A Phase-Coded FMCW-Based Integrated Sensing and Communication System Design for Maritime Search and Rescue
Abstract
1. Introduction
- A comprehensive MIMO FMCW ISAC simulation platform for maritime SAR: We developed a unified simulation platform that integrates realistic maritime clutter modeling, multitarget sensing, and dual-function communication [22,23]. The system incorporated compound K-distributed sea clutter with tunable shape and scale parameters, a flexible MIMO transceiver architecture, and support for four representative phase coding schemes: Barker, Frank, Costas, and Zadoff–Chu [17]. This enables system evaluation from waveform generation and coding to beamforming, range-Doppler processing, and communication decoding, under varying channel and clutter conditions [24,25].
- Sensing performance analysis under heavy clutter: We evaluated detection metrics such as PSLR, ISLR, and multitarget detection accuracy via permutation-based pairing. Monte Carlo simulations demonstrated the superior autocorrelation and sidelobe suppression of ZC codes [26,27]. In addition, range estimation error is assessed as a function of target range and clutter level, showing that ZC-coded FMCW achieves a higher accuracy than conventional uncoded FMCW, particularly in clutter-dense zones (50–70 m), thus improving robustness in mid-range detection. The system also supports extraction of micro-motion signatures and life-sign features, fulfilling the aim even under strong clutter [28,29,30].
- Integrated communication analysis in ISAC scenarios: A quadrature phase shift keying (QPSK)-based communication system was used as a base time to evaluate the bit error rate (BER) and channel capacity of the PC-FMCW under the sea clutter environment represented by the compound-K distribution. Results showed that ZC-coded FMCW offers a comparable BER performance over conventional OFDM waveforms across a wide SNR range. This demonstrates the dual-function capability of our ISAC system for both high-resolution sensing and reliable communication, offering a valuable avenue for future maritime SAR platforms operating in degraded environments.
2. System Model
2.1. Transmit Radar Signal
2.2. Phase Coding Modulation and Autocorrelation Analysis
- Barker Code: A 13-length binary sequence commonly written as
- Frank Code: Generated for an integer M (e.g., ), yielding a sequence of length . The -th element is given by
- ZC Code: A constant-amplitude zero-autocorrelation code of length defined by
- Costas Code: Constructed via a permutation-based approach for a given M, typically generating a length- polyphase sequence. By placing frequency-shifted pulses in a permutation pattern, Costas arrays achieve good randomness and multipath resistance.
2.3. MIMO Array Processing and Beamforming
2.4. Clutter and Channel Modeling
2.5. Received Signal Formulation
3. Signal Processing for Sensing
Algorithm 1 MIMO Radar System Simulation Workflow |
Input: System parameters: , B, , , Input: Target parameters: , , , , Output: RTI image, Doppler spectrum, motion separation results 1: Initialize: ; Generate ULA steering vector for angle 2: Generate phase coding sequence: 3: for each chirp to do 4: Target motion model: Compute and 5: Radar return synthesis: Combine torso, arm, and clutter returns 6: Multichannel baseband signal: Modulate by codeSeq and add Additive White Gaussian Noise(AWGN) on each virtual channel 7: end for 8: Beamforming: Sum over all channels using steering vector 9: Range processing: Apply windowed Fast Fourier Transform(FFT) over fast-time 10: Clutter suppression: Estimate and subtract background 11: Target detection: Select bin with maximum 12: Motion separation: Bandpass filter for torso and arm 13: Visualization: Generate spectrogram, and envelopes |
3.1. Signal Processing for Detection
3.2. Data Cube Formation
3.3. Multitarget Detection Simulation
3.4. Communication Performance Comparison
4. Experimental Results
4.1. Single-Target Detection: Range and Doppler
4.2. Motion Component Separation
4.3. Multitarget Detection and Accuracy
4.4. Communication Performance Results
5. Discussion
- A.
- Monte Carlo Evaluation of Detection Probability
- B.
- Effect of Channel-Estimation Accuracy
- C.
- Resilience to DoA Mismatch
- D.
