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This paper deals with the problem of adaptive moving target detection, embedded in homogeneous Gaussian noise with unknown covariance matrix, for frequency diverse array multiple-input multiple-output (FDA-MIMO) radar operating in interference-dominant environment. Unlike traditional adaptive moving target detectors that need training data to estimate the jamming covariance matrix (JCM), we present the Rao and Wald test based adaptive detector, which requires no training data. Furthermore, we propose a two-stage approach to obtain maximum likelihood estimate (MLE) of the joint range, angle and Doppler, respectively. The corresponding signal-to-jamming-plus-noise ratio (SJNR) is derived to evaluate the FDA-MIMO radar performance. Simulation results show that the proposed detector outperforms the generalized likelihood ratio test (GLRT).

In this paper, we focus on the tradeoff design between communication and radar detection for a joint communication and radar (CommRadar) system by leveraging a low complexity hardware architecture. To achieve this, we exploit the communication traffic diversity to design an adaptive scheduling policy. Through adjusting the CommRadar mode selection, radar steering direction and communication user scheduling, and time allocation between communication and radar detection, we could better utilize the un-occupied transmission time to improve radar detection performance when the data traffic is light, and balance the time allocation between communication and radar detection when the data traffic is heavy. Moreover, for our adaptive scheduling method, we have provided a quantitative performance bound for data throughput, queueing delay, and radar detection performance of both short-term and long-term. By conducting extensive simulation studies, we have demonstrated that our method could greatly reduce the communication performance loss when integrating the radar functionality, while still guaranteeing radar detection performance.

Radar detection and communication are two of several sub-tasks essential for the operation of next-generation autonomous vehicles (AVs). The former is required for sensing and perception, more frequently so under various unfavorable environmental conditions such as heavy precipitation; the latter is needed to transmit time-critical data. Forthcoming proliferation of faster 5G networks utilizing mmWave is likely to lead to interference with automotive radar sensors, which has led to a body of research on the development of Joint Radar Communication (JRC) systems and solutions. This paper considers the problem of time-sharing for JRC, with the additional simultaneous objective of minimizing the average age of information (AoI) transmitted by a JRC-equipped AV. We first formulate the problem as a Markov Decision Process (MDP). We then propose a more general multi-agent system, with an appropriate medium access control protocol (MAC), which is formulated as a partially observed Markov game (POMG). To solve the POMG, we propose a multi-agent extension of the Proximal Policy Optimization (PPO) algorithm, along with algorithmic features to enhance learning from raw observations. Simulations are run with a range of environmental parameters to mimic variations in real-world operation. The results show that the chosen deep reinforcement learning methods allow the agents to obtain good results with minimal a priori knowledge about the environment.

Radar-based human activity recognition (HAR) finds various applications like assisted living and driver behavior monitoring. As radar data are heavily environment-dependent, it is becoming increasingly important to develop a transfer learning mechanism that enables a radar-based HAR system with desirable cross-environment adaptation feasibility. This paper concerns the issue of how radar-based HAR system can adapt to a new environment without source data. To this end, we devote to using the source hypothesis transfer learning architecture to build such an environment adaptation mechanism towards cross-environment radar-based HAR. In doing this, it is a challenging task to develop a reliable self-supervised labeling strategy for generating pseudo labels associated with the unlabeled target data, which is crucial to facilitate the learning of a target-specific feature extractor being responsible for environment adaptation. This paper presents the neighbor-aggregating-based labeling method and incorporates it with the existing clustering-based labeling method to perform the self-supervised labeling task. The logic behind our approach is that the above two labeling methods are complementary to each other in terms of making use of both local and global structures of adaptation data to supervise the labeling task. The coordination of both labeling methods is motivated to be implemented in the weighted combination form, which contributes to improving the reliability of generated labels. Experimental results on a public HAR dataset based on the frequency modulated continuous wave (FMCW) radar demonstrate the effectiveness of our approach.

In coherent radar system, maneuvering target with complex motions usually confronts the issues of range cell migration (RCM) and Doppler frequency spread (DFS) within the long-time coherent integration process. To address these issues and achieve reliable detection and estimation performance, this paper proposes a novel fast approach without motion parameters searching. Particularly, the proposed approach performs the third-order keystone transform (TKT) to eliminate the third-order RCM (TRCM). Then, the bilinear parametric symmetric self-correlation function (BPSSF), scaled Fourier transform (SCFT) and inverse Fourier transform (IFT) are successively operated to estimate target's velocity and acceleration. Afterwards, the improved second-order phase differentiation (ISPD) is performed to estimate target's jerk. Utilizing the estimated parameters, the compensation functions could be constructed to eliminate the remaining RCMs and DFSs as well as to obtain the energy coherent integration. By contrast, analyses of several classical approaches and the proposed one are provided concerning computational complexity, coherent integration performance, detection performance and estimation performance, which confirm that the proposed approach can balance the computational burden and the detection/estimation capabilities. Additionally, the proposed approach can avoid the effect of blind speed side-lobe (BSSL). Eventually, simulated data verification and raw data processing are conducted to evaluate the efficacy of the presented approach.

