An adaptive coding-angle-Doppler clutter suppression approach with extended azimuth phase coding array

Utilizing digital beam forming (DBF) on transmit and receive, the multiple-input multiple-output (MIMO) radar can be applied to ground moving target indication (GMTI) and can provide an efficient way to improve the GMTI performance. The operating pattern is very significant for the MIMO radar GMTI. For the traditional GMTI pattern, there is a conflict of the wider swath and narrower clutter bandwidth. The observation area can be broadened with MIMO techniques by scanning. This paper designs an effective pattern with the simultaneous transmission of multi-phase-centre multi-sub-band signals as well as beam scanning in each pulse, which provide an efficient way to settle the conflict. By beam scanning in pulse, the new pattern has wider azimuth swath, shorter revisiting period, larger coherent processing interval (CPI) number and higher SCNR. By MIMO technology, more equivalent phase centres and longer efficient baselines can be formed, so the performance of GMTI can be improved. The principle of space-time-frequency adaptive processing (STFAP) is analysed, simulation results are presented to prove the superiority of MIMO radar in clutter suppression and GMTI. Analysis of the results indicates improved detection performance with the new pattern compared to the conventional approach.

Traditional space-time (ST) adaptive processing (STAP) theory is based on the assumption of narrowband or “zero-bandwidth,” where the decorrelation within the ST snapshot is ignored. However, with radar bandwidths increasing, this assumption becomes invalid due to the deteriorated decorrelation of the received signals within the ST snapshot. The decorrelation directly causes the dispersion of the received signals in both spatial and temporal domains, leading to the spreading of the clutter spectrum in the 2-D frequency (Doppler-spatial frequency) domain. With the spreading of the clutter spectrum, the clutter suppression notch in the traditional STAP filters is widened, resulting in a relative poor ability to detect slow-moving targets. In this article, we focus on the clutter suppression for wideband radar STAP. A generalized signal model of the ground clutter is first established for the wideband array radar. Using this outcome, we analyze the influence of bandwidth on the characteristics of the ground clutter and quantitatively describe the 2-D spreading of the ground clutter on the Doppler-spatial frequency plane. Moreover, the model of clutter covariance matrix for wideband STAP (W-STAP) is established. Finally, a 2-D keystone transform (KT) algorithm, referred to as ST KT (ST-KT), is proposed to eliminate the spreading of the ground clutter in the 2-D frequency domain caused by increasing bandwidths. Simulation results are employed to validate the theoretical analysis and verify the overperformance of the ST-KT based W-STAP method in terms of the output signal-to-clutter-plus-noise ratio (SCNR) of moving targets.

Consider a multiple input multiple output (MIMO) system with pulse amplitude modulation (PAM), where each transmit antenna is allowed to choose a different transmission delay. (We refer to this system as a MIMO system with asynchronous PAM.) Such MIMO systems have been studied before for increasing diversity and avoiding unintentional beamforming. We quantify the capacity/outage capacity improvement due to asynchronism and the corresponding spectral leakage (if any) for MIMO point-to-point and MIMO broadcast channels. We show that under commonly used transmitter spectral mask constraints, the channel has unbounded degrees of freedom. Synchronous PAM exploits only a finite number of these degrees of freedom, and asynchronism helps to improve capacity. For most cases of interest, two distinct shifts attain most of the capacity improvement. These gains are robust to interference from adjacent carriers. Analysis of the spectrum of the asynchronous PAM suggests that optimal water-filling based schemes are useful for improving capacity only in directional links due to possible spectral leakage, while uniform power allocation leads to capacity improvement without any extra spectral leakage.

In the work presented here we explore the interest of target localization using multiple-input multiple-output (MIMO) radar systems with widely separated antennas. The time delays between every transmit/receive antenna are an integral part of target localization using MIMO radar systems. We assume that these time delays have already been estimated by a preprocessing algorithm. Accordingly, we propose a target localization algorithm using the least squares estimation which has a closed form without requiring any initial condition. Simulation results evaluate the performance of the proposed method in comparison with the Cramer-Rao lower bound (CRLB) and the best linear unbiased estimator (BLUE) method. The effect of the arrangement and number of transmitters and receivers on the algorithm performance is also investigated.

