability. The ideal towing speed during the “towing phase” is Fr = 0.2. This research aims to analyze the mutual influence between two floating bodies under wave action. By simulating the coupled motion of dual dynamic targets, we more precisely assess the risks and challenges inherent in salvage operations, thus providing a scientific basis for the design and optimization of salvage strategies.

Destination and time-series inference of moving objects via conditionally Markov process

This paper presents a destination and time-series inference algorithm for tracking moving targets. The destination of the object is considered the intent, and inference and state estimation are performed in the Bayesian framework. To describe the destination-aware target motion, we construct the state transition model using a conditionally Markov process. We introduce a multiple model to achieve simultaneous intent and time-series inferences. Given finite destination candidates, the maximum a posteriori hypothesis is chosen as the destination. For time-series inference, local estimates obtained from Kalman filters are fused to yield target state estimates. To address unspecified terminal conditions, the proposed algorithm incorporates parameter correction techniques based on relative geometry. Numerical simulations are performed to validate the proposed inference algorithm.

A Distributed Deadlock-Free Task Offloading Algorithm for Integrated Communication–Sensing–Computing Satellites with Data-Dependent Constraints

Integrated communication–sensing–computing (ICSC) satellites, which integrate edge computing servers on Earth observation satellites to process collected data directly in orbit, are attracting growing attention. Nevertheless, some monitoring tasks involve sequential sub-tasks like target observation and movement prediction, leading to data dependencies. Moreover, the limited energy supply on satellites requires the sequential execution of sub-tasks. Therefore, inappropriate assignments can cause circular waiting among satellites, resulting in deadlocks. This paper formulates task offloading in ICSC satellites with data-dependent constraints as a mixed-integer linear programming (MILP) problem, aiming to minimize service latency and energy consumption simultaneously. Given the non-centrality of ICSC satellites, we propose a distributed deadlock-free task offloading (DDFTO) algorithm. DDFTO operates in parallel on each satellite, alternating between sub-task inclusion and consensus and sub-task removal until a common offloading assignment is reached. To avoid deadlocks arising from sub-task inclusion, we introduce the deadlock-free insertion mechanism (DFIM), which strategically restricts the insertion positions of sub-tasks based on interval relationships, ensuring deadlock-free assignments. Extensive experiments demonstrate the effectiveness of DFIM in avoiding deadlocks and show that the DDFTO algorithm outperforms benchmark algorithms in achieving deadlock-free offloading assignments.

Knowledge hierarchy-based dynamic multi-objective optimization method for AUV path planning in cooperative search missions

This study focuses on path planning for Autonomous Underwater Vehicles (AUVs) in underwater cooperative search missions. The complexities of the ocean environment, the uncertainty of target movements, and limited communication capabilities render underwater cooperative search missions dynamic multi-objective optimization challenges. Studies focusing on communication-constrained search strategies still lack reliable metrics for assessing underwater communication quality and do not consider communication dead zones. Addressing these deficiencies, this paper introduces a novel communication optimization strategy utilizing packet error rate as a metric for assessing communication efficacy, complemented by a potential field-based method for navigating out of communication dead zones. To tackle the inherently dynamic nature of cooperative search missions for mobile underwater targets, we propose a dynamic multi-objective optimization algorithm that employs a knowledge hierarchy strategy. This method enhances the NSGA-II algorithm's efficiency by extracting effective gene segments from historical Pareto solution set and generating a new initial population through recombination, hierarchy, and prediction. Distinct from other advanced dynamic multi-objective optimization approaches that are limited to theoretical problems, our approach is directly applicable to practical scenarios. The effectiveness and practicality of the proposed method are validated through a series of simulation experiments considering the impact of underwater acoustic communication. These results demonstrate that this research is not only innovative in theory but also holds significant engineering value and practical prospects in real-world applications.

Research on the Motion Control Strategy of a Lower-Limb Exoskeleton Rehabilitation Robot Using the Twin Delayed Deep Deterministic Policy Gradient Algorithm.

