Moving target detection in clutter background with FDA‐MIMO radar via three‐dimensional focus processing

This paper proposes a new method of beamforming for colocated MIMO radar with the coprime arrays. The proposed method splits the transmitting array into multiple overlapped subarrays and then transmits a coherent signal through each subarray, to achieve a maximum power in the desired direction. However, the signals from distinct subarrays are independent to preserve the diversity benefit of the MIMO radar. Thus, the proposed method exploits the advantages of both phased array and MIMO radars as well as the coprime arrays. Simulation results are presented to signify the beampattern and the signal to interference plus noise ratio performance of the proposed method and to compare with the earlier methods such as phased array radar, MIMO radar and the coprime MIMO radar.

Radar maneuvering target detection in clutter background should not only consider the complex characteristics of the target to accumulate its energy as much as possible, but also suppress clutter to improve the signal-to-clutter ratio (SCR). The traditional fractional domain transform-based detection method requires parameters match searching, which costs heavy computational burden in case of a large amount of data. Sparse FT and sparse fractional FT can obtain high-resolution sparse representation of the target, but the signal sparsity needs to be known before, and the sparse representation performance is poor in clutter background. In this article, adaptive filtering method is introduced into the sparse fractional ambiguity function (SFRAF) method, and a SFRAF domain adaptive clutter suppression and highly maneuvering target detection algorithm is proposed, which is named as adaptive SFRAF (ASFRAF). The ASFRAF domain iterative filtering operation can suppress the clutter while retaining the signal energy as much as possible. Simulation results and measured radar data processing results show that the proposed algorithm can overcome the limitation of the SFRAF on the sparsity preset value and achieve high efficiency and robust detection of high-order phase maneuvering targets under a low SCR environment.

In a clutter-dominant target detection problem, an adaptive detector needs to either whiten or null the clutter prior to signal detection. To whiten clutter, it is required to estimate the covariance matrix of a received clutter, while a proper set or estimate of the parameters of clutter subspace model is required to null the received clutter. In this paper, we investigate the detection performance of a subspace-based detector and a sample covariance matrix(SCM)-based detector presented in [1] and [2], respectively, but for a mono-static radar system. Our simulation results show that the subspace-based detector not only can reach a predetermined probability of false alarm (fully nulling clutter) but also offers better detection performance as compared with its counterpart. In this case, the SCM-based detector only offers better detection performance for detecting targets with Doppler frequency resided in clutter region as compared with that of subspace-based one.

Joint transform correlator is one of the most successful optical pattern recognizers which is composed of Ar⁺ laser, spatial filter, Fourier transform lenses, EALCD (electrically addressed liquid crystal displays) and CCD (Charge Coupled Device). It has many applications in industry and military. The bottle technologies are how to recognize the target in clutter scene and how to increase the brightness of the correlation peak which represents the detected target. In this paper, we researched joint transform correlator (JTC) for infrared target detection, and used wavelet transform technology to increase the contrast of input image which contains target and reference objects, reduced the high frequency noise and realized target detection in clutter scene. According to our requirement, we selected the two-order spline wavelet, and made input image have discrete binary wavelet transformed. Because edge always contains much information of image, we adopted the method of modular maximum to extract the edge of different scales after wavelet transform mentioned above. In addition, combined with threshold division which could eliminate some of the clutter, we could get the remarkable edge. As experiment result, we detected the infrared image, both with computer correlation matched method and optical JTC method. The experiments show that the wavelet transform technology is one of the best data processing methods for the target detection in clutter background.

