Micro-motion Dynamics Research Articles

Edge-dependent anomalous topology in synthetic photonic lattices subject to discrete step walks

Anomalous topological phases, where edge states coexist with topologically trivial Chern bands, can only appear in periodically driven lattices. When the driving is smooth and continuous, the bulk-edge correspondence is guaranteed by the existence of a bulk invariant known as the winding number. However, in lattices subject to periodic discrete step walks the existence of edge states does not only depend on bulk invariants but also on the boundary. This is a consequence of the absence of an intrinsic time dependence or micromotion in discrete step walks. We report the observation of edge states and a simultaneous measurement of the bulk invariants in anomalous topological phases in a two-dimensional synthetic photonic lattice subject to discrete step walks. The lattice is implemented using time multiplexing of light pulses in two coupled fibre rings, in which one of the dimensions displays real-space dynamics and the other one is parametric. The presence of edge states is inherent to the periodic driving and depends on the properties of the boundary in the implemented two-band model with zero Chern number. We provide a suitable expression for the topological invariants whose calculation does not rely on micromotion dynamics. Published by the American Physical Society 2024

Floquet engineering of Lie algebraic quantum systems

We propose a `Floquet engineering' formalism to systematically design a periodic driving protocol in order to stroboscopically realize the desired system starting from a given static Hamiltonian. The formalism is applicable to quantum systems which have an underlying closed Lie-algebraic structure, for example, solid-state systems with noninteracting particles moving on a lattice or its variant described by the ultra-cold atoms moving on an optical lattice. Unlike previous attempts at Floquet engineering, our method produces the desired Floquet Hamiltonian at any driving frequency and is not restricted to the fast or slow driving regimes. The approach is based on Wei-Norman ansatz, which was originally proposed to construct a time-evolution operator for any arbitrary driving. Here, we apply this ansatz to the micro-motion dynamics, defined within one period of the driving, and obtain the driving protocol by fixing the gauge of the micro-motion. To illustrate our idea, we use a two-band system or the systems consisting of two sub-lattices as a testbed. Particularly, we focus on engineering the cross-stitched lattice model that has been a paradigmatic flat-band model.

Micro-Motion Dynamics and Shape Parameters Estimation Based on an Infrared Signature Model of Spatial Targets

Infrared imaging is widely applied in the discrimination of spatial targets. Extracting distinguishable features from the infrared signature of spatial targets is an important premise for this task. When a target in outer space experiences micro-motion, it causes periodic fluctuations in the observed infrared radiation intensity signature. Periodic fluctuations can reflect some potential factors of the received data, such as structure, dynamics, etc., and provide possible ways to analyze the signature. The purpose of this paper is to estimate the micro-motion dynamics and geometry parameters from the observed infrared radiation intensity signature. To this end, we have studied the signal model of the infrared radiation intensity signature, conducted the geometry and micro-motion models of the target, and we proposed a joint parameter estimation method based on optimization techniques. After analyzing the estimation results, we testified that the parameters of micro-motion and geometrical shape of the spatial target can be effectively estimated by our estimation method.

Multi-Scale Convolutional Neural Networks for Space Infrared Point Objects Discrimination

Object discrimination plays an important role in an infrared (IR) imaging system. However, at a long observing distance, the presence of detector noise and the absence of robust features make space objects' discrimination difficult to tackle with. In this paper, a multi-scale convolutional neural network (MCNN) is proposed for feature learning and classification. It consists of three parts: transformation, local convolution, and full convolution. Different from previous objects' classification methods, the MCNN can automatically extract features of objects at multi-timescales and multi-frequencies. Low-level features are combined with high-level features to simultaneously capture long-term tendency and short-term fluctuations of the time sequences of IR radiation intensity. Training data are generated from IR radiation models considering micro-motion dynamics and inherent properties of space point objects under different scenarios. The simulation results indicate that our method not only promotes the performance but is also robust to the detector noise. The classification accuracy can reach 96% at a strong noise level (signal-to-noise ratio is 10 dB) in a simulation scenario.

Exo-atmospheric infrared objects classification using recurrence-plots-based convolutional neural networks.

