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 National Key Laboratory for Positioning, Navigation and Timing Technology, Changsha 410073, China
2.
National Key Laboratory for Positioning, Navigation and Timing Technology, Changsha 410073, China

Funds: Supported by the National Natural Science Foundation of China (62531025)

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

Available Online: 2026-08-07

Abstract

Abstract

Objective To address the performance degradation of GNSS signal detection in weak-signal environments and the lack of false alarm rate (FAR) control in existing deep learning models, this study investigates a detection method under a constant false alarm rate (CFAR) constraint to enhance receiver reliability and practicality. Methods This study models the detection of direct sequence spread spectrum signals as a binary classification problem and proposes a comprehensive DL-based detection framework. The methodology is centered on three core innovations. First, an adaptive one-dimensional residual neural network (1D-ResNet-18) is designed to suit the characteristics of I/Q sampled time-series data. Key modifications include adjusting the input convolution kernels to 1×3 and removing the initial maximum pooling layer to prevent the loss of fine-grained features inherent in weak signals. Second, a "noise score pool threshold calibration" mechanism is introduced. By inputting a large volume of pure noise samples into the trained network, an empirical distribution of confidence scores for the "signal present" category is constructed. Decision thresholds are then dynamically determined based on the quantiles corresponding to preset FAR levels. Third, an unnormalized data preprocessing strategy is adopted, as it is demonstrated that preserving the original signal amplitude information is beneficial for network learning under low signal-to-noise ratio (SNR) conditions. The model's performance was rigorously validated using a simulated GPS L1 C/A signal dataset under various SNRs, FAR settings, and non-ideal colored noise environments. Results and Discussions Experimental results demonstrate significant performance gains achieved by the proposed method. At a FAR of 0.01, the detection probability approaches 100% at an SNR of –8 dB, marking a substantial improvement over traditional techniques. A systematic comparison of different FAR settings (0.0001, 0.001, and 0.01) indicates that while the detection probability curve predictably shifts as the FAR decreases, overall performance remains consistently high. Furthermore, the unnormalized data preprocessing strategy consistently outperformed the normalized strategy, introducing a performance gain of approximately 1 dB in the low SNR range. Compared with traditional autocorrelation detection methods, the deep learning model exhibits a significant overall detection performance improvement of 3 to 4 dB across various false alarm rates. Notably, the method also displayed robust generalization in untrained, non-ideal colored noise environments. Conclusions The proposed method effectively bridges data-driven deep learning with classical detection theory, resolving the lack of FAR control in neural networks. By balancing high sensitivity with precise false alarm management, this work provides a practical and robust framework for navigation signal processing in complex environments.
- Signal detection,
- Direct sequence spread spectrum,
- Deep learning,
- False alarm rate,
- Threshold calibration

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References(19)

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