Tri-Domain Multimodal Fusion: A Triple- Branch BiLSTM-CNN Framework for Robust Satellite Telemetry Anomaly Detection

Tri-Domain Multimodal Fusion: A Triple- Branch BiLSTM-CNN Framework for Robust Satellite Telemetry Anomaly Detection

Nayna Potdukhe, Astha Thakur, Anushree Fadnavis, Shrushti Samarth, Bhumika Thakre

Department of Artificial Intelligence and Machine Learning Government Polytechnic,Nagpur ,India. https://orcid.org/0009-0000-8668-1203

Abstract – Telemetry data that reflects the operational condition of onboard subsystems is continuously produced in vast quantities by satellite systems.[1,5]. For ensuring satellite reliability and preventing system failures, it is necessary to identify unusual trends in this data. Telemetry signals are frequently difficult for conventional anomaly detection techniques to handle because of their complicated temporal and multidimensional character. This study suggests a multimodal deep learning architecture for anomaly prediction in satellite telemetry data in order to solve this issue.

The suggested method starts with data preprocessing, which includes data cleaning, normalization, and feature engineering utilizing lag-based features and rolling statistical metrics. After that, the class imbalance is addressed using the Synthetic Minority Oversampling Technique (SMOTE), and the sequential telemetry patterns are created using a sliding window method. The system uses a multimodal architecture that integrates three complementary models: a spectrogram-based CNN for identifying the signal’s frequency domain features, a CNN for extracting spatial representations from telemetry-derived images, and a Bidirectional Long Short-Term Memory (BiLSTM) network with an attention mechanism for learning temporal patterns[11,13].

A feature fusion layer integrates the features taken from these models, which is followed by completely connected neural layers that use a sigmoid activation function to classify anomalies. Performance indicators including accuracy, precision, recall, F1-score, and AUC are used to assess the model’s detection efficacy. The suggested multimodal method successfully integrates temporal, spatial, and spectral data from telemetry data, according to experimental findings, which enhances the ability to detect anomalies. This framework offers a flexible way to automatically monitor the health of satellites and send out timely alerts about anomalies.

Keywords – Satellite Telemetry, Anomaly Detection, Multimodal Deep Learning ,BiLSTM, Self-Attention Mechanism, Convolutional Neural Networks (CNN), Spectrogram Analysis, Sensor Fusion, Spacecraft Health Monitoring, SMOTE, Time-Series Analysis, F1-Score Optimization.

1. INTRODUCTION

   The escalating complexity of contemporary satellite constellations and the rapid transition toward autonomous space operations have rendered traditional telemetry monitoring techniques progressively inadequate. Conventional “out-of-limits” (OOL) checking, which triggers alerts based on static, human-defined threshold crossings, is fundamentally reactive and fails to detect the subtle, non-linear degradations and inter-variable anomalies that often precede mission-critical hardware failures[3,4].

   As satellite systems generate massive volumes of highdimensional time-series data,[1,5] there is a critical need for robust Anomaly Detection Systems (ADS) that can provide early warnings with high precision and low falsealarm rates. This paper introduces a novel Multimodal Triple-Branch Deep Learning Framework designed to decode the complex health signatures of

   orbital assets. By recognizing that satellite health is a multifaceted phenomenon, our proposed architecture processes telemetry through three distinct computational “lenses” simultaneously[1,5].

   Temporal sequences, spatial-structural patterns, and frequency-domain spectral signatures.The first core component of our framework is the Temporal Intelligence Branch, engineered to capture the time-dependent evolution of satellite states. Unlike standard recurrent architectures that often lose context over long sequences, our model utilizes a Bidirectional Long Short-Term Memory (BiLSTM) network. This allows the system to analyze telemetry streams in both forward and backward temporal directions, effectively maintaining a “global memory” of the satellites behavior and detecting trends that develop over time[13,15].

   To further refine this process, we integrated a SelfAttention Mechanism within the BiLSTM layers. This attention layer acts as a dynamic weighting system, allowing the model to focus its cognitive resources on the most relevant time steps within a sequence. By amplifying transient anomalies while suppressing steady- state noise, this branch ensures that even the most fleeting precursors to failure are captured and analyzed with

   granular sensitivity. Complementing the temporal analysis is the Spatial-Structural Branch, which introduces a unique perspective on inter-variable dependencies.