Object discrimination plays an important role in infrared (IR) imaging systems. However, at long observing distance, the presence of detector noise and absence of robust features make exo-atmospheric object classification difficult to tackle. In this paper, a recurrence-plots-based convolutional neural network (RP-CNN) is proposed for feature learning and classification. First, it uses recurrence plots (RPs) to transform time sequences of IR radiation into two-dimensional texture images. Then, a CNN model is adopted for classification. Different from previous object classification methods, RP representation has well-defined visual texture patterns, and their graphical nature exposes hidden patterns and structural changes in time sequences of IR signatures. In addition, it can process IR signatures of objects without the limitation of fixed length. Training data are generated from IR irradiation models considering micro-motion dynamics and geometrical shape of exo-atmospheric objects. Results based on time-evolving IR radiation data indicate that our method achieves significant improvement in accuracy and robustness of the exo-atmospheric IR objects classification.

Parameter Estimation of Micro-Motion Targets for High-Resolution-Range Radar Using Online Measured Reference.

Micro-motion dynamics produce Micro-range (m-R) signatures which are important features for target classification and recognition, provided that the range resolution of radar signal is high enough. However, dechirping the echo with reference measured by narrow bandwidth radar would generate the residual translational motion, which exhibits as random shifts of envelopes of range profiles. The residual translational motion would destroy the periodicity of m-R signatures and make a challenge to estimate rotational parameter. In this work, we proposed an efficient high-resolution range profile (HRRP)-based method to estimate rotational parameter, in which online measured reference distances are used to dechirp the radar raw echo. Firstly, the deduction for the modified first conditional comment of range profiles (MFCMRP) is introduced in detail, and the MFCMRP contain periodic and random components when dechirped by measured reference, corresponding to the rotational motion and the reference measured errors compared with actual reference. Secondly, the Wavelet Transform (WT) is utilized to separate the measured errors from the MFCMRP. The estimations of measured errors are used to compensate the MFCMRP, and then autocorrelation is performed on the estimated periodic component to obtain the estimation of rotational period. Lastly, the rotational amplitudes and phases are achieved by inverse Radon transform (IRT) of the compensated HRRP. The effectiveness of the proposed method in this paper is verified by synthetic data and measured radar data.

Parameter estimation of micro‐motion targets for high‐range‐resolution radar using high‐order difference sequence

Micro-range (m-R) signatures which are induced by micro-motion dynamics can be observed from range profiles, providing that the range resolution of radar is high enough. For real scenarios, micro-motion is often mixed with macro-motion (translation). To extract the micro-motion signatures, it is required to remove the macro-motion component. The widely employed range alignment technique fails for rigid-body targets with micro-motion, since the relative distances between different scattering centres on a rigid-body target are varying and it is unable to obtain a stable reference range profile. Thus, the extracted m-R signatures will be accompanied with residual macro-motion, which may lead to the degradation. However, this issue is often ignored in the research of m-R signatures extraction. In this work, by modelling the motions of scattering centres as the superimposition of a polynomial signal (represents macro-motion) and a sinusoidal signal (represents micro-motion), a micro-motion period estimation method based on high-order difference sequence is proposed. The property that the difference operation can decrease the order of polynomial signals while preserve sinusoidal signals with the same frequency enables the proposed method to extract m-R signatures in the presence of macro-motion. The effectiveness of the proposed method is validated by synthetic and measured radar data.

Analysis of infrared signatures of exo-atmosphere micro-motion objects based on inertial parameters

As one of important physical properties of exo-atmosphere objects, micro-motion dynamics provides extra information for objects discrimination. In this paper, a model and theoretical analysis of time sequence of infrared (IR) signature considering different inertial parameters of micro-motion objects are presented. It overcomes the shortcoming of the existing methods, which depend on the assumption of specific micro-motion types. Firstly, an inertial model of exo-atmosphere micro-motion object is constructed. Next, the relationship between inertial parameters and IR signature model is derived. Finally, power spectral density transformed from the time sequence of IR signature is obtained. Four groups of simulations are conducted demonstrating the effects of inertial parameters posed on IR signature. Simulation results show that inertial parameters including moments of inertia (MOI) and initial angular rate (IAR) determine the period of motion, thus the frequency of time-varying radiant intensity received by the sensor. The proposed method exploits more explicit physical meaning of exo-atmosphere micro-motion objects and provides a novel approach to characterize exo-atmosphere micro-motion objects irrespective of their specific micro-motion types, which exhibits a greater flexibility in IR objects discrimination tasks.