PREFACE. CONTRIBUTORS. 1 MIMO Radar - Diversity Means Superiority (Jian Li and Petre Stoica). 1.1 Introduction. 1.2 Problem Formulation. 1.3 Parameter Identifiability. 1.4 Nonparametric Adaptive Techniques for Parameter Estimation. 1.5 Parametric Techniques for Parameter Estimation. 1.6 Transmit Beampattern Designs. 1.7 Conclusions. Appendix IA Generalized Likelihood Ratio Test. Appendix 1B Lemma and Proof. Acknowledgments. References. 2 MIMO Radar: Concepts, Performance Enhancements, and Applications (Keith W. Forsythe and Daniel W. Bliss). 2.1 Introduction. 2.2 Notation. 2.3 MIMO Radar Virtual Aperture. 2.4 MIMO Radar in Clutter-Free Environments. 2.5 Optimality of MIMO Radar for Detection. 2.6 MIMO Radar with Moving Targets in Clutter: GMTI Radars. 2.7 Summary. Appendix 2A A Localization Principle. Appendix 2B Bounds on R(N). Appendix 2C An Operator Norm Inequality. Appendix 2D Negligible Terms. Appendix 2E Bound on Eigenvalues. Appendix 2F Some Inner Products. Appendix 2G An Invariant Inner Product. Appendix 2H Kronecker and Tensor Products. Acknowledgments. References. 3 Generalized MIMO Radar Ambiguity Functions (Geoffrey San Antonio, Daniel R. Fuhrmann, and Frank C. Robey). 3.1 Introduction. 3.2 Background. 3.3 MIMO Signal Model. 3.4 MIMO Parametric Channel Model. 3.5 MIMO Ambiguity Function. 3.6 Results and Examples. 3.7 Conclusion. References. 4 Performance Bounds and Techniques for Target Localization Using MIMO Radars (Joseph Tabrikian). 4.1 Introduction. 4.2 Problem Formulation. 4.3 Properties. 4.4 Target Localization. 4.5 Performance Lower Bound for Target Localization. 4.6 Simulation Results. 4.7 Discussion and Conclusions. Appendix 4A Log-Likelihood Derivation. Appendix 4B Transmit-Receive Pattern Derivation. Appendix 4C Fisher Information Matrix Derivation. References. 5 Adaptive Signal Design For MIMO Radars (Benjamin Friedlander). 5.1 Introduction. 5.2 Problem Formulation. 5.3 Estimation. 5.4 Detection. 5.5 MIMO Radar and Phased Arrays. Appendix 5A Theoretical SINR Calculation. References. 6 MIMO Radar Spacetime Adaptive Processing and Signal Design (Chun-Yang Chen and P. P. Vaidyanathan). 6.1 Introduction. 6.2 The Virtual Array Concept. 6.3 Spacetime Adaptive Processing in MIMO Radar. 6.4 Clutter Subspace in MIMO Radar. 6.5 New STAP Method for MIMO Radar. 6.6 Numerical Examples. 6.7 Signal Design of the STAP Radar System. 6.8 Conclusions. Acknowledgments. References. 7 Slow-Time MIMO SpaceTime Adaptive Processing (Vito F. Mecca, Dinesh Ramakrishnan, Frank C. Robey, and Jeffrey L. Krolik). 7.1 Introduction. 7.2 SIMO Radar Modeling and Processing. 7.3 Slow-Time MIMO Radar Modeling. 7.4 Slow-Time MIMO Radar Processing. 7.5 OTHr Propagation and Clutter Model. 7.6 Simulations Examples. 7.7 Conclusion. Acknowledgment. References. 8 MIMO as a Distributed Radar System (H. D. Griffiths, C. J. Baker, P. F. Sammartino, and M. Rangaswamy). 8.1 Introduction. 8.2 Systems. 8.3 Performance. 8.4 Conclusions. Acknowledgment. References. 9 Concepts and Applications of A MIMO Radar System with Widely Separated Antennas (Hana Godrich, Alexander M. Haimovich, and Rick S. Blum). 9.1 Background. 9.2 MIMO Radar Concept. 9.3 NonCoherent MIMO Radar Applications. 9.4 Coherent MIMO Radar Applications. 9.5 Chapter Summary. Appendix 9A Deriving the FIM. Appendix 9B Deriving the CRLB on the Location Estimate Error. Appendix 9C MLE of Time Delays - Error Statistics. Appendix 9D Deriving the Lowest GDOP for Special Cases. Acknowledgments. References. 10 SpaceTime Coding for MIMO Radar (Antonio De Maio and Marco Lops). 10.1 Introduction. 10.2 System Model. 10.3 Detection In MIMO Radars. 10.4 Spacetime Code Design. 10.5 The Interplay Between STC and Detection Performance. 10.6 Numerical Results. 10.7 Adaptive Implementation. 10.8 Conclusions. Acknowledgment. References. INDEX.