This paper deals with the use of Reconfigurable Intelligent Surfaces (RISs) for radar surveillance in Non-Line Of Sight (N-LOS) scenarios. First of all, the geometry of the scene and the new system concept is described with emphasis on the required operative modes and the role played by the RIS. Then, the specific radar equation (including the RIS effect) is developed to manage the coverage requirements in the challenging region where the LOS is not present. Both noise and clutter interference cases (pulse length-limited and beamwidth-limited surface clutter as well as volume clutter) are considered. Hence, a digression on the use of the radar timeline for the new operative mode is presented together with the data acquisition procedure and the resolution issues for the range, azimuth, and Doppler domains. Finally, the interplay among the system parameters and, in particular, those involving the RIS is discussed and analyzed via numerical simulations., Comment: typo corrected in the first equation on page 6

Modern millimeter-wave automotive radars are employed to keep a safe distance between vehicles and reduce the collision risk when driving. Meanwhile, an on-board radar module is supposed to operate in the line-of-sight condition, which limits its sensing capabilities in intersections with obstructed visibility. Therefore, this paper investigates the scheme with passive reflector, enabling the automotive radar to detect an approaching vehicle in the non-line-of-sight (blind) urban intersection. First, extensive radar measurements of the backscattering power are carried out with the in-house assembled millimeter-wave radar equipment. Next, the measured data is employed to calibrate an accurate analytical model, deduced and described in this paper. Finally, the analytical models are deployed to define the optimal parameters of the radar scheme in the particular geometry of the selected intersection scenario. Specifically, it is found that the optimal angular orientation of the reflector is 43.5, while the 20 m curvature radius shows better performance compared to a flat reflector. Specifically, the curved convex shape increases scattering power by 20 dB in the shadow region and, thus, improves the detection probability of the vehicle, approaching the blind intersection. publishedVersion

This paper proposes a new transmitter precoding approach for co-existence between multiple-input multiple-output (MIMO) radar and multi-user MIMO communication systems employing phase shift keying (PSK) modulation. To enable the satisfactions of radar communication functionalities, the common practice is to suppress the mutual interferences, while our proposed scheme is to use the known interferences existing already in radar and communications systems and make the most of them: selectively reverse the destructive interferences to the negative direction while retaining the interferences which are beneficial to the desired signal. The resulting interferences are always kept constructive and can contribute to the power of the useful signal. Numerical results validate that the effective signal to noise ratio (SNR) can be enhanced by 7 dB with no additional power-peruser investment.

Fast chirp FMCW MIMO radar with inter-chirp coding provides high emission power by utilizing simultaneous antennas transmissions. However, the coding produces high side-lobes in the Doppler and angle dimensions of the radar's ambiguity function. The high side-lobes may cause miss-detection due to masking between targets that are at similar range and have large received power difference, as is often the case in automotive scenarios. In this paper, we develop a novel code optimization method that attenuates the side-lobes of the radar's ambiguity function and hence improves the probability of detection in the case of multiple targets. We formulate an analytical expression for the ambiguity function side-lobes, which takes into account that the targets range difference migrates during the detection interval, and that the migration is proportional to the targets Doppler frequency difference. We then develop an optimization algorithm that finds the code that minimizes the ambiguity function side-lobes average energy. The optimized code attenuates the ambiguity function side-lobes at the Doppler frequencies that are near the main lobe frequency. These frequencies correspond to targets with small speed difference, for which the range difference between the targets does not change significantly during the detection interval, and hence overlapping in range can cause severe interference. We demonstrate by simulation that the optimized code achieves an improvement in the ambiguity function side-lobes attenuation and an improvement in the probability of detection compared to reference codes.

Millimeter-wave radars are being increasingly integrated into commercial vehicles to support advanced driver-assistance system features. A key shortcoming for present-day vehicular radar imaging is poor azimuth resolution (for side-looking operation) due to the form factor limits on antenna size and placement. In this paper, we propose a solution via a new multiple-input and multiple-output synthetic aperture radar (MIMO-SAR) imaging technique, that applies coherent SAR principles to vehicular MIMO radar to improve the side-view (angular) resolution. The proposed 2-stage hierarchical MIMO-SAR processing workflow drastically reduces the computation load while preserving image resolution. To enable coherent processing over the synthetic aperture, we integrate a radar odometry algorithm that estimates the trajectory of ego-radar. The MIMO-SAR algorithm is validated by both simulations and real experiment data collected by a vehicle-mounted radar platform., 13 pages

Reliable trajectory prediction methods are critical in providing predictive safety intelligence for vital decision making in intelligent transportation systems to further enhance the safety of drivers and passengers. To tackle complicated maneuvering and interactions between objects, learning-based algorithms were used to replace classic model-based trajectory prediction algorithms. However, most algorithms implicitly presume that they are executed at centralized processing units after gathering data from edge sensors and delivering the results back to the on-board units. This causes an increase in computation time and latency; thus, reducing the reaction time of the drivers. To reduce the computation time and latency and consider the robustness of local sensors, we propose a decentralized radar-dedicated framework with a deep-learning (DL) model, called predictive RadarNet, to predict future trajectories over binary range angle (RA) maps with a probabilistic representation according to the original radar RA maps for presenting the uncertainty of the estimated trajectories. In addition, to reduce the model size for low-complexity, we designed a prepossessing technique that can largely reduce the size of the input tensors without losing information. Moreover, we found that the functions of the DL-model consist of two operations: future inference of original radar RA maps and transformation to binary RA maps. Thus, we designed two models with different kernels that are suitable for dealing with the two operations. Simulations show that the proposed decentralized framework using predictive RadarNet can provide reliable prediction results with a low computation time.

Sparse recovery (SR) is a promising tool for radar signal processing. However, SR-based target detection is still an open problem in the challenging scenarios of grid mismatch, very high dimensionality and constant false-alarm rate (CFAR) requirement. To address the above challenges, an efficient approach termed as knowledge-aided generalized approximate message passing (KA-GAMP) is proposed. Firstly, traditional signal processing (TSP) is performed to obtain prior knowledge about targets of interest. Based on this prior knowledge, dimensionality reduction is carried out, and a new approximate observation model of the received signal is established. Then, considering the grid-mismatch problem, target parameter estimations are carried out before SR, and an estimate of the measurement matrix is obtained. Finally, by exploiting the sparsity of the received signal, GAMP is adopted to perform target recovery. Based on recovery results, target detection is implemented. Interestingly, it is shown that the noise envelope outputted by GAMP approximately follows an i.i.d Gaussian distribution, and the proposed detector is CFAR. Numerical results via both Monte Carlo simulations and the measured data show that the proposed approach is superior to the TSP-based method in terms of target detection performance.

