Spectrum competition and hardware complexity inherent in communication and radar systems can be alleviated by a dual-function radar communication (DFRC) systems. However, enhancing detection capabilities for maneuvering or weak targets remains a significant challenge, as traditional radar signal accumulation algorithms are not directly applicable to DFRC systems. This article proposes a novel method that integrates signal accumulation and parameter estimation for target detection in DFRC systems. The approach employs an orthogonal frequency division multiplexing (OFDM) waveform within a multiple-input-multiple-output (MIMO) framework, enabling the detection of high-speed or weak targets through a computationally efficient signal accumulation process. The proposed method comprises three key steps: first, the designed signals combined with the multiple signal classification (MUSIC) algorithm for target angle estimation in the spatial domain. Second, a two-step signal accumulation process is introduced, which separately extracts range and velocity information for weak or high-speed targets. Third, acceleration is derived based on velocity information and accumulation time. We present explicit expressions and detailed analyses of various performance metrics, including accumulation output response for high-speed targets, multiple target scenarios, and cases with low-signal-to-noise ratio (SNR). Additionally, Cramér-Rao bounds (CRBs) for azimuth, range cell, and velocity cell estimation in DFRC MIMO-OFDM systems are derived. Simulation results validate the proposed method, demonstrating its superior target detection performance compared to existing techniques.

Dual-function radar-communication (DFRC) systems are key to addressing spectrum congestion and hardware constraints in future 5G/6G networks. Among various DFRC waveform candidates, OFDM stands out for its flexibility, but its high range sidelobes pose challenges for weak target detection in cluttered environments. In this work, we propose a novel waveform optimization framework that jointly minimizes the peak sidelobe level (PSL), maintains a low symbol error rate (SER), and enforces constant envelope constraints. To solve the resulting non-convex, NP-hard problem efficiently, we introduce a Block-wise Majorization-Minimization (BWMM) algorithm that iteratively refines the phase of each OFDM symbol to suppress both auto- and cross-correlation sidelobes. Theoretical analysis and simulation results validate that the proposed BWMM-PSL method significantly enhances radar sensing performance while preserving communication reliability.

This paper proposes a novel design algorithm for dual-function radar communication (DFRC) Orthogonal Frequency-Division Multiplexing (OFDM) waveform. The algorithm achieves a tradeoff between detection performance and communication bit error rate (BER) performance. Firstly, the algorithm modulates communication information on the phase of the subcarrier coefficient of OFDM waveform using M-phase-shift keying (MPSK) schemes such as Binary-PSK (BPSK). Subsequently, the waveform minimises the weighted integrated sidelobe level (WISL) of the transmit waveform and the receive mismatch filter while ensuring the BER. Additionally, constraints are placed on constant amplitude, mainlobe energy and signal-to-noise ratio (SNR) loss. To address the non-convex optimization issues arising from algorithm design, a Weight Alternating Direction Method of Penalty (W-ADPM) network-based approach simultaneously optimises the transmit waveform and receives mismatched filters. The simulation experiments demonstrate that the proposed algorithm has better convergence performance for the proposed waveform compared to the Alternating Direction Method of Multipliers (ADMM) algorithm. Besides, compared to traditional matched filters, the jointly transmitted and received mismatched filters proposed in this paper provide better WISL cross-correlation performance while ensuring the BER.

Dual-function radar-communication (DFRC) systems offer a promising solution to mitigate spectrum competition and hardware complexity in 5G/6G communication. Orthogonal Frequency Division Multiplexing (OFDM) technology has been commonly employed in current DFRC signals. However, many DFRC signal sequences exhibit poor range sidelobes, making them unsuitable for weak target detection. In this paper, we design encrypted sparse transmitting waveforms to encrypt signals. In the time domain, the DFRC signal's Peak Side Level (DPSL) is minimized to enhance radar detectability, while simultaneously constraining the communication Bit Error Ratio (BER) and the constant envelope value of the signal to maintain communication quality. To address the non-convex optimization problem, we develop a Block Successive Upper-bound Minimization (BSUM) framework, which alternately updates each communication phase location. This framework aims to lower the dual-function cross- and auto-correlation peak sidelobe levels, referred to as the Block Successive Upper bound Minimization for DFRC DPSL (BSUM-DPSL) algorithm. The proposed algorithm's effectiveness is theoretically validated, and simulation results demonstrate that the effectiveness of designed MIMO-OFDM waveform in comparison with other waveforms.

Deep learning stands out as a potent state-of-the-art technique for target recognition. Unfortunately, the trustworthiness and reliability of deep learning networks encounter challenges in radar target recognition. In this letter, an inductive conformal prediction (ICP) enhanced long short-term memory spiking neural network (LSTM-SNN) is proposed. It integrates with the concept of conformal prediction in statistical learning theory and deep learning, and applied to birds and drones recognition with radar. The proposed method can provide good recognition results for drones and birds with supplying confidence and credibility for each identification, and yields a confidence interval containing the true value of the estimated at the desired confidence level, such as 98%. The benefits of the LSTM-SNN method were demonstrated with the bird detection datasets obtained by radar in the airport.