This chapter is attempted to provide a survey of the advanced concepts and related issues involved in Multiple Input Multiple Output (MIMO) systems. MIMO system technology has been considered as a really significant foundation on which to build the next and future generations of wireless networks. The chapter addresses advanced MIMO techniques such as polarization diversity and antenna selection. We gradually provide an overview of the MIMO features from basic to more advanced topics. The first sections of this chapter start by introducing the key aspects of theMIMO theory. TheMIMO systemmodel is first presented in a genericway. Then, we proceed to describe diversity schemes used in MIMO systems. MIMO technology could exploit several diversity techniques beyond the spatial diversity. These techniques essentially cover frequency diversity, time diversity and polarization diversity. We further provide the reader with a geometrically based models for MIMO systems. The virtue of this channel modeling is to adopt realisticmethods for modeling the spatio-temporal channel statistics from a physical wave-propagation viewpoint. Two classes for MIMO channel modeling will be described. These models involve the Geometry-based Stochastic ChannelModels (GSCM) and the Stochastic channel models. Besides the listedMIMO channel models already described, we derive and discuss capacity formulas for transmission over MIMO systems. The achieved MIMO capacities highlight the potential of spatial diversity for improving the spectral efficiency of MIMO channels. When Channel State Information (CSI) is available at both ends of the transmission link, the MIMO system capacity is optimally derived by using adaptive power allocation based on water-filling technique. The chapter continues by examining the combining techniques for multiple antenna systems. Combining techniques are motivated for MIMO systems since they enable the signal to noise ratio (SNR) maximization at the combiner output. The fundamental combing techniques are the Maximal Ratio Combining (MRC), the Selection Combining (SC) and the Equal Gain Combining(EGC). Once the combining techniques are analyzed, the reader is introduced to the beamforming processing as an optimal strategy for combining. The use of multiple antennas significantly improves the channel spectral efficiency. Nevertheless, this induces higher system complexity of the communication system and the communication system performance is effected due to correlation between antennas that need to be deployed at the same terminal. As such, the antenna selection algorithm for MIMO systems is presented. To elaborate on this point, we introduce Space time coding techniques for MIMO systems and we evaluate by simulation the performance of the communication system. Next, we emphasis on multi polarization techniques for MIMO systems. As a background, we presume that the reader has a thorough understanding of antenna theory. We recall the basic antenna theory and concepts that are used throughout the rest of the chapter. We rigorously introduce the 3D channel model over the Non-Line of Sight (NLOS) propagation channel for MIMO system with polarized antennas. We treat the depolarization phenomena and we study its effect on MIMO system capacity. The last section of the chapter provides a scenario for collaborative sensor nodes performing distributed MIMO system model which is devoted to sensor node localization in Wireless Sensor Networks. The localization algorithm is based on beamforming processing and was tested by simulation. Our chapter provides the reader by simulation examples for almost all the topics that have been treated for MIMO system development and key issues affecting achieved performance.

We address the problem of training data corruption in space-time adaptive processing (STAP) for ground moving target indication (GMTI) radar scenarios characterized by high densities of ground targets. A site-specific clutter simulation is used to demonstrate the impact that target signals in the training data have on STAP performance. Measured MCARM data results are presented that reveal similar performance trends as those observed in the simulations. A strategy for mitigating the deleterious effects of targets in the training data using a priori knowledge of the radar environment (e.g., locations of roads) to edit the training data is presented.

The ground moving-target indication (GMTI) technique can detect civil and military moving targets, which means that this technique has received much attention. Strong clutter background suppression is one of the critical problems in this application. However, the detection performance in heterogeneous environment can be degraded due to the inaccurate estimation of the clutter covariance matrix (CCM). In this paper, we propose a robust GMTI method using a spaceborne dual-platform synthetic aperture radar (SAR) system, which can obtain highly accurate CCM in nonuniform clutter. Firstly, the accurate CCM is estimated based on the SAR image obtained by the former platform. Then, space–time adaptive processing (STAP) is carried out using the obtained the CCM. Finally, the detection threshold is set according to the estimated CCM and detection is executed accurately. Compared with the traditional CCM estimation method in STAP using the clutter nearby the cell under test, this method directly estimates the CCM using the clutter of the cell under test, which can avoid CCM estimation mistakes in heterogeneous clutter environment. The clutter can be whitened and depressed more effectively. Additionally, with the accurate threshold acquired from the CCM, the detection probability can be effectively improved under a certain false-alarm criterion. Based on simulation data, GMTI experiments in a heterogeneous environment such as clutter with strong pollution, junction zone of hot and cold clutter, and clutter with nonuniform power are carried out; the results show that the moving targets can be effectively detected with the proposed method.

Synchronization across multiple distributed apertures is the standard assumption in proposed multiple-input multiple-output (MIMO) radar systems. However, perfect phase synchronization is difficult to realize. Assuming frequency synchronization, probably through a GPS system or reception of a beacon, we address the issue of unknown LO phases of local oscillators in a MIMO radar system, and consider the problem of joint estimation of target reflectivity and LO phases. The bistatic target reflectivity is subject to a complex normal prior distribution given by a multiple point scatterer model and LO phases are assumed independent and identically distributed random variables. © 2010 IEEE.

Multiple-input multiple-output synthetic aperture radar (MIMO SAR) system has drawn considerable attention for its wider detected swath without decreasing PRF compared to the traditional multichannel synthetic aperture radar (MC-SAR) system. For MIMO SAR system, how to extract the matched signal or suppress the unmatched interferences is the foremost task. Using the OFDM chirp signals as the transmitted signals, the robust principal component analysis (RPCA) based method can be employed for ground moving target indication (GMTI) with no need for separating the matched signal and unmatched interferences. The simulation demonstrates the effectiveness of RPCA based method for GMTI in MIMO SAR system.

