Intelligent Detection of DSSS Signals Under False-Alarm Rate Constraints Based on a Noise Score-Pool Threshold Calibration Mechanism

ZHANG Tao; TANG Xiaomei; SUN Guangfu

doi:10.11999/JEIT260414

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ZHANG Tao, TANG Xiaomei, SUN Guangfu. Intelligent Detection of DSSS Signals Under False-Alarm Rate Constraints Based on a Noise Score-Pool Threshold Calibration Mechanism[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260414

Citation:

ZHANG Tao, TANG Xiaomei, SUN Guangfu. Intelligent Detection of DSSS Signals Under False-Alarm Rate Constraints Based on a Noise Score-Pool Threshold Calibration Mechanism[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260414

Citation:

ZHANG Tao, TANG Xiaomei, SUN Guangfu. Intelligent Detection of DSSS Signals Under False-Alarm Rate Constraints Based on a Noise Score-Pool Threshold Calibration Mechanism[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260414

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Intelligent Detection of DSSS Signals Under False-Alarm Rate Constraints Based on a Noise Score-Pool Threshold Calibration Mechanism

doi: 10.11999/JEIT260414 cstr: 32379.14.JEIT260414

1.
College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
2.
National Key Laboratory for Positioning, Navigation and Timing Technology, Changsha 410073, China

Funds: The National Natural Science Foundation of China (62531025)

Received Date: 2026-04-08
Accepted Date: 2026-07-28
Rev Recd Date: 2026-07-16

Available Online: 2026-08-07

Abstract

Abstract

Objective To address the degradation in Global Navigation Satellite System (GNSS) signal detection performance under weak-signal conditions and the limited control of the probability of false alarm (P_fa) in existing Deep Learning (DL) models, a DSSS signal detection method with a prescribed P_fa is investigated. The method enables the DL detector to be evaluated within a Constant False Alarm Rate (CFAR) framework. Methods DSSS signal detection is formulated as a binary classification problem, and a DL-based detection framework is developed. An improved one-dimensional ResNet-18 is designed for I/Q sampled time-series data. The input convolution kernel is set to 1×7, and the initial maximum pooling layer is removed to preserve weak-signal temporal features. The first residual layer is also configured without downsampling. A noise score-pool-based threshold calibration mechanism is developed to impose P_fa constraints on the detection decision. A large number of pure-noise samples are processed by the trained network to obtain the empirical distribution of confidence scores for the signal-present class. The decision threshold is then calibrated according to the quantile corresponding to the preset P_fa. In addition, the effect of signal normalization on detection performance is evaluated. The proposed method is validated using a simulated GPS L1 C/A signal dataset under different Signal-to-Noise Ratio (SNR) and P_fa settings and in non-ideal colored-noise environments. Results and Discussions The proposed method achieves high detection performance under different P_fa constraints. At a P_fa of 0.01, the detection probability reaches 100% at an SNR of –8 dB. When the P_fa decreases from 0.01 to 0.001 and 0.000 1, the detection curve shifts toward higher SNRs, but the decrease in detection performance remains limited. The unnormalized preprocessing strategy consistently outperforms Root-Mean-Square (RMS) normalization, providing a performance gain of approximately 1 dB. Compared with the traditional autocorrelation detection method, the proposed DL-based detector provides a detection performance gain of approximately 3～4 dB across the tested P_fa settings. In colored-noise environments not used for network training, the proposed method maintains effective detection performance and demonstrates robustness to noise mismatch. The structural ablation results further show that removing the maximum pooling layer and retaining the temporal resolution of the first residual layer improve detection performance under low-SNR conditions. Conclusions A DL-based DSSS signal detection method with noise score-pool-based threshold calibration is proposed. The empirical distribution of pure-noise confidence scores is used to calibrate the decision threshold, thereby incorporating the DL detector into a CFAR-based detection framework. The improved one-dimensional ResNet-18 effectively extracts features from I/Q time-series data, whereas the unnormalized preprocessing strategy preserves useful signal-amplitude information. The proposed method improves detection sensitivity while maintaining effective P_fa control and exhibits robustness under non-ideal colored-noise conditions.
- Signal detection,
- Direct Sequence Spread Spectrum(DSSS),
- Deep Learning(DL),
- Probability of false alarm,
- Threshold calibration

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References(19)

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