   In complex satellite systems, sensors do not operate in isolation; for instance, power consumption, thermal regulation, and orbital positioning share deep physical correlations. To exploit these latent relationships, we implement a spatial encoding strategy where onedimensional telemetry sequences are reshaped into 2D Synthetic Imagery ($8 \times 8$). This transformation enables the application of Deep Convolutional Neural Networks (CNNs) for advanced pattern recognition. The CNN branch is specifically trained to identify “Structural Breaks” in these synthetic health mapsdetecting shifts in the internal physics of the satellite that would be invisible to traditional sequencebased models.

   By treating telemetry as a visual texture, the model can effectively differentiate between nominal operational noise and actual structural deviations in the satellite’s profile.Recognizing that many hardware-level degradations, such as mechanical friction in reaction wheels or power supply ripples, manifest primarily as periodic oscillations, our framework includes a dedicated Spectral Diagnostic Branch. This branch utilizes ShortTime Fourier Transform (STFT) to convert raw telemetry into Spectrograms, providing a high-fidelity view of the signals frequency domain.A specialized CNN then analyzes these spectral signatures to detect anomalies such as oscillator drift, mechanical fatigue, or electronic interference. By integrating this frequency-domain analysis, the model gains an “acousticlike” diagnostic capability, ensuring that hardware degradations masked in the time-series domain are identified through their spectral fingerprints.

   This tripartite approach ensures that if an anomaly is subtle in the time domain, it is likely to be captured by either the spatial or spectral branches.Finally, to make this framework viable for real-world mission deployment, we addressed the inherent challenges of Class Imbalance and Operational Reliability.

   Given that actual anomalies are “Black Swan” events representing a tiny fraction of total observationswe implemented SMOTE (Synthetic Minority Over-sampling Technique) to create a balanced training environment.

   This ensures the model learns to identify rare failure states as effectively as nominal operations. Furthermore, rather than using a static classification threshold, we employ an F1-optimized Dynamic Alert System. By automatically identifying the optimal threshold through Precision-Recall analysis, our system maximizes the balance between high detection rates and the minimization of false alarms that cause operator fatigue.This holistic, multimodal fusion of BiLSTM- Attention, Spatial CNN, and Spetral Analysis provides a scalable, deployment-ready solution for the next

   generation of intelligent satellite ground station operations.

2. DIAGRAM

   Fig1:Proposed Model Architecture Flowchart

   Fig 2: Overview of Multi-SatNet system pipeline

3. LITERATURE REVIEW

   For the purpose of guaranteeing the safety and dependability of spacecraft activities, anomaly detection is essential for satellite telemetry. Traditional methods, which depend on statistical thresholds and rule-based procedures, are frequently inadequate at capturing the intricate temporal, spatial, and frequency-domain dependencies in telemetry data [1, 2].

   Many strategies have been suggested in response to the rise of machine learning and deep learning: Models Based on Time Series:

   Because of their capacity to capture long-term dependencies, LSTM-based models have been extensively used to identify anomalies in sequential telemetry data. Using a BiLSTM model with attention, Hundman et al [1] achieved 91.29% accuracy. In a similar way, Malhotra et al.[13] employed LSTM networks to achieve 95.53% accuracy in detecting anomalies in spacecraft time series. Temporal Convolutional Networks (TCN), which have demonstrated 96.45% accuracy on OPS-SAT benchmarks [5], have also been studied[1,12,13,21].

   Methods Utilizing Autoencoders and GANs: Autoencoder-based models, such as variational and deep autoencoders, detect anomalies by looking for reconstruction errors and reconstructing typical telemetry

   patterns. Zhou & Paffenroth [16] and Sakurada & Yairi

   [14] showed that they could identify anomalies with an accuracy rate of more than 90%. The Bai [8] study, which achieved 90.0% accuracy, also used GAN-based techniques to create synthetic normal behavior, allowing for the identification of minor anomalies[14,16,18,22]. Multimodal Strategies and Graph Neural Networks The correlations between telemetry channels and data modalities have been modeled using graph neural networks (GNNs). By fusing telemetry, spectrogram, and image features, Deng et al. [11] and Lai et al. [10] achieved 91.091.5% accuracy using spatiotemporal graph learning methods. Complex dependencies that single- modality models may overlook are well captured by these multimodal methods[11,33].

