Target Motion Parameters 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 Novel Joint Motion Compensation Algorithm for ISAR Imaging Based on Entropy Minimization.

Space targets move in orbit at a very high speed, so in order to obtain high-quality imaging, high-speed motion compensation (HSMC) and translational motion compensation (TMC) are required. HSMC and TMC are usually adjacent, and the residual error of HSMC will reduce the accuracy of TMC. At the same time, under the condition of low signal-to-noise ratio (SNR), the accuracy of HSMC and TMC will also decrease, which brings challenges to high-quality ISAR imaging. Therefore, this paper proposes a joint ISAR motion compensation algorithm based on entropy minimization under low-SNR conditions. Firstly, the motion of the space target is analyzed, and the echo signal model is obtained. Then, the motion of the space target is modeled as a high-order polynomial, and a parameterized joint compensation model of high-speed motion and translational motion is established. Finally, taking the image entropy after joint motion compensation as the objective function, the red-tailed hawk-Nelder-Mead (RTH-NM) algorithm is used to estimate the target motion parameters, and the joint compensation is carried out. The experimental results of simulation data and real data verify the effectiveness and robustness of the proposed algorithm.

A coherent integration method of an active sonar for maneuvering turning target detection.

The detection probability of underwater weak targets using active sonar is low, and inter-pulse coherent integration can improve the signal-to-noise ratio of echoes. When a target executes a maneuvering turn, complex range and Doppler frequency migrations occur during the coherent integration time that decrease the coherent integration gain. Most existing integration methods simplify the target motion to a finite-order polynomial model but fail to integrate a maneuvering turning target (MTT) due to model mismatch. Hence, this study proposes an underwater MTT integration method based on the modified Radon-Fourier transform. The proposed method constructs a theoretically accurate motion model for the MTT and a phase compensation function to compensate for the Doppler frequency migration. Furthermore, it yields a well-focused integration peak in the range-velocity and offset angle-turn rate dimensions and accurately estimates the target motion parameters. Moreover, the proposed method is suitable for targets with radial and oblique uniform motions. The effectiveness of the proposed method is demonstrated through simulations and a lake test. The proposed method demonstrates good integration performance, with an integration gain approximately 4-7 dB higher than that of traditional methods when using 30 integration pulses.

Two-phase visual servoing for capturing tumbling non-cooperative satellites with a space manipulator

In this paper, a visual servoing approach is developed to capture the docking rings of tumbling non-cooperative satellites with a space manipulator. The primary challenge addressed is the potential for the docking ring to leave the monocular camera’s field-of-view as the manipulator approaches the target, due to the ring’s large size. To solve this issue, a two-phase visual servoing scheme combining a monocular camera and a three-line structured light vision system is proposed. In an effort to augment the success rate and safety of capture operations, several constraints are formulated, encompassing manipulator’s kinematics, monocular camera’s field-of-view, obstacle avoidance, structured light’s breakpoints and smooth capture. Subsequently, a nonlinear model predictive controller is proposed to manage these constraints in real-time and regulate the system. System models are established based on image moments and pose for each phase, selecting these features as visual feedback to simplify the formulation of servo constraints and avoid the complex circle-based pose measurement. Furthermore, to ensure unbiased predictions, the model disturbances arising from the imprecise estimation of target motion parameter are observed using an extended Kalman filter, which are then incorporated into the predictive control framework. The simulation results demonstrate the effectiveness of this scheme.

An Underwater Localization Algorithm for Airborne Moving Sound Sources Using Doppler Warping Transform

When an airborne sound source is in rapid motion, the acoustic signal detected by the underwater sensor experiences a substantial Doppler shift. This shift is intricately linked to the motion parameters of the sound source. Analyzing the Doppler shift characteristics of received acoustic signals enables not only the estimation of target motion parameters but also the localization of the airborne sound source. Currently, the predominant methods for estimating parameters of uniformly moving targets are grounded in classical approaches. In this study, the application of the Doppler warping transform, traditionally applicable to sound sources in uniform linear motion, is extended to encompass a broader spectrum of sound source trajectories. Theoretical and experimental data validate the efficacy of this transform in linearizing the Doppler shift induced by a source in curved motion. Building upon this foundation, a methodology is proposed for locating airborne acoustic sources in curved motion from underwater. Sea experimental data corroborate the method’s effectiveness in achieving underwater localization of a helicopter target during curved motion.