- Impact of Clutter Parameters
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
AFDM | affine frequency division multiplexing |
AWGN | Additive White Gaussian Noise |
BER | Bit Error Rate |
DFRC | Dual-Function Radar-Communication |
EPIRB | Emergency Position Indicating Radio Beacon |
FFT | Fast Fourier Transform |
FMCW | Frequency-Modulated Continuous Wave |
IFFT | Inverse Fast Fourier Transform |
ISAC | Integrated Sensing and Communication |
ISLR | Integrated Sidelobe Level Ratio |
LFM | Linear Frequency Modulation |
MIMO | Multiple-Input Multiple-Output |
MSE | Mean Squared Error |
MVDR | Minimum Variance Distortionless Response |
OFDM | Orthogonal Frequency-Division Multiplexing |
OTFS | orthogonal time frequency space |
PRF | Pulse Repetition Frequency |
PSLR | Peak Sidelobe Level Ratio |
QAM | Quadrature Amplitude Modulation |
QPSK | Quadrature Phase Shift Keying |
RCS | Radar Cross Section |
SAR | Search and Rescue |
Satellite SAR | Satellite-based Synthetic Aperture Radar |
SNR | Signal-to-Noise Ratio |
STFT | Short-Time Fourier Transform |
UAV | Unmanned Aerial Vehicle |
ULA | Uniform Linear Array |
ZC | Zadoff–Chu Sequence |
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System/Method | ΔR (m) | Limitation |
---|---|---|
X-band SAR [4] | 1–2 | Long revisit cycle; no real-time monitoring. |
Marine FMCW Radar [5] | ∼0.6 | No Doppler or comm.; poor clutter handling. |
OFDM-ISAC [6] | ∼1 | Sensing degrades under heavy clutter. |
EO/IR UAV Imaging [7] | - | Visibility-dependent; limited by light conditions. |
Signal Type | Base Pattern | Code Length | Total Length |
---|---|---|---|
(Chirps) | (⌈100/CodeLen⌉) | ||
Classic FMCW | - | 1 | 100 |
Barker | Barker code | 13 | 8×, truncate to 100 |
Frank (M = 4) | Order-4 Frank code | 16 | 7×, truncate to 100 |
ZC (u = 1) | ZC sequence | 31 | 4×, truncate to 100 |
Costas (M = 4) | Order-4 Costas code | 16 | 7×, truncate to 100 |
Coding Scheme | PSLR (dB) | ISLR (dB) |
---|---|---|
Classic FMCW | 0.09 | 18.17 |
Barker | 1.21 | 6.70 |
Frank | 1.51 | 10.04 |
Costas | 1.51 | 13.44 |
ZC | 3.22 | 1.35 |
Parameter | Value | Description |
---|---|---|
Carrier frequency | ||
Wavelength | ||
B | FMCW bandwidth | |
Range resolution | ||
Maximum unambiguous range | ||
Chirp duration | ||
Sampling rate | ||
1000 | Samples per chirp | |
Pulse repetition frequency | ||
1000 | Number of chirps per frame | |
M | 4 | Number of transmit antennas |
N | 4 | Number of receive antennas |
d | Element spacing () | |
Adaptive | Beamforming vector (MVDR) | |
Code | ZC | Phase coding |
50 m | Initial target range | |
0.3 m | Torso oscillation amplitude | |
0.2 Hz | Torso oscillation frequency | |
0.5 m | Arm oscillation amplitude | |
1.0 Hz | Arm oscillation frequency | |
1.0 m2 | Torso radar cross-section | |
0.9 m2 | Arm radar cross-section | |
m | 3 | Clutter shape parameter |
1 | Clutter scale parameter | |
50 | Number of clutter scatterers |
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Xing, D.; Zhang, C.; Zhang, Y. A Phase-Coded FMCW-Based Integrated Sensing and Communication System Design for Maritime Search and Rescue. Sensors 2025, 25, 5403. https://doi.org/10.3390/s25175403
Xing D, Zhang C, Zhang Y. A Phase-Coded FMCW-Based Integrated Sensing and Communication System Design for Maritime Search and Rescue. Sensors. 2025; 25(17):5403. https://doi.org/10.3390/s25175403
Chicago/Turabian StyleXing, Delong, Chi Zhang, and Yongwei Zhang. 2025. "A Phase-Coded FMCW-Based Integrated Sensing and Communication System Design for Maritime Search and Rescue" Sensors 25, no. 17: 5403. https://doi.org/10.3390/s25175403
APA StyleXing, D., Zhang, C., & Zhang, Y. (2025). A Phase-Coded FMCW-Based Integrated Sensing and Communication System Design for Maritime Search and Rescue. Sensors, 25(17), 5403. https://doi.org/10.3390/s25175403