   Benchmark Assessments:

   Datasets like OPS-SAT[2] and SMAP/MSL are now common benchmarks for assessing anomaly detection models. Models that integrate CNN, LSTM, and attention mechanisms regularly attained more than 95% accuracy [12], [13], while Ruzczak et al. [11] recorded 98.4% accuracy on the OPS-SAT benchmark. The significance of hybrid and multimodal feature extraction in actual spacecraft anomaly identification is highlighted by these benchmarks[1,2].

   Studies of Surveys and Reviews

   The progression of anomaly detection in spacecraft telemetry has been summed up in recent survey studies, which highlight the shift from statistical and thresholdbased approaches to deep learning and multimodal frameworks [14], [15]. These reviews emphasize difficulties that drive the creation of resilient multimodal models, such as class imbalance, noisy telemetry, and interpretability[8,28,29,30].

4. METHODOLOGY

   This research introduces a deep learningbased diagnostic framework called Multi-SatNet, developed to improve anomaly detection in satellite telemetry systems. Conventional spacecraft monitoring techniques typically depend on fixed thresholds and univariate analysis, which are insufficient for identifying complex interactions among multiple telemetry variables. To overcome these limitations, the proposed approach utilizes a multimodal deep learning architecture with a late-fusion strategy.

   The framework analyzes satellite telemetry through three complementary perspectives: temporal behavior, spatial relationships among sensors, and frequency-domain patterns. Each computational branch extracts distinct information from telemetry data, allowing the model to build a comprehensive representation of subsystem behavior. The methodology consists of three major stages: data preprocessing and feature engineering, multimodal feature extraction, and anomaly classification[1,11,17].

   1. Data Preprocessing and Feature Engineering

      The effectiveness of a deep learning model largely depends on the quality and representation of input data. Therefore, a structured preprocessing pipeline is designed to convert raw telemetry streams into meaningful feature sets suitable for training[1,13,21].

      1. Feature Augmentation

         Raw telemetry signals often lack contextual indicators needed to identify gradual degradation in spacecraft subsystems. To enhance the representation of telemetry signals, statistical features are generated using sliding windows. These features include rolling mean and rolling standard deviation, which capture temporal trends and variability in sensor readings.

         In addition, lag-based temporal features are created to represent relationships between current and previous observations.These engineered attributes help the model recognize progressive anomalies where the rate of change in sensor values is more informative than the absolute magnitude.

      2. Temporal Window Segmentation

         Continuous telemetry sequences are divided into overlapping segments using a sliding window approach with a fixed length of T = 50 time steps. This segmentation enables the model to preserve temporal context while differentiating between random noise and persistent abnormal patterns.

      3. Handling Class Imbalance

         In practical satellite telemetry datasets, anomalous events occur infrequently and usually account for a very small portion of the data. Training a model on such imbalanced datasets can cause it to favor normal class predictions.

         To address this problem, the Synthetic Minority Oversampling Technique (SMOTE)[21] is applied. SMOTE generates artificial samples for the minority class by interpolating between existing anomaly samples in feature space. As a result, the dataset becomes more balanced, which improves the models ability to learn rare anomaly patterns.

      4. Data Normalization

         Telemetry measurements originate from sensors with different units and scales, such as temperature, voltage, and rotational speed. To stabilize the training process, all features are normalized using MinMax scaling, which transforms values into a range between 0 and 1.

         X’ = (X Xmin) / (Xmax Xmin)

         where X is the original value and X is the normalized

         value.

   2. Multimodal Feature Extraction Architecture

      A key contribution of the Multi-SatNet model is its threebranch feature extraction structure, which analyzes telemetry data simultaneously across different domains.

      1. Temporal Branch (BiLSTM with Attention)

         The temporal branch captures sequential dependencies within telemetry signals using a Bidirectional Long

         ShortTerm Memory (BiLSTM) network. Unlike conventional LSTM models that process sequences in a single direction, BiLSTM considers both past and future context within the sequence[15].