Parameter estimation of ground moving targets in synthetic aperture radar systems based on vortex echo data

Ground Moving Target Indication is a critical field within synthetic aperture radar (SAR) research, as traditional SAR images are defocused and displaced due to the target’s trajectory-direction velocity and radial velocity, respectively. Therefore, an accurate estimation of the target’s motion parameters is required. This study introduces a two-dimensional method for estimating target motion parameters using vortex SAR. It utilises the Bessel magnitude and spatial phase term from vortex echo data to calculate the pitch and azimuth angle, from which the motion parameters of ground slow-motion targets are derived. The proposed algorithm operates faster and has a lower computational cost than the traditional parameter estimation algorithm. Its efficacy was confirmed through simulation experiments and mean square error analysis of the estimated parameters.

Line spectrum detection and motion parameters estimation for underwater moving target

For the detection of underwater moving target with constant speed at low signal-to-noise ratio (SNR), a track-before-detect method is proposed based on motion parameters estimation and hidden Markov model (HMM). Optimization algorithm is used for the moving target motion parameter estimation with cost function obtained by energy accumulation along line spectrum tracked with 1-D Hidden Markov model in spatially filtered LOFAR spectrum. This method is suitable for detecting weak targets with fast changes in azimuth.

Passive localization using horizontal dual-array acoustic intensity striations

The formation of interference striations is closely related to channel structure and target location, which has attracted extensive attention from scholars at home and abroad. In this paper, the passive localizing method using observed interference striations of horizontal dual-arrays will be studied. The relationship between interference phenomena and target motion is discussed firstly, then the basic principle of the passive positioning method by using horizontal dual-array acoustic intensity striations is derived mathematically. Meanwhile, it can be seen that the shape of the interference striations is related to the parameters, which reflects the motion parameters of the target. This provides mathematical theory support for extracting the the target motion state parameters carried out by Hough transformation according to these acoustic interference striations.The simulation results show that the proposed method can effectively obtain the targets’ range. Meanwhile, comparing the ranging results of different groups of parameters, we can see that the more accurate the target motion parameters are, the smaller the estimated target distance error is. Finally, the performance of the algorithm under different noise conditions is discussed. The simulation results show that the error of extracted parameters is larger under the condition of low SNR, which leads to a larger passive positioning error. However, at high SNR, the range estimation performance is good, within 10%.

Adaptive hybrid Kalman filter for attitude motion parameters estimation of space non-cooperative tumbling target

Attitude motion parameters estimation of the space non-cooperative tumbling target is the premise of the on-orbit target capture task. However, existing methods cannot balance the estimation accuracy and efficiency. In this paper, an adaptive hybrid Kalman filter is presented to estimate the attitude, angular velocity and inertia motion parameters of the space non-cooperative tumbling target. First, the dynamic model of the non-cooperative tumbling target is derived, and the nonlinear discrete state-space equation is obtained. Then, a hybrid Kalman filter is designed by combining the extended Kalman filter and the unscented Kalman filter. The adaptive technique is introduced to track time-varying uncertain measurement noise caused by complex space disturbances. After that, based on the first-order linearization technique, the sufficient condition for the local convergence of the proposed method is proved. Finally, the numerical simulation experiment was conducted to compare the proposed method with the adaptive extended Kalman filter and the adaptive unscented Kalman filter. The experimental results show that the estimation accuracy of the proposed method is much higher than that of the adaptive extended Kalman filter and slightly lower than that of the adaptive unscented Kalman filter, and the iteration-average Central-Processing-Unit time is 64.2% smaller than that of the adaptive unscented Kalman filter and slightly longer than that of the adaptive extended Kalman filter. The proposed method achieves a much higher accuracy with a slight loss of iteration-average Central-Processing-Unit time.

Adaptive pseudo‐spectrum based track‐before‐detect for targets with uncertain existence

AbstractIn the existing velocity filtering based track‐before‐detect (VF‐TBD), target existence is generally assumed to be constant, and multiple successive frames in a sliding window are jointly processed. However, when the target existence changes abruptly in a processing batch, the incorporation of noise‐only frames may corrupt target energy integration. This results in degraded detection and estimation performances with serious decision delays. A pseudo‐spectrum based VF‐TBD is proposed to effectively detect and track targets with existence uncertainty. An adaptive integration strategy is developed to realise effective energy accumulation with only informative frames by matching target appearance and disappearance times in addition to the velocity. The change of target existence can be discovered in time, and the decision delay is reduced. An estimation strategy based on the maximum output SNR is presented to supply accurate estimations of not only target motion parameters (including the position and velocity) but also time parameters (including appearance and disappearance times). The target output envelope, SNR gain and receiver operating characteristic curve of targets with uncertain existence are analysed theoretically. Numerical simulations exhibit the validity of the proposed method.The cover image is based on the Original Article Adaptive pseudo‐spectrum based track‐before‐detect for targets with uncertain existence by Peiyuan Li et al., https://doi.org/10.1049/rsn2.12502.

