Staying Found: Collaborative Localization Under Attack

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Author: Denis Avetisyan

New research details a robust method for multi-agent systems to maintain accurate positioning even when facing malicious interference to radio signals.

This paper presents a resilient collaborative localization framework leveraging multi-hypotheses navigation and covariance intersection to detect and mitigate the impact of spoofed sensor data.

Maintaining reliable localization in multi-agent systems is increasingly challenged by the growing threat of cyberattacks that compromise sensor data. This paper, ‘Multi-Hypotheses Navigation in Collaborative Localization subject to Cyber Attacks’, addresses this vulnerability through a novel approach to resilient navigation. By leveraging multi-hypotheses tracking and covariance intersection, the proposed method enables agents to collaboratively identify and mitigate the impact of spoofed radio frequency measurements. Does this framework represent a viable path toward robust, secure multi-agent systems operating in contested environments?

Navigating Uncertainty: The Foundations of Robust Localization

Contemporary technological systems are profoundly reliant on precise localization, a trend dramatically amplified within the growing domain of multi-agent systems. From autonomous vehicles coordinating routes to robotic swarms performing complex tasks, these systems depend on knowing the position and orientation of individual components with unwavering accuracy. This creates a critical dependency, as even minor inaccuracies or disruptions in localization data can cascade into significant functional failures or compromised operational integrity. The increasing sophistication of these systems, coupled with their deployment in safety-critical applications, elevates the importance of robust and reliable localization techniques, making it a foundational element for ensuring dependable performance and preventing potentially hazardous outcomes.

Radio frequency (RF) measurements form the backbone of many modern localization systems, enabling devices to determine position and orientation through signal strength or time-of-flight estimations. However, this very reliance introduces a significant vulnerability: RF signals are readily spoofable. An attacker can inject false RF signals, mimicking legitimate beacons or altering existing ones, thereby manipulating the perceived location of a target system. This isn’t merely a theoretical concern; successful RF spoofing can have critical consequences, ranging from navigation errors in autonomous vehicles and robotic systems to the disruption of critical infrastructure dependent on precise location data. The inherent broadcast nature of RF, combined with the relative ease with which signals can be generated and transmitted, makes comprehensive defense a complex engineering challenge, demanding robust authentication and validation mechanisms to ensure data integrity and system security.

Conventional localization techniques, such as trilateration based on received signal strength or time-of-arrival estimations, frequently assume a trustworthy environment, rendering them vulnerable to even simple spoofing attacks. These methods typically lack the inherent mechanisms to differentiate between genuine signals and maliciously crafted ones designed to manipulate position estimates. Consequently, a compromised agent can be induced to believe it occupies a false location, potentially disrupting coordinated multi-agent operations or causing navigational errors. This susceptibility underscores the urgent need for defensive strategies – including signal authentication, anomaly detection, and robust filtering algorithms – capable of verifying signal integrity and mitigating the impact of adversarial interference on localization accuracy and system reliability. Developing these innovative defenses is crucial for ensuring the safe and effective deployment of localized systems in increasingly contested environments.

Multi-Hypotheses Navigation: A Systemic Approach to State Estimation

Multi-Hypotheses Navigation (MHN) addresses the challenges posed by uncertainty in state estimation by simultaneously considering multiple plausible agent states rather than converging on a single, potentially inaccurate, solution. This approach diverges from traditional filtering methods, such as Kalman filtering, which rely on a single best estimate. By maintaining a probability distribution across several hypotheses, MHN provides a more complete representation of possible states, increasing robustness in ambiguous or noisy environments. Each hypothesis encapsulates a potential trajectory and associated sensor measurements, allowing the system to continue functioning even when individual sensor data is unreliable or conflicting. The system then evaluates the likelihood of each hypothesis based on incoming data, pruning less probable states while retaining a diverse set of possibilities until sufficient information resolves the ambiguity.

Tagging of Hypotheses within a Multi-Hypotheses Navigation (MHN) system involves assigning descriptive labels, or tags, to each maintained agent state possibility. These tags encapsulate relevant information regarding the hypothesis, such as the source of the data supporting it, the estimated confidence level, or specific environmental characteristics. This categorization facilitates efficient management of multiple hypotheses by enabling prioritized evaluation and pruning of less probable states. The tagging process allows the system to quickly identify and focus on the most relevant hypotheses during decision-making, improving computational efficiency and the overall robustness of state estimation, particularly in ambiguous or noisy environments. Tagging also supports explainability by providing a traceable history of the evidence supporting each hypothesis.

Accurate agent state estimation is critical for effective Multi-Hypotheses Navigation (MHN) as it directly impacts the validity of maintained hypotheses. This estimation process relies on sensor fusion, combining data from multiple sources to create a comprehensive and reliable representation of the agent’s position, velocity, and orientation. Inertial Measurement Unit (IMU) measurements – specifically accelerations and angular velocities – are a core component of this fusion, providing high-frequency, short-term state updates. However, IMU data is susceptible to drift and bias; therefore, it is typically integrated with data from other sensors, such as cameras or LiDAR, through filtering techniques like Kalman filtering or particle filtering, to correct for these errors and maintain a consistent and accurate agent state estimate over time. The quality of this fused data directly determines the ability of the MHN system to effectively differentiate between plausible and implausible hypotheses.