Dual-functional radar-communication (DFRC) technique has been viewed as a promising component in the emerging platforms. When synthesizing the desired transmit beampattern, the degrees of freedom of waveform design is limited, which introduces high multi-user interference (MUI), thus degrading the communication performance. Inspired by the applications of the Reconfigurable Intelligent Surface (RIS) in mitigating MUI, in this paper, we investigate joint waveform design and passive beamforming in RIS-assisted DFRC system. We first study the minimization of MUI under the strict beampattern constraint by jointly optimizing DFRC waveform and RIS phase shift matrix. To deal with the coupled variables, we propose an alternating algorithm based on manifold optimization. Subsequently, the trade-off between radar and communication performances is investigated. Simulation results show that for both cases of strict beampattern and trade-off design, with the help of RIS, the system throughput can be significantly improved. Moreover, compared with the scenario where no RIS is employed, the obtained beampattern matches with the target transmit beampattern better.

Unified waveform design for automotive joint radar-communications (JRC) leverages the scarce spectrum efficiently and has become a key topic for investigation of late. Designing such a waveform necessitates meeting the requirements of both systems, thereby making it a challenging task. The contribution of this paper is to formulate the JRC design problem into an optimization problem and propose an algorithm to maximize the signal-to-clutter-plus-noise-ratio (SCNR) of radar system and signal-to-noise-ratio (SNR) at communicating vehicle, simultaneously. Central to this are the exploitation of the communication link to acquire environment/ channel information and enhance radar tasks, flexibility to impart trade-off between the two systems during design as well the formulation of the optimization problem to include sidelobe constraints and yield solutions robust to Doppler shifts. The designed waveforms exhibit enhanced radar performance in terms of probability of detection and communication performance in terms of bit error rate (BER), while taking into account the trade-off between two systems. The numerical simulations corroborate the claim of optimized performance with environment/ channel information, ease of effecting trade-off and the use of design flexibility.

This paper studies the spectrum allocation problem between spectrum service providers (SSPs) and terminals equipped with orthogonal frequency division multiplexing (OFDM) integrated radar and communication (IRC) systems. In particular, IRC-equipped terminals such as autonomous vehicles need to buy spectrum for their radar functions, e.g., sensing and detecting distant vehicles, and communication functions, e.g., transmitting sensing data to road-side units. The terminals determine their spectrum demands from the SSPs subject to their IRC performance requirements, while the SSPs compete with each other on the service prices to attract terminals. Taking into account the complicated interactions, a hierarchical Stackelberg game is proposed to reconcile the spectrum demand and service price, where the SSPs are the leaders and the terminals are the followers. Due to the spectrum constraints of the SSPs, we model the lower-layer subgame among the terminals as a generalized Nash equilibrium problem. An iterative searching algorithm is then developed that guarantees the convergence to the Stackelberg equilibrium. Numerical results demonstrate the effectiveness of our proposed scheme in terms of social welfare compared to baseline schemes.

In this paper, we consider the multi-frame integration (including intra-frame integration and inter-frame integration) problem for the detection of weak moving targets, where range walk effect (RWE) occurs within the integration time. A novel integration method is proposed to achieve the accumulation of a target's multi-frame radar echoes. More specifically, the proposed integration method contains two main steps, where the modified Radon Fourier transform (RFT) is firstly used to realize the intra-frame coherent integration for every frame of echo signals. After that, the RFT-domain integration algorithm is presented to achieve the inter-frame coherent integration based on the characteristics of RFT outputs, during which the accumulation of target energy distributed in multiple frames could be realized. The explicit expressions and analysis for various performance measures of the proposed method, such as integration output response, probability of detection, input-output signal-to-noise ratio (SNR), and blind speed sidelobe (BSSL) response are derived and given. Simulation experiments are given to demonstrate the effectiveness of the proposed method.

This paper deals with the multiple targets localization problem via multi-channel ultra-wideband (UWB) imaging radar non-line-of-sight (NLOS) signal processing. A novel matching-based radar imaging algorithm is proposed to obtain the positions of multiple targets in the L-shaped corner scenario with complex multipath ghost signals. Firstly, a multipath propagation model for the multiple targets scenario is established. Then the positions of the actual multipath ghosts are extracted from the radar image, and the candidate targets corresponding to these multipath ghosts are derived. Secondly, the ellipse-cross-localization method is proposed to obtain the positions of the candidate multipath ghosts, followed by two defined matching factors to measure the similarity between actual and candidate multipath ghosts. According to the similarity, decision rules are designed to determine the actual targets. Compared with the localization algorithm based on one-dimensional range profile, the proposed algorithm can effectively cope with the cases of multiple targets, even in the cases of rough walls and noise. Finally, simulations and experimental data are used to validate the effectiveness of the proposed algorithm.

Generally ground clutter has serious range dependence and range ambiguity in the forward-looking array airborne radar system under a high pulse repetition frequency (PRF) system. In this work, a range ambiguous clutter suppression method based on transmitting beam main lobe correction for the FDA MIMO radar is proposed. Firstly, this method uses the designed transmission pulse weights to compensate for the initial phases formed by the accumulation of frequency increments between pulses, so as to solve the problem that the main lobe high gain illuminated angle is inconsistent between the MIMO equivalent transmitting beampattern pulses synthesized at the receiving in the traditional FDA MIMO system. Secondly, by restoring and compensating received echoes, the influence of the main lobe correction weights of the transmitting signal is eliminated. Finally, through the characteristics of transmitter correction compensation to distinguish different range ambiguous clutters, the echoes from the range ambiguous areas can be discretely distributed in the power spectrum while preserving the signal echoes in the observation area. Thus range ambiguous clutter suppression is realized. The simulation results show that the proposed method can improve the clutter suppression performance compared with the MIMO and linear frequency increment FDA radar.

