Based on a low-noise cesium spectral lamp, a high-sensitivity self-oscillating cesium atomic magnetometer with a wide operating temperature range has been developed, solving problems with existing sensors such as a limited operating temperature range and difficult startup at low temperatures. The temperature feedback mechanism is used to make adjustments to any fluctuations in the cesium lamp’s excitation source in real time, improving the magnetometer’s stability and operating temperature range. Herein, the design and optimization of the cesium atomic magnetometer are presented, and a prototype of the magnetometer is described. The quantum limit sensitivity of the cesium atomic magnetometer is estimated by evaluating the intrinsic relaxation rate in the geomagnetic field. A test demonstrates that the cesium atomic magnetometer’s sensitivity in the geomagnetic background is around $140\mathrm{f}\mathrm{T}/\sqrt{\mathrm{H}\mathrm{z}}$ at 1 Hz at room temperature, and the operating temperature range is from −50 °C to 70 °C, surpassing most of the commercial products of its kind in terms of sensitivity and operating temperature range.

Magnetic anomaly detection from aerial platforms enables passive, non-contact detection of underwater ferromagnetic targets. To address the issues of traditional Orthonormal Basis Function (OBF) detection relying on prior knowledge of the closest contact distance and susceptibility to mismatch with single-scale templates, this paper proposes an aerial magnetic anomaly target detection method that integrates multi-scale OBF with a convolution-augmented gated attention unit (CGAU). Six sets of fixed OBF convolution kernels each output three matching responses, which are concatenated with the original sequence to form 19-channel features. Four layers of one-dimensional (1-D) convolution and two stages of CGAU extract local waveform features and capture long-range dependencies. The precision (PC), detection rate (DR), and F1-score of the proposed network on the simulated test set are 94.70%, 95.11%, and 94.90%, respectively (best-performing seed = 42). On the measured dataset, it detected 11 of 12 overpass events, achieving an event detection rate of 91.67%. The proposed network has approximately 0.43 M trainable parameters and achieves a higher mean F1 across three random seeds than the seven selected baselines, demonstrating its potential for aerial magnetic anomaly target detection.