         To further enhance performance, an attention mechanism is integrated after the BiLSTM layer. The attention module assigns weights to different time steps, enabling the model to emphasize the most informative portions of the telemetry sequence.

      2. Spatial Branch (Convolutional Neural Network) Satellite subsystems often display correlated behavior across multiple sensors. To learn these relationships, telemetry feature vectors are reshaped into a twodimensional representation (8 × 8) that can be processed by a Convolutional Neural Network (CNN). The CNN employs convolutional filters and batch normalization layers to exract spatial patterns among sensor readings. This branch is particularly useful for identifying anomalies that involve simultaneous deviations across multiple telemetry channels[1ex
      3. Spectral Branch (Spectrogram-Based CNN)

         Certain satellite anomalies appear as periodic disturbances, including vibration or oscillator instability. Such signals are more evident in the frequency domain than in the raw time series. To capture these patterns, telemetry sequences are converted into spectrogram representations, and a dedicated CNN is applied to learn spectral features. This branch helps detect frequency- related anomalies that may not be visible in time-domain analysis

   3. Multimodal Fusion and Classification The outputs produced by the three feature extraction branches are merged through feature concatenation, creating a 192- dimensional fused feature vector. This unified representation integrates temporal, spatial, and spectral characteristics of the telemetry data.

   The fused features are then processed by a series of fully connected dense layers, combined with dropout (p = 0.4) and batch normalization to improve model generalization and reduce overfitting.

   Finally, a Sigmoid activation function generates an anomaly probability score:

   P \in [0,1] Higher probability values indicate a greater likelihood of abnormal system behavior. To prevent excessive false alarms in operational monitoring environments, the system applies a dynamic threshold selection strategy based on maximizing the F1score on the validation dataset. This approach ensures a balanced trade-off between anomaly detection capability and false alarm reduction.

   Overall, the proposed Multi-SatNet architecture provides a

   robust and scalable framework for real-time anomaly detection in satellite telemetry systems.

5. DIAGRAM

   Fig 3.End-to-end workflow of the detection framework

6. EXPERIMENT SETUP AND EVALUATION

   This section presents the experimental implementation and performance evaluation of the proposed Multi-SatNet framework for anomaly detection in satellite telemetry data. The experiments aim to validate the capability of the multimodal architecture to accurately identify abnormal patterns in complex telemetry signals collected from satellite subsystems.

   1. Experimental Environment

      The proposed model was implemented using Python with deep learning frameworks including TensorFlow/Keras. Data preprocessing and feature engineering were carried out using NumPy, Pandas, and Scikit-learn libraries. The experiments were performed in a standard machine learning environment capable of training deep neural networks[12,14].

   2. Dataset Preparation

      The experimental study utilizes satellite telemetry datasets containing multiple sensor readings that represent the operational status of spacecraft subsystems. These telemetry parameters include signals such as temperature measurements, voltage levels, power usage, and system performance indicators.

      The dataset contains both normal operational behavior and anomalous events, which makes it suitable for training anomaly detection models. Before training, the telemetry data is cleaned and structured to remove noise and inconsistent values[1,21].

   3. Training Data Generation

      To capture temporal dependencies in telemetry signals, the continuous data stream is segmented into sequences using a sliding window approach. Each sequence consists of 50 time steps, allowing the model to learn sequential patterns and detect gradual system deviations.

      Since anomalous events are relatively rare in satellite telemetry data, the dataset exhibits class imbalance. To address this issue, the Synthetic Minority Oversampling Technique (SMOTE) is applied to generate synthetic anomaly samples and balance the dataset distribution.

   4. Model Training

      The proposed Multi-SatNet architecture integrates three parallel feature extraction branches to analyze telemetry data from different perspectives.

      The temporal branch utilizes a Bidirectional Long ShortTerm Memory (BiLSTM) network with an attention mechanism to capture sequential dependencies in telemetry signals. This branch focuses on identifying abnormal temporal patterns that may indicate subsystem failures.

      The spatial branch employs a Convolutional Neural Network (CNN) to learn relationships between multiple telemetry channels. By transforming telemetry features

      into structured representations, the CNN captures correlations among sensor readings.

      The spectral branch analyzes telemetry signals in the frequency domain by converting time-series sequences into spectrogram representations, which are processed using a CNN to extract spectral features.