This paper considers the problem of high-speed maneuvering target detection, which involves the processing of complex range migration (RM) and Doppler frequency migration (DFM) within the coherent processing interval (CPI). By exploiting the coupling relationship between the range frequency and the Doppler frequency, a detection algorithm is proposed for high-speed maneuvering targets. After the range compression, approximate 2-D range-frequency and Doppler-frequency expressions are derived according to the principle of stationary phase (POSP) and series inversion extension. The mathematical range-Doppler frequency relationships are then exploited to search the target's first-order motion parameters and Doppler ambiguity numbers within a predetermined parameter range, and formulate the corresponding new data from the original 2-D data matrix. Finally, Fourier transform and constant false-alarm rate (CFAR) detector are further applied to detect the targets. Simulation results show that the proposed algorithm has low complexity requirements and is suitable for low signal-to-noise ratio (SNR) scenarios. Moreover, it is not sensitive to the motion order of targets.

Two-dimensional distributed source is a more realistic model to represent the target signal propagation in such fields as radar, sonar, and wireless communication. This paper presents a novel uniform circular sub-array (UCSA) configuration, which can be used to accurately estimate parameters of multiple two-dimensional incoherently distributed (TDID) sources. By sequentially utilizing the mapping relationship between the generalized array manifold, which has the spatial translation invariant relationship, and the signal subspace of TDID source, a closed-form solution of four parameters (central and spread parameters within azimuth and elevation dimension) of every TDID source can be obtained with low computational complexity. Furthermore, by properly using the signal subspace, the estimation accuracy is improved significantly. Numerical simulations illustrate the properties of the proposed method and show that it can simultaneously improve the robustness with respect to the scattering size and uncertainty of the spatial distribution. The proposed method has the potential to be used for fast and accurate multi-TDID source localization with arrays of small aperture and few array elements.

A joint transceiver coexistence design of multiple-input multiple-output (MIMO) communication and MIMO radar is addressed under different practical waveform constraints. Without radar waveform constraints, the conventional optimal waveforms can result in large modulus variation and poor range resolution. To tackle these problems, the constant-modulus and/or similarity constraints are added in this work. The proposed constant-modulus waveform constraint is favoured by the cheap and efficient nonlinear power amplifier, and the proposed similarity constraint provides a flexible control on modulus variation, range resolution and transmit power, at the cost of a small extra computational complexity.

In this study, a dual-functional radar and communication (RadCom) system architecture is proposed for application at base-stations (BSs), or access points (APs), for simultaneously communicating with multiple user equipments (UEs) and sensing the environment. Specifically, massive multiple-input multiple-output (mMIMO) communication and orthogonal frequency-division multiplexing (OFDM)-based MIMO radar are considered with the objective to jointly utilize channel diversity and interference. The BS consists of a mMIMO antenna array, and radar transmit and receive antennas. Employing OFDM waveforms for the radar allows the BS to perform channel state information (CSI) estimation for the mMIMO and radar antennas simultaneously. The acquired CSI is then exploited to predict the radar signals received by the UEs. While the radar transmits an OFDM waveform for detecting possible targets in range, the communication system beamforms to the UEs by taking into account the predicted radar interference. To further enhance the capacity of the communication system, an optimum radar waveform is designed. Moreover, the network capacity is mathematically analyzed and verified by simulations. The results show that the proposed RadCom can achieve higher capacity than conventional mMIMO systems by utilizing the radar interference while simultaneously detecting targets.

To exploit the spectrum reuse potential and enhance the cooperative sensing ability of the cooperative sensing unmanned aerial vehicle network (CSUN), we propose a novel joint sensing-communication (JSC) CSUN that can simultaneously conduct downward-looking radar sensing and sensing data fusion communication with the unified spectrum and transceiver by adopting the beam sharing scheme. To achieve the beam sharing scheme, we design a novel antenna array equipped on each UAV, which can generate a sensing beam and a communication beam orthogonally for downward detection and data-fusion communication, respectively. To quantify the cooperative sensing performance of CSUN, we propose and formulate a novel upper-bound average cooperative sensing area (UB-ACSA) as the cooperative sensing performance metric, based on deriving the closed-form expressions for the average mutual sensing interference of CSUN and the communication capacity constraints. Finally, extensive simulations are conducted to validate the theoretical results. We also demonstrate that the cooperative sensing performance of the JSC CSUN can improve by 66.3 percent compared with the conventional CSUN in terms of UB-ACSA.

As electromagnetic environments in battlefields are more and more complex, there are more kinds of suppression jamming noise including both single jamming signals and compound jamming signals. Thus, suppression jamming recognition especially for compound jamming signals is becoming vital and challenging. Whereas conventional methods are prone to owning the low recognition accuracy and the high computational complexity, especially under low jamming-to-noise ratio (JNR) conditions. In this paper, a novel jamming recognition network (JRNet) based on robust power-spectrum features is proposed to recognize ten kinds of suppression jamming signals including four single jamming patterns and six compound jamming patterns. The proposed method combines the significant representative power of the JRNet and distinguished power-spectrum features of jamming signals to promote the recognition performance. By integrating residual blocks and asymmetric convolution blocks, the JRNet is capable to address the degradation problem and enhance the recognition ability for subtle features. The simulation results show that the overall recognition accuracy of the proposed method is more than 90% even when the JNR is −18 dB and is close to 100% at −6 dB. Compared with five comparison methods in recent literatures, the proposed JRNet achieves better and stable recognition performance especially under low JNR conditions with relatively less storage source and a bit more FLOPs and inference time.