Ballistic targets micro-motion and geometrical shape parameters estimation from sparse decomposition representation of infrared signatures.

Micro-motion dynamics and geometrical shape are considered to be essential evidence for infrared (IR) ballistic target recognition. However, it is usually hard or even impossible to describe the geometrical shape of an unknown target with a finite number of parameters, which results in a very difficult task to estimate target micro-motion parameters from the IR signals. Considering the shapes of ballistic targets are relatively simple, this paper explores a joint optimization technique to estimate micro-motion and dominant geometrical shape parameters from sparse decomposition representation of IR irradiance intensity signatures. By dividing an observed target surface into a number of segmented patches, an IR signature of the target can be approximately modeled as a linear combination of the observation IR signatures from the dominant segmented patches. Given this, a sparse decomposition representation of the IR signature is established with the dictionary elements defined as each segmented patch's IR signature. Then, an iterative optimization method, based on the batch second-order gradient descent algorithm, is proposed to jointly estimate target micro-motion and geometrical shape parameters. Experimental results demonstrate that the micro-motion and geometrical shape parameters can be effectively estimated using the proposed method, when the noise of the IR signature is in an acceptable level, for example, SNR>0 dB.

Extraction of Micro-Doppler Frequency From HRRPs of Rotating Targets

Micro-motion dynamics, such as rapid rotation, vibration and spinning motion, impose additional time-varying frequency modulation on the returned radar signals, which is known as the micro-Doppler (m-D) effect. Micro-Doppler frequency is considered as a stable and unique feature, where the uniqueness means that different micro-motions have distinct signatures. Thus, radar m-D feature extraction is of great potential in target classification and identification. This paper presents m-D frequency estimation from the HRRPs of rotating targets in frequency-stepped signal (FSS) based on the circular correlation (CC) coefficients and the circular average magnitude difference (CAMD) coefficients. The m-D frequency of rotating targets can be estimated accurately from the two proposed methods and the corresponding computational cost burden is also investigated. The accuracy and efficiency of the estimations are compared and revealed by the simulated trials and experimental data.

Micro-motion dynamics analysis of ballistic targets based on infrared detection

Micro-motion dynamics analysis of ballistic targets based on infrared detection

Micromotion dynamics and geometrical shape parameters estimation of exoatmospheric infrared targets

The micromotion dynamics and geometrical shape are considered to be essential characteristics for exoatmospheric targets discrimination. Many methods have been investigated to retrieve the micromotion features using radar signals returned from targets of a given shape. We explore a way of jointly estimating micromotion dynamics and geometrical shape parameters from the infrared (IR) signals of targets in remote detection distance. It is found that the micromotion dynamics of the target would induce a periodic fluctuating variation on the IR irradiance intensity signature. In addition to the micromotion characteristics, the fluctuation could also reflect target structure properties, which offer a possible clue in extracting the features of micromotion dynamics and geometrical shape. Thus, the data model of target IR irradiance intensity signatures induced by micromotion patterns including spinning, coning, and tumbling is developed, and a method of parameters estimation based on joint optimization analysis techniques is proposed. Experimental results demonstrated that the parameters of target micromotion dynamics and geometrical shape can be effectively estimated using the proposed method, if the input signature contains multiple dominant frequency components.