The motion control system of a lower-limb exoskeleton rehabilitation robot (LLERR) is designed to assist patients in lower-limb rehabilitation exercises. This research designed a motion controller for an LLERR-based on the Twin Delayed Deep Deterministic policy gradient (TD3) algorithm to control the lower-limb exoskeleton for gait training in a staircase environment. Commencing with the establishment of a mathematical model of the LLERR, the dynamics during its movement are systematically described. The TD3 algorithm is employed to plan the motion trajectory of the LLERR's right-foot sole, and the target motion curve of the hip (knee) joint is deduced inversely to ensure adherence to human physiological principles during motion execution. The control strategy of the TD3 algorithm ensures that the movement of each joint of the LLERR is consistent with the target motion trajectory. The experimental results indicate that the trajectory tracking errors of the hip (knee) joints are all within 5°, confirming that the LLERR successfully assists patient in completing lower-limb rehabilitation training in a staircase environment. The primary contribution of this study is to propose a non-linear control strategy tailored for the staircase environment, enabling the planning and control of the lower-limb joint motions facilitated by the LLERR.

SimpleTrackV2: Rethinking the Timing Characteristics for Multi-Object Tracking.

Multi-object tracking tasks aim to assign unique trajectory codes to targets in video frames. Most detection-based tracking methods use Kalman filtering algorithms for trajectory prediction, directly utilizing associated target features for trajectory updates. However, this approach often fails, with camera jitter and transient target loss in real-world scenarios. This paper rethinks state prediction and fusion based on target temporal features to address these issues and proposes the SimpleTrackV2 algorithm, building on the previously designed SimpleTrack. Firstly, to address the poor prediction performance of linear motion models in complex scenes, we designed a target state prediction algorithm called LSTM-MP, based on long short-term memory (LSTM). This algorithm encodes the target's historical motion information using LSTM and decodes it with a multilayer perceptron (MLP) to achieve target state prediction. Secondly, to mitigate the effect of occlusion on target state saliency, we designed a spatiotemporal attention-based target appearance feature fusion (TSA-FF) target state fusion algorithm based on the attention mechanism. TSA-FF calculates adaptive fusion coefficients to enhance target state fusion, thereby improving the accuracy of subsequent data association. To demonstrate the effectiveness of the proposed method, we compared SimpleTrackV2 with the baseline model SimpleTrack on the MOT17 dataset. We also conducted ablation experiments on TSA-FF and LSTM-MP for SimpleTrackV2, exploring the optimal number of fusion frames and the impact of different loss functions on model performance. The experimental results show that SimpleTrackV2 handles camera jitter and target occlusion better, achieving improvements of 1.6%, 3.2%, and 6.1% in MOTA, IDF1, and HOTA, respectively, compared to the SimpleTrack algorithm.

Visuomotor predictors of interception.

Intercepting moving targets is a fundamental skill in human behavior, influencing various domains such as sports, gaming, and other activities. In these contexts, precise visual processing and motor control are crucial for adapting and navigating effectively. Nevertheless, there are still some gaps in our understanding of how these elements interact while intercepting a moving target. This study explored the dynamic interplay among eye movements, pupil size, and interceptive hand movements, with visual and motion uncertainty factors. We developed a simple visuomotor task in which participants used a joystick to interact with a computer-controlled dot that moved along two-dimensional trajectories. This virtual system provided the flexibility to manipulate the target's speed and directional uncertainty during chase trials. We then conducted a geometric analysis based on optimal angles for each behavior, enabling us to distinguish between simple tracking and predictive trajectories that anticipate future positions of the moving target. Our results revealed the adoption of a strong interception strategy as participants approached the target. Notably, the onset and amount of optimal interception strategy depended on task parameters, such as the target's speed and frequency of directional changes. Furthermore, eye-tracking data showed that participants continually adjusted their gaze speed and position, continuously adapting to the target's movements. Finally, in successful trials, pupillary responses predicted the amount of optimal interception strategy while exhibiting an inverse relationship in trials without collisions. These findings reveal key interactions among visuomotor parameters that are crucial for solving complex interception tasks.

Effects of direction specific exercise training on athletic performance: a systematic review and meta-analysis.