Fixed-time controller of visual quadrotor for tracking a moving target

In order to eliminate the dependence of finite-time control on the initial state of the system in the image-based visual servoing (IBVS) of a quadrotor UAV (QUAV) for the moving target, we propose a control scheme for the moving target of a QUAV based on fixed-time stability. We select the image moments of the virtual camera plane as features and establish an image moment dynamics model that includes target motion parameters based on them. Then, we use the backstepping method to design the fixed-time controller for the system. The design steps of the controller mainly consist of two parts. Firstly, we designed a fixed-time stable linear velocity observer to address the unmeasurable linear velocity and unmeasurable monocular camera depth issues of QUAV. Then, using a high-order tracking differentiator to estimate the target linear velocity as a feedforward, combined with the fixed-time linear velocity observer estimated QUAV linear velocity, we designed a fixed-time controller for the system using the backstepping method. We demonstrated the fixed-time stability of the controller using the Lyapunov theory. The effectiveness and robustness of the proposed method are demonstrated by numerical simulation. At the same time, the comparative simulation shows that the method can guarantee the rate of convergence of the system and has high control accuracy.

Continuous dynamic measurement of frequency scanning interferometry based on motion phase synchronization compensation and calibration.

We present a continuous dynamic frequency scanning interferometry (DFSI) measurement method based on motion phase synchronization compensation and calibration. By introducing heterodyne interferometry (HI) synchronization measurement and frequency scanning interferometry (FSI) motion phase compensation, dynamic continuous measurement is achieved and effectively suppresses the distance error introduced by the Doppler effect (DE). Based on this, the influence of the initial optical frequency deviation (OFD) of the tunable laser and the OFD of the HI laser on the dynamic absolute distance measurement (DADM) is analyzed; the relationships between the error of DADM with the variation of the OFD and the target motion parameters are investigated; and the residual DE introduced by the OFD is shown as the fundamental cause of the degradation of the accuracy of DFSI. We propose an online optical frequency measurement method based on HI combined with H13C14N gas absorption cells to resolve this problem. High-precision motion phase compensation is achieved by calibrating the optical frequency (fixed frequency) of the measured HI laser and the initial frequency of the tunable laser online during measurement and then performing motion phase calibration. To verify the effectiveness of our method, an optical frequency calibration experiment, a continuous DADM experiment, and a precision evaluation experiment were conducted, and a highly accurate continuous DADM was achieved.

A Coherent Integration and Parameter Estimation Method for Constant Radial Acceleration Weak Target via SOKT-IAR-LVD

In order to enhance the detection and parameter estimation capacity to the maneuvering target with complex motions, a low complexity coherent integration and parameter estimation method named SOKT-IAR-LVD is proposed in this paper. In SOKT-IAR-LVD, first, the second-order keystone transform (SOKT) is utilized to eliminate the range curvature induced by target acceleration. Second, improved axis rotation (IAR) is applied to regulate the linear range migration by rotating the fast time axis and the target envelope is aligned along the slow time axis with a quadratic phase characteristic. At last, the target signal is coherently integrated via the Lv’s Distribution (LVD) transform. The target motion parameters, including range, velocity, and acceleration, are estimated by the IAR and LVD results. The integration gain and computational load of SOKT-IAR-LVD are analyzed. Without needing to estimate the Doppler ambiguity number and target acceleration, the computational burden of SOKT-IAR-LVD is three orders of magnitude lower than that of the Radon-Lv’s Distribution (RLVD) method. Simulation results demonstrate that the detection performance of SOKT-IAR-LVD is almost the same as that of RLVD and that the required input SNR of SOKT-IAR-LVD is 17.4 dB lower than that of SOKT–Radon Fourier transform (SOKT-RFT) when the detection threshold is set to 12 dB.

Estimation of Target Motion Parameters from the Tonal Signals with a Single Hydrophone.