      The outputs of these three branches are combined through a feature fusion layer, which concatenates the extracted features into a unified representation. The fused feature vector is then passed through fully connected dense layers to perform final anomaly classification.

      The final layer uses a Sigmoid activation function to produce a probability score indicating whether the input telemetry sequence represents normal behavior or an anomaly.

   5. Evaluation Metrics

      The performance of the proposed anomaly detection system is evaluated using standard classification metrics. These include accuracy, precision, recall, and F1-score, which collectively measure the models ability to correctly identify anomalous events while minimizing false alarms. Accuracy measures the overall correctness of predictions, while precision and recall evaluate the models effectiveness in identifying anomalies. The F1- score provides a balanced measure of these two metrics and is particularly useful for imbalanced datasets[23,25].

   6. Experimental Analysis

   The experimental results demonstrate that the proposed Multi-SatNet multimodal framework effectively captures complex patterns in satellite telemetry data. By combining temporal, spatial, and spectral features, the model provides improved anomaly detection capability compared to single-model approaches.

   The fusion of multiple feature representations enables the system to detect both sudden faults and gradual system

   degradations, making it suitable for real-time satellite health monitoring and predictive maintenance.

7. RESULTS

   This section, we present a comprehensive evaluation of the proposed Multimodal Fusion model for satellite telemetry anomaly detection. The model combines temporal, spatial, and spectral features to enhance detection reliability.

   1. Quantitative Performance Metrics

      The models performance was evaluated on a held-out test set after training for 100 epochs. The key performance indicatorsAccuracy, Precision, Recall, and F1-Score are summarized in Table II.

      TABLE PERFORMANCE EVALUATION SUMMARY

      Metrics	Value
      Accuracy	84.14%
      Precision	80.54%
      Recall (Sensitivity)	90.03%
      F1-Score	85.02%

      The high Recall (90.03%) is a critical achievement, as it signifies the model’s ability to identify 9 out of 10 anomalies correctly. In the context of satellite health monitoring, failing to detect an anomaly (False Negative) is significantly more detrimental than a false alarm[1,23,25].

      Fig. 4. Prediction outcome breakdown showing the percentage distribution of TP, TN, FP, and FN.

   2. Confusion Matrix Analysis

      The classification results are further validated b the confusion matrix. As shown in the analysis:

      • True Positives (TP): 298 anomalies were correctly identified.
      • True Negatives (TN): 259 normal instances were correctly classified.
      • False Negatives (FN): Only 33 anomalies were missed (10% error rate).
      • False Positives (FP): 72 normal samples were incorrectly flagged as anomalies.

        Fig. 5. Confusion Matrix displaying absolute counts and classification accuracy for normal vs. anomalous instance. This distribution indicates that while the model is slightly aggressive in flagging anomalies (22% False Positive rate), it maintains an exceptionally high safety margin by minimizing missed critical events[1,2,23].

   3. Threshold Optimization and Class Separation

      A significant contribution of this work is the implementation of an Auto-Optimal Threshold. Utilizing the Precision-Recall curve, the system determined an optimal threshold of 0.23.

      The probability distribution analysis reveals that the model successfully shifts the majority of normal telemetry instances toward the 0.0 probability mark, while anomalous sequences exhibit a distinct concentration near 1.0. This clear bifurcation validates that the fusion of BiLSTM and CNN branches effectively extracts unique fault signatures that are often overlapping in raw telemetry data[1,4,36].

      85%, confirming the model’s ability to generalize across unseen telemetry sequences without significant overfitting.

      Fig. 6. Anomaly probability distribution histogram demonstrating clear bifurcation between classes at a 0.23 threshold.

      Fig. 7. Model accuracy curves demonstrating high training and validation performance.

   4. Learning Dynamics

   The training process utilized an Adam optimizer with a learning rate of $3 \times 10^{-4}$ and SMOTE for class balancing. The loss curves demonstrate steady convergence, with training loss reaching near-minimal levels. The validation accuracy stabilized atapproximately

   Fig. 8. Model training and validation loss curves showing steady convergence over 60 epochs.

   Fig. 9. Model accuracy curves demonstrating high training and validation performance

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