High-resolution delay-Doppler estimation is an important requirement for automotive radar systems, especially in multi-target scenarios that require better target separation performance. Orthogonal frequency-division multiplexing (OFDM) is a promising candidate waveform for future intelligent transport networks, since it enables the integration of both radar and communication functionalities. Exploring the dual functionality enabled by OFDM, this paper presents a new cooperative method for high-resolution delay-Doppler estimation. The proposed subspace-based method exploits the combination of both the radar and received communication signals to estimate target parameters. The procedure achieves high-resolution delay-Doppler estimation for both uncorrelated, partially correlated and coherent signals, and enables a significant reduction in the required bandwidth when compared to previous approaches which do not exploit the knowledge of the communication signals. Laboratory measurements at 24 GHz and simulation results demonstrate the efficacy of the proposed method for the estimation of multiple targets.

In the intelligent transportation system, it is an efficient way for the intelligent vehicle to use the integrated radar and communications system (IRCS) to obtain the range and velocity estimations of other vehicles and simultaneously communicate with other facilities, such as vehicles and base stations. In the IRCS, the transmitted waveform is the orthogonal frequency division multiplexing (OFDM) integrated radar and communications waveform that contains communications information. Due to the existence of communications information, the traditional range and velocity estimation methods in radar can not be directly utilized in the IRCS. Moreover, to improve the resolution of range and velocity estimations, the signal bandwidth and coherent processing interval (CPI) are usually required to be increased, which will result in the increase of system cost and the reduction of update rate. To solve these problems, a auto-paired super-resolution range and velocity estimation method is proposed by using the OFDM integrated radar and communications waveform. First, the communications information in the received signals is compensated. Then, the frequency smoothing is performed to reduce the correlation between the echoes reflected by different targets. Finally, the auto-paired super-resolution range and velocity estimations are obtained by exploiting the translational invariance structure of the underlying signal model in both frequency and pulse domains. Furthermore, Cramer-Rao bounds (CRBs) on range and velocity estimations with the OFDM integrated radar and communications waveform are derived. Several numerical examples are presented to verify the effectiveness of the proposed method.

This paper proposes a pulse compression method by using the offset quadrature amplitude modulation based orthogonal frequency division multiplexing (OQAM-OFDM) signal without inserting any cyclic prefix (CP). A modified interference approximation-based pulse compression method (MIA-based PCM) is proposed, by exploiting the demodulated real weights of the OQAM-OFDM signal, the inter-symbol and inter-subcarrier interferences to enhance the performance. The MIA-based PCM achieves approximated inter-range-cell interference (IRCI)-free pulse compression and perfect Doppler estimation, which has both the benefits of power efficiency and good Doppler tolerance. Finally, numerical simulation results are provided and discussed.

In this paper, the problem that the polyphase signal may not satisfy the orthogonality requirement in the condition of limited bandwidth is investigated. The use of waveforms that lack orthogonality leads to possible harmful effects in multiple-input and multiple-output (MIMO) radar and distributed coherent aperture radar (DCAR). For the purpose of enhancing the orthogonality of the signal, a multi-pulse joint transmission strategy of polyphase signal and the corresponding echo signal processing algorithm based on sliding window are proposed. We further analyze the relationship between the degradation of detection performance and the step size of the sliding window. In addition, the Doppler tolerance of the signal is also derived. Finally, the enhancement of the orthogonality by using the multi-pulse joint processing strategy and the relationship between Doppler tolerance and coherent time are verified via numerical simulations.

This article considers the dual-use unimodular signal design for a novel dual-functional radar-communication (DFRC) architecture. The information of downlink communication is modulated via the ambiguity function (AF) sidelobe nulling in the prescribed range-Doppler cells. At the same time, the sidelobe around the AF mainlobe is suppressed to ensure the radar detection performance. User equipment (UE) computes the AF of the received signal to make a judgment of the position of nulling for achieving demodulation. To this end, an objective function to evaluate the depth of nulling in AF is developed as figure of merit accounting for the worst-case sidelobe level ratio over the range-Doppler cells of interest. The resultant design is very challenging to solve due to the non-convex and non-smooth quartic fractional objective function and the NP-hard constant modulus constraint. Herein, we develop a fractional-alternating direction penalty method (FADPM) algorithm that invokes the fractional program theory and the ADPM framework. Specifically, we formulate a new ADPM form through introducing an auxiliary variable and resorting to the Dinkelbach's procedure. In each iteration, we transform the original quartic fractional program into two quadratic optimization subproblems both of which are approximated successively through a series of convex subproblems. We also provide the analytical convergence guarantee of the proposed FADPM algorithm. The simulation results verify the performance of the proposed algorithm and exhibit the effectiveness of the DFRC framework in modulation and demodulation process guaranteeing that a high data rate is achievable.

A collocated multiple-input and multiple-output (MIMO) radar has the ability to track multiple targets under the simultaneous multi-beam mode, where each transmit beam is defocused to search the entire surveillance space, and then; a focused beam is formed at the receiver to achieve a higher resolution. A joint power and bandwidth allocation strategy is developed in this mode. The optimization model is established with the aim of improving the sum of weighted posterior Cramer-Rao lower bounds (PCRLBs) of multiple targets, which is predicted based on the feedback information in the tracking recursive cycle, subject to the limited power and bandwidth budget. By exploiting the convex relaxation technique and the cycle minimizer, this problem is transformed into a two-stage convex problem, and an efficient solution is provided using the cyclical optimization framework. The effectiveness of the proposed strategy is demonstrated by comparison with three uniform resource allocation methods in many cases.