The similarity between movement patterns and force-vector specificity of training exercises and the target movement will likely result in the greatest transfer of the practiced skills and physical abilities to the intended sports skill performance. Therefore, this review aimed to investigate whether specific adaptations in athletic performance would be observed following direction specific exercise training. The literature search was performed in PubMed, Web of Science, and MEDLINE. Studies comparing acute (post-activation potentiation enhancement) and short-term (>2 weeks) effects of horizontally vs. vertically oriented resistance and plyometric training on athletic performance of recreationally active participants of either sex were included. The effect sizes were determined using a robust variance estimation random-effects model and were reported as Hedge's g. Twenty-two studies were included. For acute studies (n = 4), a small non-significant effect favoring horizontal training (HT) for sprint performance improvements (g = -0.19, p = 0.17) was evident. For short-term studies (n = 18), the results showed non-significant, small to large differences between HT and vertical training (VT) in pooled vertical and horizontal jump improvements (g = 0.06, p = 0.67), vertical (g = 0.21, p = 0.17) and horizontal jump (g = -0.15, p = 0.40), pooled vertical and horizontal maximal strength (g = 0.27, p = 0.42), horizontal (g = -0.83, p = 0.16) and vertical maximal strength (g = 0.78, p = 0.28), pooled short and medium distance sprint (g = -0.23, p = 0.16), short (g = -0.33 [-0.85, 0.19], p = 0.19) and medium (g = -0.12 [-0.37, 0.13], p = 0.28) distance sprint, and COD speed and maneuverability (g = -0.45, p = 0.26). HT and VT were both equally effective in improving vertically and horizontally athletic performance, potentially refuting the theory of directional specificity of training on athletic performance outcomes.

A novel internal target volume definition based on velocity and time of respiratory target motion for external beam radiotherapy.

This study aimed to develop a novel internal target volume (ITV) definition for respiratory motion targets, considering target motion velocity and time. The proposed ITV was evaluated in respiratory-gated radiotherapy. An ITV modified with target motion velocity and time (ITVvt) was defined as an ITV that includes a target motion based on target motion velocity and time. The target motion velocity was calculated using four-dimensional computed tomography (4DCT) images. The ITVvts were created from phantom and clinical 4DCT images. The phantom 4DCT images were acquired using a solid phantom that moved with a sinusoidal waveform (peak-to-peak amplitudes of 10 and 20mm and cycles of 2-6s). The clinical 4DCT images were obtained from eight lung cancer cases. In respiratory-gated radiotherapy, the ITVvt was compared with conventional ITVs for beam times of 0.5-2s within the gating window. The conventional ITV was created by adding a uniform margin as the maximum motion within the gating window. In the phantom images, the maximum volume difference between the ITVvt and conventional ITV was -81.9%. In the clinical images, the maximum volume difference was -53.6%. Shorter respiratory cycles and longer BTs resulted in smaller ITVvt compared with the conventional ITV. Therefore, the proposed ITVvt plan could be used to reduce treatment volumes and doses to normal tissues.

Radial velocity estimation method for a single hydrophone based on passive target line spectrum characteristics

Passive radial velocity estimation based on a single hydrophone has a wide range of applications in engineering, and can be used in small underwater platforms such as submerged buoys and underwater gliders. The conventional passive radial velocity estimation method based on a single hydrophone has poor performance due to the influence of noise. In this paper, the time delay coherence coefficient (TDCC) of passive target line spectrum is derived based on a single hydrophone. Further, the radial velocity can be obtained by the time delay required by the phase change of TDCC for one period. Based on the characteristics that the line spectrum phase is stable and the noise phase is random, coherent averaging (CA) is applied to suppress noise. The use of CA greatly reduces the influence of noise on TDCC and efficiently improves the accuracy of radial velocity estimation. The performance and system error of the proposed CA-TDCC method are analyzed through a series of simulations. Finally, the CA-TDCC method is used to process SwellEx-96 data, and the relative error of radial velocity estimation is less than 10%, which verifies the effectiveness of this method in practical applications.Abbreviation: CA, Coherent averaging; SNRs, Signal-to-noise ratios; TDCC, Time delay coherence coefficient; TMA, Target motion analysis.