Orbital Angular Momentum (OAM) has been considered as a new dimension in wireless transmission. In Multiple Input Multiple Output (MIMO) system, OAM can also be utilized as the new Degrees of Freedom (DoF) to manipulate the beamforming instead of the traditional Channel State Information (CSI) feedback, so that the computation complexity of the beamforming dramatically decreases, as well as the capacity of the transmission is maintained approaching the upper bound of the MIMO system. For example, this beamforming manipulation with OAM is extremely efficient in the Line of Sight (LoS) MIMO system. In this paper, the mathematical model of the DoF in beamforming manipulation in MIMO with OAM is established. The computation complexity is evaluated, and compared in LoS environment within MIMO, MIMO-OAM, and OAM. The quantitatively simulation confirms the fact that MIMO-OAM has the best trade-off between the capacity and the computation complexity, which gives a consolidated evidence that the MIMOOAM scheme can be adopted as an efficient beamforming scheme in LoS backhaul links between the base station and the self backhaul small cell station, or Near Field Communications (NFC) directly between mobile terminals. Due to these promising applications, MIMO-OAM may become one of the candidate technologies in Beyond 5G (B5G) mobile communications.

In conventional ways of fast responding and high performing model predictive control using Field Programmable Gate Arrays (FPGA), the approaches developed are applicable only to Single Input Single Output System (SISO). Many real time systems are Multiple Input Multiple Output (MIMO) systems and loop interaction is a major concern and has to be reduced in control of MIMO systems. Also, direct digital control with ability to solve the Quadratic Programming (QP) problem online, which can be applied to complex systems with fast response time and/or embedded applications and with limited computational resource for supervisory control approach, is required in many complex industrial MIMO systems. A novel direct digital control of Model predictive controller with FPGA for a MIMO system which considers and reduces loop interaction and which can also be implemented with very less investment is proposed here. The system was modeled from first principles and then simulated in Labview environment for MIMO system. Implementation of the system and response was verified for a stirred tank system. The proposed system has good performance in terms of reduction in loop interaction which is a major concern in implementation of controller for MIMO systems. Also, direct digital control of MIMO system instead of supervisory control was accomplished.

Multidimensional waveform encoding is a new multiple-input multiple-output (MIMO) technique for remote sensing. The orthogonality is acquired by digital beamforming in elevation. It has a potential to enhance ground moving target indication (GMTI) performance due to the larger along-track virtual array of the radar system. This paper proposed two modes for GMTI based on multidimensional waveform encoding: intrapulse azimuth beam scanning and intrapulse azimuth encoding. Compared to the phased-array GMTI systems, the mode with intrapulse azimuth beam scanning has improved area search rates, and the mode with intrapulse azimuth encoding has improved minimal detectable velocity performance. Theoretical analysis and simulation results demonstrate that the proposed modes have better performance than the phased-array GMTI systems.

In high-frequency surface wave radar (HFSWR) systems, clutter is a common phenomenon that causes objects to be submerged. Space-time adaptive processing (STAP), which uses two-dimensional data to increase the degrees of freedom, has recently become a crucial tool for clutter suppression in advanced HFSWR systems. However, in STAP, the pattern is distorted if a clutter component is contained in the main lobe, which leads to errors in estimating the target angle and Doppler frequency. To solve the main-lobe distortion problem, this study developed a clutter-suppression method based on beam reshaping (BR). In this method, clutter components were estimated and maximally suppressed in the side lobe while ensuring that the main lobe remained intact. The results of the proposed algorithm were evaluated by comparison with those of standard STAP and sparse-representation STAP (SR-STAP). Among the tested algorithms, the proposed BR algorithm had the best suppression performance and the most accurate main-lobe peak response, thereby preserving the target angle and Doppler frequency information. The BR algorithm can assist with target detection and tracking despite a background with ionospheric clutter.

Design of a mixed fuzzy controller for multiple-input multiple-output systems

Traditional space-time adaptive processing (STAP) is a strategy for clutter suppression in airborne radar, which requires a large number of computational complexity and secondary data. In order to address the problem, reduced-dimension (RD) STAP is generally used. We propose a novel RD STAP through searching the best channels as the auxiliary channels to cancel the interference. Based on the estimation of the clutter Fourier basis vectors offline, a parameter named angle-Doppler correlation coefficient (ADC 2) is constructed to evaluate the capability of each auxiliary channel in clutter suppression, and the best sets of RD channels can be selected. The proposed algorithm can achieve the best detection performance with the fixed number of auxiliary channel. When the degrees of freedom (DOF) are restricted to a small value, only one auxiliary channel is needed to guarantee the SINR loss less than 3 dB. Therefore, the requirement of the training sample can be reduced, which makes the proposed approach more suitable for the heterogeneous clutter environments.