A novel application of time-modulated arrays to multiple-input-multiple-output (MIMO) radar is presented and studied. The proposed scheme is to partition the transmit antenna of MIMO radar to a number of time-modulated subarrays that are overlapped. A series of orthogonal waveforms are emitted by these time-modulated subarrays with the unidirectional phase center motion time scheme. The resulting system can be referred to as subarrayed time-modulated MIMO (STM-MIMO) radar. As the tradeoff of conventional phased-array and MIMO radars, the STM-MIMO radar can obtain the coherent processing gain and waveform diversity gain simultaneously. The performance in terms of output signal-to-interference-plus-noise ratio and transmit/receive beampattern for the STM-MIMO radar is highlighted and compared to that of phased-array and MIMO radars. Simulation results validate the effectiveness of the proposed STM-MIMO radar technique.

Phased multiple-input-multiple-output (MIMO) radar has been proposed in the recent years to exploit the coherent processing gain of phased array radar and the waveform diversity of the MIMO radar. The transmit array is divided into subarrays of equal length to show some improvements as compared to both phased array and MIMO radar alone. In this letter, a hybrid phased MIMO radar with unequal subarrays has been proposed to steer multiple beams of different gain and unique waveforms towards a particular target. The unequal size and greater length of subarrays give an extra degree of freedom by illuminating the small targets with more focused beams. As a result, the echoes received by the receiver provide better information about the target. The effectiveness of proposed scheme is shown through simulation results.

Conventional multiple-input and multiple-output (MIMO) radar with frequency diverse array (FDA) has capability of resolving range ambiguity, however, it lacks range resolution of targets within an identical range cell. Considering this case, the FDA-MIMO radar employing the large frequency offset and inter-pulse coding modulation scheme, which is named the enhanced FDA-MIMO radar in this paper, is explored to resolve range ambiguity and estimate target parameters. Large frequency offset causes the signals of different antennas to occupy different frequency bands. Besides, the orthogonal polyphase codes among pulses are utilized to distinguish range ambiguous targets. Therefore, the proposed framework is advantageous in separating range ambiguity echo and realizing high resolution of target. To further resolve the targets within a range cell, a two-stage estimation method for angle and the principal range difference with the maximum likelihood estimator (MLE) is proposed. Finally, we firstly study the Ziv-Zakai bound (ZZB) and the Cramér-Rao bound (CRB) for the enhanced FDA-MIMO radar, which are performed to assess the estimation performance of the proposed method. Extensive simulations are implemented to demonstrate that the enhanced FDA-MIMO radar is superious in terms of range resolution and range ambiguity resolution.

Frequency diverse array (FDA)-multiple-input and multiple-output (MIMO) radar can form angle-distance 2D beam pattern, which has good anti-jamming ability. This paper proposes a towed jamming suppression method based on FDA-MIMO radar. The minimum variance spectral estimation (MVSE) algorithm is used to accurately locate the target and the jamming location, and then the target and the jamming are identified based on the difference in Doppler frequency. Finally, the minimum variance distortionless response (MVDR) adaptive beamforming algorithm suppresses jamming. In addition, this paper selects the square frequency increment instead of the commonly used linear frequency increment, so that the beam has a better range resolution to obtain a better anti-jamming effect. The simulation results prove the effectiveness of the method proposed in this paper.

Objective: Advancement in radar technology is moved from single antenna based system to multiple antenna system. From the viewpoint of the radar performance improvement, analysis of low Radar Cross Section (RCS) target detectability in rich scattering environment is utmost important. Methods: From the perspective of the advancement of the radar signal processing, phased array radar and MIMO radar have been already explored. This paper deals with the phased-MIMO radar, exploiting coherence signal processing gain and waveform diversity. Joint combination of coherency and diversity will improve the radar performance. Improvements: This paper deals with the analysis on the trade-off between the coherency and spatial diversity. The superiority of phased-MIMO radar, over other two systems, has been presented with respect to some selected parameters as in this paper.

