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, Cramr-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.

In the integrated radar and communication (RadCom) systems, the integrated orthogonal frequency division modulation (OFDM) waveform with interleaved subcarriers (IS) can simultaneously perform radar and communication functions. However, the autocorrelation function (ACF) of the integrated waveform may have high sidelobe level with high probability due to the discontinuous spectrum for radar. To solve this problem, in this letter, a joint subcarrier and power allocation method is proposed to minimize the peak-to-sidelobe ratio (PSLR) while meeting the requirement of the communication data rate (CDR) and maintaining a certain range resolution. The formulated optimization problem is nonconvex, and we propose the cyclic minimization algorithm to obtain the subcarrier and power allocation. Finally, several numerical results are provided to verify the effectiveness of the proposed method.

Numerous studies on direction finding have focused on traditional passive sparse arrays with enhanced degrees-of-freedom (DoFs). However, many unexplored areas remain in the realm of active sparse multiple-input multiple-output (MIMO) radar, which holds the potential for further enhancing DoFs. In this study, we concentrate on direction, specifically two-dimensional (2D) direction-of-arrival and 2D direction-of-departure, finding for sparse bistatic MIMO radar. Unlike most existing studies that utilize only partial (cross-) covariance data associated with different subarrays, we propose a four-stage reconstruction-based approach that reconstructs the covariance data for the entire transceiver step by step while maintaining low computational complexity, as it requires only addition operations. This process includes a data rearrangement procedure utilizing our defined selection matrices. To evaluate the performance of our proposed approach, we also derive the Cramér-Rao bound for the multi-parameters involved in our sparse bistatic MIMO radar framework. Numerical simulations are conducted to validate the advantages of the proposed method, including increased identifiability and improved estimation performance, thanks to the enhanced DoFs.

To improve the processing performance of the integrated radar and communications (IRC) waveforms in colored noise scenario with range ambiguity, a complementary mismatch filter group (CMFG) design method is proposed by using the philosophy of the complementary sequence. The proposed method can decrease the sidelobe level (SLL) of unambiguous output and the energy of range ambiguous returns, and improve the output signal to noise ratio (SNR). To achieve this, a weighted optimization problem is developed, and the closed-form solution is derived. Finally, several simulation results are presented to show the effectiveness of the designed CMFG.

In hypersonic target detection scenarios involving large time– bandwidth product signals, the scale effect of target echo pulses leads to severe mismatch in conventional pulse compression filters. To address this issue, an efficient method for pulse compression and inter-pulse coherent integration, termed the Improved Radon Inverse Fourier Transform (IRIFT), is proposed. The IRIFT algorithm improves upon the Modified Radon Inverse Fourier Transform (MRIFT) in two key aspects. First, it replaces the compensation function in MRIFT with truncated sinc interpolation for velocity searching and sample collection, effectively eliminating the signal-to-noise ratio (SNR) loss and parasitic harmonic interference introduced by the compensation function. Second, following velocity search in the two-dimensional frequency domain, IRIFT performs range pulse compression using filters matched to the corresponding velocity bins, enabling ideal compression of echoes from hypersonic targets. The range– Doppler spectrum with well-focused echoes is then obtained via inverse FFT along the range dimension. A complete mathematical formulation of the radar echo model and the proposed algorithm is presented within a digital signal processing framework. The analysis quantifies the range and velocity estimation biases induced by conventional methods that neglect intrapulse scale effects, and derives the theoretical upper bound of output SNR gain under ideal matched filtering and coherent integration. Extension to hypersonic targets with constant acceleration is also included. Simulation results demonstrate that the proposed algorithm achieves near-optimal performance in both detection and parameter estimation for single and multiple targets.

In this paper, we present a novel cooperative scheme of joint optimal resource allocation, such that the overall performance of the coexisting radar-communications (CRC) system can be improved. In our proposed scheme, target detection and multiuser communication are performed by radar and communication subsystems at the same time, as well as a control center, which is responsible for joint resource management. We aim to minimize the ISLR for target detection and maximize the sum-rate for communications simultaneously by jointly optimizing the band assignment and transmit power allocation. Since the resulting optimization problem involving two performance metrics and a binary constraint is a multiobjective nonconvex problem, a two-tier iterative decomposition (TT-ID) approach is devised to obtain the globally optimal solution. However, compared with the conventional radar signals, the autocorrelation function of the devised radar signal may still have relatively high sidelobes. In particular, when the data transmission becomes the primary purpose of the CRC system, the sidelobe performance gets worse. As a consequence, some weak targets are most likely overshadowed by the adjacent strong targets through the matched filtering at the radar receiver. To address this, a spectral estimation algorithm based on the Bayes Cauchy–Gaussian (Bayes–CG) model is employed to further reduce the range sidelobes of the matched filter output at the radar receiver according to the prior distribution of the desired autocorrelation. Finally, several numerical results are provided to show the merits of the proposed method.