In the shallow-water waveguide environment, the tonal signals radiated by moving targets carry modal interference and Doppler shift information. The modal interference can be used to obtain the time of the closest point of approach (tCPA) and the ratio of the range at the closest point of approach to the velocity of the source (rCPA/v). However, parameters rCPA and v cannot be solved separately. When tCPA is known, the rCPA and the v of the target can be obtained theoretically by using the Doppler information. However, when the Doppler frequency shift is small or at a low signal-to-noise ratio, there will be a strong parametric coupling between rCPA and v. In order to solve the above parameter coupling problem, a target motion parameter estimation method from tonal signals with a single hydrophone is proposed in this paper. The method uses the Doppler and modal interference information carried by the tonal signals to obtain two different parametric coupling curves. Then, the parametric coupling curves can be used to estimate the two motion parameters. Simulation experiments verified the rationality of this method. The proposed method was applied to the SWellEx-96 and speedboat experiments, and the estimation errors of the motion parameters were within 10%, which shows the method is effective in its practical applications.

High-Speed Maneuvering Target Inverse Synthetic Aperture Radar Imaging and Motion Parameter Estimation Based on Fast Spare Bayesian Learning and Minimum Entropy

High-speed maneuvering target inverse synthetic aperture radar (ISAR) imaging is always a hot topic in signal processing. High-speed maneuvering targets often have high-order maneuvering characteristics, such as translational and rotational characteristics, which destroy the signal structure of stationary targets and make basic imaging processing methods such as range-Doppler (RD) algorithm no longer suitable. In this paper, a high-resolution imaging method for high-speed maneuvering targets is proposed, which uses the fast sparse Bayesian learning (SBL) algorithm and the minimum entropy algorithm for ISAR high-resolution imaging and motion parameter estimation, respectively. Because SBL makes full use of the characteristics of the target and the environment, it can obtain an ISAR high-resolution image of the maneuvering target. However, the high computational complexity caused by matrix inversion and some matrix operations in SBL iteration limit the practical application of SBL in ISAR imaging. In view of the special structure of the matrix required to be inverted, we propose a fast SBL algorithm, which uses a new decomposition method to obtain the decomposition formula of the inverse matrix. Based on the decomposition factors, the multiplication operation involving the inverse matrix can be quickly calculated using fast Fourier transform (FFT), which greatly improves the computational efficiency. Image entropy represents the sharpness and focusing degree of an image, and so the minimum entropy algorithm can estimate the motion parameters of maneuvering targets more accurately. We combine the minimum entropy algorithm with the fast SBL algorithm to realize phase error correction and high-resolution imaging, which has better noise sensitivity and can obtain the best focusing degree image. Finally, simulation results prove the effectiveness of this algorithm.

The Dead-reckoning Navigation Guidance Law Based on Neural Network Collaborative Forecasting

For predicting missile’s interception point, the current guidance law based on neural networks avoids to model the strong nonlinear motion of a missile and simultaneously improve the anti-jamming ability of the guidance law. Although the advantages of solving the predicted intercept point problem based on neural networks are obvious, the difficulty in obtaining the target missile information still exists. In this work, we propose a dead-reckoning navigation guidance (DRNG) law. First, a neural network-based collaborative forecast scheme is proposed and utilize the advantages of different neural networks to greatly reduce the difficulty in acquiring the target information. Second, we construct an approximate realistic aerodynamic characteristics environment to simulate the motion parameters of missiles and targets. We also introduce real-time error correction for increasing the prediction accuracy of the network and improve the robustness of the proposed DRNG by using the model self-update algorithm. Finally, through a large number of simulation experiments, results show that the proposed DRNG completes the interception task in a noisy environment, when only the position parameters of a target missile are known. Moreover, it has a more optimized ballistic trajectory as compared with the traditional guidance law.

Signal Accumulation Method for High-Speed Maneuvering Target Detection Using Airborne Coherent MIMO Radar