Rotor blades radar echo modeling and its mechanism analysis

Since the rotorcraft can easily be recognized by using the micro-Doppler (m-D) signature of rotor blades, the m-D effect induced by micro-motion dynamics plays an important role in target recognition and classification. However, the existing researches on the rotor blades pay little attention to the mechanism of the time-domain and time-frequency-domain flash phenomena. To comprehensively explain the flash phenomena from physics, the modeling of the rotor blades and the mechanism of the flash phenomena are studied in this paper. Firstly, for the rotor blades, the target cannot be represented as a rigid, homogeneous line nor several points. Taking the scattering coefficients and the interval of adjacent scattering points (the scattering point distribution on the blade) into consideration, the scattering point model of the rotor blade echo is established, and the influence of the scattering point distribution on the radar echo is analyzed as well. The detailed mathematic analysis and comparison results show that the conventional integral model of the rotor blade is only a special case of the scattering point model. Furthermore, In the case where the scattering point model is approximately equivalent to the conventional integral model, the critical interval of adjacent scattering points is deduced by mathematic analysis. Secondly, on the basis of the proposed model above, the physical mechanism of the time-domain and time-frequency-domain flash phenomena is studied from the viewpoint of the electromagnetic (EM) scattering. On the one hand, considering the EM scattering and scattering point distribution, the mechanism of the time-domain flashes is analyzed. Ideally, when the rotor blade is at the vertical position relative to the radar line of sight, i.e., at the flash time, the blade has the strongest echo. At this moment, the radar echo consists of echoes of all scattering points, thus inducing the time-domain flashes. At the non-flash time, the scattering points at the tip of blade and hub of rotor have stronger scattering intensities, so the echo is much weaker than that at the flash time. On the other hand, the time-frequency analysis and the cross range resolution are simultaneously used to analyze the mechanism of the time-frequency-domain flashes in the m-D signature. The m-D signature of the rotor blades consists of three parts: the time-frequency-domain flashes, the sinusoidal Doppler curves, and the zero-frequency band. At the flashes time, the Doppler frequency of adjacent scattering points cannot be distinguished, thus the m-D signature has the frequency band caused by all scattering points, i.e., the time-frequency-domain flashes appear. At the non-flash time, the sinusoidal Doppler curves and the zero-frequency band are caused by the scattering points at the tip of blade induced by the scattering points at the hub of rotor respectively. Finally, the simulation results about the scattering point model with the different intervals of adjacent scattering points show that the effectiveness of the proposed model and the correctness of theoretical analysis.

Imaging and micro‐Doppler analysis of vibrating target in multi‐input–multi‐output synthetic aperture radar

The micro-Doppler (m-D) signature induced by the micro-motion dynamics of a component or structure on a radar target can provide additional information for classification, recognition, and identification of the object. Since the m-D signature from conventional synthetic aperture radar (SAR) is insufficient for further application, attention centred on multi-input–multi-output (MIMO) SAR. MIMO SAR employs diversity to improve SAR performance and shows great potential in applications including high-resolution wide-swath remote sensing, slow moving target detection, and three-dimensional (3D) imaging. Take vibration for research, because vibration is a basic kind of micro-motion and it occurs commonly such as vehicle engine. In this study, the vibration signal modelling is deduced and the m-D signature of vibrating target is analysed, based on short-term shift-orthogonal waveform in MIMO SAR. Simulations verify the influence of vibration parameters on imaging and time–frequency profile and show the m-D signature difference through different MIMO channels. The results demonstrate the potential of MIMO SAR for 3D micro-motion estimation.

Parameter Estimation of Radar Targets with Macro-Motion and Micro-Motion Based on Circular Correlation Coefficients

Micro-Doppler (m-D) signatures induced by micro-motion dynamics, which are of great importance for target classification, have received increasing attention among the radar community. For scenarios when the micro-motion of radar target is accompanied by macro-motion, the periodicity of m-D is disturbed by macro-motion. In that case, extraction of micro-motion signatures based on the periodicity of time-frequency representation (TFR) of the radar echo may become invalid. In this work, we show that the periodicity of TFR is replaced by circular periodicity in the presence of macro-motion. In view of this, the circular correlation coefficients of TFR are employed to characterize the circular periodicity of TFR and to provide an estimate of the micro-motion period. The property of circular correlation coefficients enables us to estimate the micro-motion period of radar targets in the presence of macro-motion. Experiments with synthetic data and measured radar data validate the effectiveness of the proposed method.