To meet the urgent demand of low-observable moving target detection in complex environments, a novel method of Frequency Diverse Array (FDA) radar signal processing method based on Space-Rang-Doppler Focusing (SRDF) is proposed in this paper. The current development status of the FDA radar, the design of the array structure, beamforming, and joint estimation of distance and angle are systematically reviewed. The extra degrees of freedom provided by FDA radar are fully utilizsed, which include the Degrees Of Freedom (DOFs) of the transmitted waveform, the location of array elements, correlation of beam azimuth and distance, and the long dwell time, which are also the DOFs in joint spatial (angle), distance, and frequency (Doppler) dimensions. Simulation results show that the proposed method has the potential of improving target detection and parameter estimation for weak moving targets in complex environments and has broad application prospects in clutter and interference suppression, moving target refinement, etc..

In recent years, the frequency diverse array (FDA) radar has attracted much attention due to its range-angle dependent beam-pattern. And multiple input multiple output (MIMO) radar has been widely investigated with the advantage of transmit waveform diversity. In this paper, a FDA-MIMO Inverse Synthetic Aperture Radar (ISAR) is proposed to achieve anti-jamming of ISAR imaging. LFM (Linear Frequency Modulated) and SFCS (Stepped-Frequency Chirp Signal) signals are transmitted to jammer and targets respectively, and the received signals are matched filtering by the signal transmitted to the targets. So the jamming signal can't matched with the reference signal, the well anti-jamming effect can be obtained. The performance of the proposed method is evaluated by the simulation results.

Radar parameter estimation in terms of its range, angle and velocity plays a crucial role in many applications. Multiple-input multiple-output (MIMO) radar with frequency diverse array (FDA) is capable of resolving targets in one directional beam with different ranges and velocities (Doppler shifts). Prevalent methods obtain target parameters in a sequential manner to get rid of exhausted multi-dimensional search, but they suffer from accumulated estimation errors. To tackle this issue, a tensor generalized weighted linear predictor (TGWLP) is devised for FDA-MIMO radar parameter estimation, where the parameters are estimated in a parallel manner. Tensor modeling of multidimensional FDA-MIMO radar signal is developed, so that the joint parameter estimation is casted into multiple-pulse-group version of three-dimensional (3D) HR problem associated to the mixed Swerling model. Pulse-group diversity is exploited to obtain precise velocity estimation. In the presence of targets with some identical parameters, the final estimations of unambiguous slant range, conic angle, and radial velocity of a moving target can be easily obtained after the parallel frequency estimations. Besides, all the parameter pairing is automatically achieved, which is free of the extra burden from pairing process. Furthermore, the identifiability of the proposed joint estimator is analyzed. Finally, theoretical analysis and simulations are included to demonstrate that the proposed approach can achieve improved performance compared to the existing methods.

Multiple-input multiple-output (MIMO) radar enjoys the advantage of increased degrees-of-freedom and spatial diversity gain, but it cannot effectively resolves the targets closely spaced in the same angle cell (but different range cells). Frequency diverse array (FDA)-MIMO radar can handle this problem by exploiting its range-dependent beampattern. FDA-MIMO radar was, thus, suggested for range–angle estimation of targets. Nevertheless, it is necessary to provide theoretical performance analysis for such a relatively new radar technique. Since multiple signal classification (MUSIC) algorithm is widely adopted in most of the FDA-MIMO literature, this paper derives the Cramer–Rao lower bound and mean square error expressions in MUSIC-based range–angle estimation algorithms for a general FDA-MIMO radar. Furthermore, the corresponding range and angle resolution thresholds in target detection and localization are also derived. Numerical results verify that the FDA-MIMO indeed outperforms conventional MIMO radar in both range–angle estimation and resolution threshold performance.

Different from traditional phased array radar, frequency diverse array (FDA) radar uses a small frequency increment across the array elements; thus, the beampattern of FDA can be performed in both range and angle domains. Nevertheless, the transmit beampattern of conventional FDA is coupled in range and angle domains, which is not suitable for target detection. In this paper, we propose a multi-carrier FDA framework to generate the range-angle-decoupled beampattern. To focus the transmit energy at the desired position and suppress the interference at the undesired position, we then propose a beampattern synthesis approach based on Lagrange programming neural network, which can be implemented by hardware easily. Numerical simulation results demonstrate the effectiveness of the proposed method.