Frequency modulated continuous wave (FMCW) based millimeter wave (MMW) radar systems have found widespread applications in advanced driving assistant system recently. MMW radar has clear superiority in many aspects, such as excellent range resolution, adaptability to various environments and low cost. However, its fatal weakness is the poor angular resolution which is very important in automotive radar applications. This shortcoming can be overcome by using a physically large aperture. However, achieving moderate angular resolution requires an array that is too large to be practically integrated into a vehicle. In this paper, a high resolution direction of arrival (DOA) approach is proposed by utilizing the relative motion between radar and targets, and a very large virtual aperture can be created by combining the signal vectors from multiple consecutive coherent processing intervals (CPIs). It should be pointed out that, the proposed approach is applicable for both stationary and moving targets even without motion data of vehicles. Therefore, it is completely different from the synthetic aperture radar (SAR). The effectiveness is verified by both simulation results and the measurement data. It is demonstrated that the angular resolution can be superior to 1° by a linear array consisting of only 4 elements.

In cognitive radio enabled vehicular ad-hoc networks (CR-VANETs), the secondary users, i.e., the secondary intelligent vehicles have the ability to perceive the driving environment and use the unoccupied spectrum of primary users for data transmission. In this paper, we consider the radar assisted CR-VANETs, in which the secondary users firstly sense the surroundings using radar modules periodically and Swerling 0, Swerling 2 and Swerling 4 target models are considered respectively. Note that the secondary users access the spectrum of primary users and communicate with their corresponding receivers when no one is detected by the low range radar module. Moreover, we design the joint radar sensing and data transmission frame structure and formulate the radar sensing-throughput tradeoff problem mathematically. It is proved that the formulated tradeoff indeed has the optimal radar sensing time which yields the maximum throughput for the secondary networks. Numerical results and simulations verify that the optimal radar sensing time achieving the maximum throughput for Swerling 0 target model is ${0.05}\; \text{ms}$ when the radius of the radar sensing region is 320 meters, the densities of the primary users and secondary users are $\lambda _p=0.01 / m^2$ and $\lambda _s=0.2 / m^2$ , the received radar SNR is 10 dB and the detection probability is 99.9%. For the Swerling 2 and Swerling 4 target models, the optimal radar sensing times achieving the maximum throughput is ${0.14}\; \text{ms}$ and ${0.08}\; \text{ms}$ respectively when the detection probability is 99%.

Mutual interference among the radar sensors has become a serious concern due to the extensive growth of self driving vehicles (SDV) equipped with such sensors. The problem becomes more severe with the increase in traffic density, where a large number of SDVs gather within the proximity of inter-vehicular radars. Thereby, the number of resources required to maintain the neighbourhood orthogonality increases with traffic density and further leads to the problem of radar blindness. In this paper, we propose a graph-based resource allocation (GRA) scheme to assign resources to the running SDV pool. GRA assures that two closely located SDVs may not simultaneously use the same resource. Also, we integrate the notion of traffic-based dynamic-range approach (TDA) with GRA. Then, through simulation results, it is shown that GRA outperforms the state of art random allocation approach. Further, it is shown that GRA, along with TDA, may eliminate the problem of radar blindness.

Most autonomous vehicles build their perception systems on expensive sensors, such as LIDAR, RADAR, and high-precision Global Positioning System (GPS). However, cameras can provide richer sensing at a considerably lower cost, this makes them a more appealing alternative. A driving assistance system (DAS) based on monocular vision has gradually become a research hotspot, and inter-vehicle distance estimation based on monocular vision is an important technology in DAS. There are still constrains in the existing methods for estimating the inter-vehicle distance based on monocular vision, such as low accuracy when distance is larger, unstable accuracy for different types vehicles, and significantly poor performance on distance estimation for severely occluded vehicles. To improve the accuracy and robustness of ranging results, this study proposes a monocular vision end-to-end inter-vehicle distance estimation method based on 3D detection. The actual area of the rare view of the vehicle and the corresponding projection area in the image are obtained by 3D detection method. An area-distance geometric model is then established on the basis of the camera projection principle to recover distance. Our method shows its potential in complex traffic scenarios by testing the test set data provided on the real-world computer vision benchmark, KITTI. The experimental results have superior performance than the existing published methods. Moreover, the accuracy of occluded vehicle ranging results can reach approximately $98\%$ , while the accuracy deviation between vehicles with different visual angles is less than $2\%$ .

When the elevation of target is smaller than a beamwidth, the complex multipath signals will distort the feature of direct signal reflected from target. The elevation of target can hardly be estimated accurately. Hence, in this paper, we proposed three kinds of neural networks models including deep neural network (DNN), 1-D convolutional neural network (1-D CNN) and 2-D convolutional neural network (2-D CNN) and their optimization method to mitigate phase distortion caused by multipath signals and enhance the phase feature of direct signal. The direction of arrival (DOA) estimation accuracy of physics-driven methods including digital beamforming (DBF) and multiple signal classification (MUSIC) is effectively improved. Concretely, we analyze the origins of error of DOA estimation in multipath environment and discuss the importance of phase feature to DOA estimation. A complete framework of feature-to-feature phase enhancement is built for DOA estimation in radar systems. The results of experiments with real data collected from a very high frequency (VHF) radar demonstrate the superior DOA estimation performance of proposed feature-to-feature learning methods with respect to other state-of-the-art methods including physics-driven methods and existing data-driven methods.

In recent years, software defined radio and digital signal processing have been widely used in communication and radar. As a result, the hardware and RF front-end for radar and wireless communication tends to be similar. Thus, using the same RF and hardware platform for joint radar-communication becomes viable. Joint radar-communication would bring more efficient plan and usage for the radio spectral resource. Furthermore, it could enable new applications that require information exchange and precise localization at the same time. In this paper, cyclic prefixed single carrier (CP-SC) and its variations are chosen as the waveforms for joint radar-communication. CP-SC waveform and its variations are popular in wireless communication and have been chosen by a few standards like IEEE 802.11ad and LTE-advanced. Efficient algorithms are proposed to use such waveforms for range and speed detection/estimation of targets. The proposed algorithms are derived from the maximum likelihood (ML) principle and have low computational complexity. Simulations show that the estimation performance of the proposed method is almost the same as that of ML and is much better than that of the channel estimation based method.