Micro-motion frequency estimation of radar targets with complicated translations

Micro-Doppler (m-D) signatures induced by micro-motion dynamics, which are of great importance for target classification, have received increasing attention among the radar community. However, most of the existing m-D signature-extraction methods are based on the assumption that translations of radar targets have been completely compensated by other methods or they merely involve scenarios when radar targets undergo low order polynomial translations. These methods may become invalid for maneuvering targets with complicated translations. In this work, radar targets with micro-motion and complicated translations are considered. Inspired by the differential element method and the shift invariant difference operation, a micro-motion frequency estimation method based on piecewise translation compensation (PTC) and time-frequency squared difference sequences (TFSDS) is proposed. The PTC can compensate the translations of radar targets at the largest extent. After compensation, the TFSDS-based frequency estimator gives an estimation of micro-motion frequency; this estimator can remove the effect of residual translation and avoid the separation of the multicomponent radar echo. The combination of PTC and TFSDS enables us to extract the micro-motion frequency of radar targets with complicated translations without a prior knowledge. Experiments with synthetic and measured data confirm the effectiveness and good performance of the proposed method.

Feature extraction of micro-motion frequency and the maximum wobble angle in a small range of missile warhead based on micro-Doppler effect

Micro-Doppler effect is induced by the micro-motion dynamics of the radar target itself or any structure on the target. In this paper, a simplified cone-shaped model for ballistic missile warhead with micro-nutation is established, followed by the theoretical formula of micro-nutation is derived. It is confirmed that the theoretical results are identical to simulation results by using short-time Fourier transform. Then we propose a new method for nutation period extraction via signature maximum energy fitting based on empirical mode decomposition and short-time Fourier transform. The maximum wobble angle is also extracted by distance approximate approach in a small range of wobble angle, which is combined with the maximum likelihood estimation. By the simulation studies, it is shown that these two feature extraction methods are both valid even with low signal-to-noise ratio.

Bistatic occlusion effect of missile warhead based on micro-Doppler effect

Micro-Doppler effect is induced by the micro-motion dynamics of the radar target itself or any structure on the target. Spinning, coning and wobble are typical basic micro-motions of missile warhead. In this paper, a cone-shaped model for missile warhead with micro-nutation is established under the case of bistatic radar system, followed by the theoretical formula of bistatic micro-nutation is derived. Using the method of short-time Fourier transform, which confirms the simulated results are identical to the theoretical results. In addition, in order to further approach the actual environment. Base on micro-Doppler effect, the effective scattering center model with occlusion effect is considered under the case of bistatic radar system. The simulated results are compared with the fixed scattering point model without the occlusion effect. By the simulation studies, it shows the otherness between the actual environment and ideal assumption. A certain of theoretical analysis conclusions for extracting the features of missile warhead in bistatic radar system are gained, which can be applied to radar target recognition in the future work.

ROTATIONAL MICRO-MOTION MODULATED JAMMING FOR COUNTERING ISAR BASED ON INTERMITTENT SAMPLING REPEATER

The intermittent sampling repeater jamming (ISRJ) can only produce fake point scatterers in the down-range, while the time- varying frequency induced by rotational micro-motion dynamics blurs the image in the cross-range. This paper focuses on the combination of the above two methods to counter the inverse synthetic aperture radar (ISAR). Based on the signal models of coherent ISAR signal processing, principles of the jamming are derived. The key jamming parameters and the selection criteria are determined by two steps. Finally, the validity of the proposed algorithm is demonstrated using the numeric simulations and simulations based on the measured data.

Modeling Time-Evolving Infrared Characteristics for Space Objects With Micromotions

A space object undergoes micromotion dynamics such as spinning, coning, and rolling, in addition to the centroid translation along the trajectory in space. In this work, by taking into account micromotion dynamics and heat transfer, an infrared (IR) signature prediction model for space objects is developed. Results demonstrate that micromotion dynamics results in periodic variation of the projected area to spaceborne sensors, thus influencing the IR characteristics of the space object.