The airborne coherent multi-input multi-output (MIMO) radar benefiting from coherent integration (including intra-channel integration and inter-channel integration) of multi-channel echoes can obtain superior detection performance for high-speed maneuvering targets. However, due to the coupling motion characteristic between multiple airborne platforms and high-speed targets, the range walk (RW) and Doppler walk (DW) will occur within the intra-channel integration. Besides, the inter-channel signals also exist envelope and phase differences, which will lead to the difficulty in multi-channel integration. We make contributions towards addressing these limitations. On the one hand, we establish a three-dimensional (3-D) geometrical model for high-speed maneuvering target detection with airborne coherent MIMO radar and derive the mathematical expression of radar echoes, while the generalized Radon-Fourier transform (GRFT) is utilized to simultaneously correct the RW and DW for acquiring intra-channel integration. On the other hand, we analyze the properties of GRFT outputs and propose a GRFT-domain integration method to achieve the multi-channel coherent integration. More specifically, the proposed method first constructs a set of coupling equations for the estimation of target motion parameters (i.e., position, velocity and acceleration), then, the target's motion parameters can be estimated by Newton-Raphson method and solving the linear equations. Second, in order to settle the envelope and phase differences between multi-channel echoes, the envelope alignment and phase compensation functions of inter-channel echoes are constituted based on the estimated parameters. Finally, the coherent accumulation of multi-channel echoes can be implemented. Numerical simulations demonstrate the effectiveness of the proposed method.

Modified method of invariant immersion in the synthesis of measuring procedures for estimating the motion parameters of a maneuvering target

The problem of processing the results of radar measurements of the target motion parameters under conditions of disturbances due to aircraft maneuvers is considered. In order to adapt to the disturbances of measuring procedures, the method of invariant immersion has been modified. The application of the modified method in the case of a linear observation equation leads to a widespread form of a discrete measurement procedure obtained in this paper as a special case of modified invariant immersion equations. On the basis of statistical modeling of digital processing of the results of radar measurements of the movement parameters of the maneuvering target, the efficiency indicators of radar stations are calculated – these are the averaged absolute and relative errors in the measurement of motion parameters, the averaged wiring coefficient and the averaged time of the duration of the breaks of the tracks. Their analysis allows us to assert the high efficiency of using new measuring procedures synthesized using acceleration models in the form of white noise and Singer in the information and measurement systems of radar stations. This becomes possible due to the presence of the matrix coefficient of adaptation in the structure of the modified equations of invariant immersion. The comparison was carried out with Kalman measurement procedures synthesized using these acceleration models. Calculations of performance indicators confirm the reliability of the results obtained, since the wellknown fact of an increase in the error of measuring the height of an aircraft by monopulse radar stations near the surface is demonstrated. The results of the study will be useful in the development of adaptive measurement procedures for information and measurement systems operating under conditions of disturbances of measuring processes, for example, for survey locators.

High Maneuvering Target Long-time Coherent Integration and Motion Parameters Estimation Based on Bayesian Compressive Sensing

This paper intends to address the long-time coherent integration (LTCI) of high maneuvering targets in low signal-to-noise (SNR) environments with high computational efficiency and accuracy. The high-order motion parameters estimation, considering the acceleration and jerk, is modeled as the under-estimated linear regression and the complex-field Bayesian compressive sensing (BCS) algorithm is introduced to resolve the sparse recovery. To correct the high-order range migration (RM) and reduce the order of Doppler frequency migration (DFM), the adjacent cross correlation (ACCF) is applied. An effective method based on non-coherent integration is proposed to extract the self-term from ACCF result. Further, with the proper design of sensing matrix, the acceleration and jerk motion parameters are estimated by sparse reconstruction based on the complex-field BCS. Compared with the traditional methods based on time-frequency transform, like Lv's distribution (LVD), the proposed BCS algorithm is free from the interference of cross-terms and maintains the super-resolution ability, which provides better performance in parameters estimation and multi-targets discrimination. Finally, the numerical experiments validate the advantages of the proposed method in motion parameters estimation, super-resolution ability and computational efficiency.

TDOA –FDOA method for tracking of a moving target in a distributed sensor scenario

Tracking of an underwater target in ocean environment is very important in both military and civilian applications. This paper focuses on the localization and tracking of a moving target by a passive multi-static sonar system using TOA, Time Difference and Frequency Difference of Arrival measurements. An algorithm is developed for an acoustic sensor distributed network of five sensors and an acoustic moving source. TDOAFDOA method is used to track a moving source usingTime Difference of Arrival (TDOA) and Frequency Difference of Arrival (FDOA) measurements, as a closed form solution. The highly nonlinear measurement equations of TDOA and FDOA are first converted into pseudo linear equations. This is achieved by introducing extra parameters. These equations are solved using a weighted least squares (WLS) method. Five distributed stationary receivers are used to track a moving acoustic target. The moving target parameters estimated from the above WLS method are improved by an Unscented Kalman Filter. The mean square errors of these estimates are compared and the results are analyzed.