Noncoherent detection schemes are an appealing and low-complexity alternative in multi-user massive MIMO uplink systems compared to classical coherent detection algorithms, since no actual channel knowledge is required at the receiver. For noncoherent multi-user detection to function, the induced power at the base station is utilized to separate the different users. However, spatial separation is impossible when the users are located in the far-field of the receiving antenna array. Consequently, noncoherent detection fails in such scenarios. To this end, beamspace techniques can be applied, focusing the energy of the incident wave to a smaller subset of the receive antennas and enabling again the noncoherent detection scheme. This paper analyzes the beamspace capabilities of a dielectric lens and an analog beamforming network applied at the receiver. Furthermore, a sub-array architecture is proposed, relaxing the design requirements for practical implementation. It is shown that noncoherent detection in combination with beamspace techniques performs comparably to channel-estimation-based detection. In addition, the sub-array architecture revealed a significant performance enhancement accompanied by a reduced user separability., acceptedVersion

In this paper, we propose an ergodic interference alignment (IA) method for interference elimination in spectrum sharing multiple-input multiple-output (MIMO) radar and multi-user MIMO communication systems. In existing ergodic IA methods, the channel state information (CSI) is generally assumed to be constant, but time-varying channels should be considered in practice. Differently, our proposed ergodic IA allows to time-varying and/or frequency selective channels for spectrum sharing radar-communication systems. The essence is equivalent to design a simple transmit beamforming, which helps to find a pair of desired complementary channels easily without long-time system delay, therefore, the interferences can be effectively cancelled. The proposed method is evaluated by generalized likelihood ratio test (GLRT) and signal to interference plus noise ratio (SINR) for the radar functionality, while the communication functionality is examined by bit error rate (BER) performance. Both theoretical analysis and simulation results validate that the effectiveness of the ergodic IA method for spectrum sharing radar-communication systems.

This paper considers the direction of arrival (DOA) estimation of coherent and uncorrelated targets by exploiting a flexible multiple input multiple output radar. First, the proposed radar is generalized with two coprime expansion factors for enlarging the inter-element spacing of transmit and receive arrays, referred to as sparse arrays with flexible inter-element spacing (SA-FIS), which shows that the conventional nested or coprime ones are the special cases. The range of consecutive lags and the number of unique lags in the sum-difference coarray are derived in closed form. It is verified that SA-FIS can obtain the maximum unique lags and also suppress the mutual coupling effects. We then extend the FIS viewpoint to sparse nonuniform linear arrays, where the two-level nested and coprime arrays are employed to achieve a significant increase in degrees of freedom (DOFs). Furthermore, to fully utilize these unique lags, we propose a reduced-complexity two-step sparse representation algorithm. By modifying and removing the off-diagonal elements of the estimated target covariance matrix, the proposed method can just identify the diagonal ones, thereby leading to further improved performance with much lower computational complexity. Finally, the Cramer-Rao bound on DOAs and correlated coefficients for SA-FIS is derived. Numerical simulations demonstrate the superiority of the proposed method with SA-FIS in terms of DOFs, computations, estimation accuracy, and resolution capability compared with previous ones.

The collocated time-division multiplexing (TDM) multiple-input multiple-output (MIMO) technique and frequency-modulated continuous-wave (FMCW) chirp sequence are widely used in automotive radar because of the low hardware complexity and the high-accurate range and velocity measurements. In conventional TDM chirp sequences, the transmitters are switched according to their natural spatial order (referred to as a fixed sequential order), thereby inducing space-Doppler frequency coupling and a relatively low unambiguous Doppler interval. In this paper, a new FMCW chirp sequence and corresponding signal processing method for TDM MIMO automotive radar are proposed. The novel waveform adopts the concept of random transmission to distort the linear phase relationship between space and Doppler, therefore, overcoming the coupling problem. Then, via compressed sensing processing, the scheme can suppress the sidelobe pedestal arisen in the conventional matched filter for targets detection. Besides, based on multiple-frequency observations of the same targets, the velocity ambiguity can be adequately resolved. Compared with conventional chirp sequence, the proposed scheme simultaneously overcomes the typical TDM MIMO chirp sequence problems, thereby avoiding a reduction in the data rate. Also, the scheme significantly reduces the mutual interference between adjacent systems. The simulations and experimental results demonstrate the effectiveness of the proposed chirp sequence scheme.

Interference among frequency modulated continues wave automotive radars can either increase the noise floor, which occurs in the most cases, or generate a ghost target in rare situations. To address the increment of noise floor due to interference, we proposed a low calculation cost method using adaptive noise canceller to increase the signal-to-interference ratio. In a quadrature receiver, the interference in the positive half of frequency spectrum is correlated to that in the negative half of frequency spectrum, whereas the beat frequencies from real targets are always present in the positive frequency. Thus, we estimated the power of the negative frequency as an indication of interference, and fed the positive frequency and negative frequency components into the primary and reference channel of an adaptive noise canceller, respectively. The least mean square algorithm was used to solve for the optimum filter solution. As a result, both the simulation and experiment showed a good interference mitigation performance., Comment: This paper has been submitted to IEEE Transactions on Vehicular Technology

The observation of the passenger compartment using an interior RADAR becomes challenging when a vehicle is in motion. The signal received by the RADAR sensor measures not only the target's motion that we would like to detect, e.g., vital signs, but also a second, unwanted motion induced by exterior effects, e.g., displacement caused by the road, strong wind gusts, or car engine vibrations. Consequently, traditional techniques used to remove white Gaussian noise cannot be applied in this scenario. We propose a theoretical framework to investigate the denoising of a signal received by an interior RADAR using accelerometer measurements and a mechanical model to generate the cancellation signal stemming from the (unwanted) vibrational motion. We explain the denoising technique and show that the fundamental idea behind the cleaning process works well. Our observations are corroborated by a robust mathematical model and verified by simulations. Furthermore, the aforementioned denoising techniques enable us to formulate and solve an optimization problem to find an estimation for the passenger's mass in certain cases.

A control framework adopting the on-board radar sensor and vehicle-to-vehicle (V2V) communication has been developed to fulfill the automated vehicle following in the longitudinal and lateral directions. First, a linear feedforward and feedback controller constrained by the string stability is designed to follow the velocity of the preceding vehicle and ensure a safe inter-vehicle distance. Then, a path estimation method is proposed to calculate the trajectory of the preceding vehicle based on its historical movement data transmitted by V2V. The model predictive control method is applied to regulate the steering angle of the front wheel. Furthermore, the robustness and adaptability of the proposed method are validated by simulations and tests under several driving conditions. The results indicate that the proposed method shows the reliable performance to realize the longitudinal and lateral vehicle following only by radar and V2V, independent of high-accuracy positioning system and road marking, and is promising to expand available areas of automated vehicle platooning.

A major challenge in designing a spectral co-existence between radar and communication systems is how to simultaneously provide efficient utilization of the shared spectrum while maintaining a reliable performance for both systems. Due to its flexibility in using linear independent waveforms and superiority in terms of target detection and estimation, the collocated multiple-input-multiple-output (MIMO) radars provide a paramount capability in realizing spectrum sharing with communication systems. In this paper, we consider the use of joint transmit and receive beamformers for both the MIMO radar and MIMO communication systems. Specifically, we design a two-tier alternating optimization spectrum sharing framework that is based on interference alignment (IA) approach. The effectiveness of spectrum sharing between radar and communication systems is guaranteed by mitigating the mutual interference signals in the shared spectral band. Subsequently, motivated by the excellent performance provided by IA in different wireless network scenarios, we propose a spectrum sharing framework that consists of a main iterative algorithm with two subalgorithms included within its operation. The first subalgorithm is used to maximize the signal-to-interference-plus-noise ratio of the radar system, whereas the second one is utilized to maximize the average sum-rate of the communication system. The main iterative algorithm is proposed to jointly optimize the transmit and receive beamforming filters of both systems by performing alternating optimization between the two subalgorithms. The convergences of the proposed algorithm and its subalgorithms are numerically verified. Simulation results are provided to confirm the superiority of the proposed framework for both systems.

The problem of target detection in colocated multiple-input multiple-output radar with unknown disturbance covariance matrix is considered. According to the generalized likelihood ratio test, we design an adaptive detector by exploiting the persymmetric structure of the covariance matrix. In particular, training data are not required in the proposed adaptive detector. Analytical expressions for the probability of false alarm and detection probability are derived, which are confirmed by Monte Carlo simulations. The expression for the probability of false alarm shows that the proposed detector exhibits a constant false alarm rate property against the disturbance covariance matrix. Numerical examples based on both simulated and experimental data confirm that the proposed detector has better detection performance than its counterparts.

The temporal correlation of target can be exploited to improve the radar estimation performance. This paper studies the estimation of target scattering coefficients in an adaptive radar system, and a novel estimation method based on Kalman filter (KF) with waveform optimization is proposed for the temporally correlated target in the scenario with both noise and clutter. Different from the existing indirect methods, a direct optimization method is proposed to design the transmitted waveform and minimize the mean square error of the KF estimation. Additionally, the waveform is optimized subject to the practical constraints including the transmitted energy, the peak-to-average power ratio, and the target detection performance. With clutter and noise, the waveform optimization problem is non-convex. Therefore, a novel two-step method is proposed and converts the original non-convex problem into several semidefinite programming problems, which are convex and can solve efficiently. Simulation results demonstrate that the proposed KF-based method with waveform optimization can outperform state-of-art methods and significantly improve the estimation performance.

Cognitive dynamic system (CDS) is a structured engineering model and research tool inspired by certain features of the human brain. As a special function of CDS, cognitive risk control (CRC) actualizes the concept of predictive adaptation to bring risk under control when encountered with unexpected uncertainty. In this paper, the first experimental demonstration of CRC is presented in the practical application of vehicular radar systems, and an algorithm for transmit-waveform selection in cognitive vehicular radar (CVR) based on CRC is proposed. During each perception–action cycle, the perceptor of CVR processes new environmental inputs and provides the processed information to the executive through feedback channel for the selection of cognitive action. With the mechanism of task-switch control being functional all the time, the CVR will switch to a more capable operation mode in the face of unexpected disturbances or adverse events. In such cases, a new subsystem of executive is brought into play, in which the risk-sensitive cognitive action is finally selected and applied to the environment. Simulation results have shown the robustness and effectiveness of the proposed CVR system, which can make the next-generation vehicular radars more intelligent and play an important role in future self-driving cars.

Mutual interference between automotive radar sensors is becoming a major concern due to the rapid increase of vehicles equipped with such systems. While there has been a plenty of studies on the interference of frequency modulated continuous wave (FMCW) radars, no work on chirp sequence (CS) radars has been reported in the literature in spite of their growing popularity in the automotive field. In this regard, this work presents an investigation of mutual interference for automotive CS radars. We analytically derive formulas describing the probability of ghost target appearance, and the signal-to-interference mitigation gain for interference of different waveforms, including continuous wave, FMCW, and CS waveforms, with comparison to an equivalent FMCW radar model. The derived formulas on the signal-to-interference mitigation gain are also verified by simulation results.