Pruning the Improbable: Geometric Reduction for Efficient Localization

Geometric reduction techniques minimize computational demands by systematically eliminating hypotheses that are geometrically improbable. These methods commonly utilize Convex Hull algorithms to define the boundaries of plausible states; any hypothesis falling outside this hull is discarded. Distance tests, often employing metrics like Euclidean or Manhattan distance, further refine this filtering process by evaluating the proximity of a hypothesis to known valid states or constraints. By focusing computation on a reduced hypothesis space, these techniques significantly improve the efficiency of state estimation and search algorithms, particularly in high-dimensional problem spaces where the number of possible hypotheses is substantial. The effectiveness of these methods relies on the ability to accurately define the convex hull and select appropriate distance metrics relevant to the specific problem domain.

The Ramer-Douglas-Peucker algorithm is an iterative line simplification algorithm commonly used as a pre-processing step for geometric reduction techniques. It operates by recursively removing points from a curve or line that fall within a specified tolerance distance from the simplified line segment connecting the endpoints. This tolerance, often denoted as $\epsilon$, defines the maximum allowable deviation. By reducing the number of data points while preserving the essential shape, the algorithm significantly decreases the computational burden associated with subsequent geometric filters like convex hull calculations or distance tests. The algorithm’s efficiency stems from its ability to reduce data complexity without substantial information loss, thereby streamlining the hypothesis space for more effective pruning.

Spectral clustering utilizes the eigenvalues of a similarity matrix to partition a dataset into distinct groups. The algorithm constructs a graph representing the hypothesis space, where nodes represent hypotheses and edge weights denote similarity. Applying spectral decomposition to the graph’s Laplacian matrix yields eigenvalues and eigenvectors; the eigenvectors corresponding to the smallest eigenvalues capture the structure of the data. The Eigengap Heuristic identifies optimal cluster numbers by searching for significant gaps between consecutive eigenvalues; a large eigengap suggests a natural separation in the data, indicating the appropriate number of clusters. This allows for efficient partitioning of the hypothesis space, focusing computational resources on the most probable states within each cluster and reducing the search space for optimal solutions. The resulting clusters represent regions of high hypothesis density, effectively guiding the search process.

Ensuring Integrity: Validation and Refinement of Localized States

Robust localization systems must contend with potentially malicious or accidental data corruption, and outlier detection serves as a critical first line of defense. These methods rigorously examine incoming sensor measurements, flagging data points that deviate significantly from expected values – these are termed measurement outliers. Such anomalies could stem from genuine sensor failures, but are equally indicative of more insidious attacks, like GPS spoofing where false signals are deliberately transmitted to mislead the system. Upon identifying a measurement outlier, defensive mechanisms are automatically triggered; these can range from temporarily disregarding the suspect data, to initiating more comprehensive diagnostic tests, or even activating fail-safe protocols to ensure continued, albeit potentially degraded, operation. This proactive approach minimizes the risk of compromised localization estimates and maintains system integrity even in the face of adversarial conditions.

Covariance Intersection (CI) offers a powerful approach to sensor fusion, particularly in scenarios where individual state estimates may be unreliable or compromised. Unlike traditional Kalman filtering which can be overly confident in a single source, CI explicitly models and combines the uncertainty associated with each estimate. This is achieved by intersecting the covariance matrices of the individual state predictions, effectively shrinking the combined uncertainty to the region where all estimates agree. The resulting fused state represents a conservative, yet robust, assessment, minimizing the impact of outliers or malicious data injections. By prioritizing the consistency of the combined uncertainty, CI effectively dampens the influence of potentially spoofed or faulty sensor readings, providing a more trustworthy and resilient Agent State estimate even in adversarial environments. This method doesn’t require knowledge of the noise statistics of each sensor, making it adaptable to complex and dynamic systems where precise calibration is difficult to maintain.

Assessing the reliability of a localized state estimate requires quantifiable metrics, and research demonstrates the effectiveness of the Normalized Estimation Error Squared ($NEES$) in this regard. This metric provides a numerical indication of consistency between the estimated state and actual measurements, revealing how well the system aligns with expected values. Notably, results indicate that even in the presence of an attack – such as GPS spoofing – the $NEES$ maintains consistency for the correct hypothesis tag, effectively distinguishing between legitimate and malicious signals. This consistent error signature allows the system to confidently identify and mitigate the attack while continuing to provide a reliable location estimate, highlighting the metric’s utility in ensuring localization integrity and robust navigation.

The presented research prioritizes systemic robustness, a design philosophy echoing the need for simplicity in complex systems. The collaborative localization method, employing multi-hypotheses navigation and covariance intersection, doesn’t attempt to eliminate uncertainty-a futile effort-but rather to manage it through distributed estimation. This approach inherently acknowledges that complete knowledge is unattainable, and focuses instead on building a resilient architecture. As John Locke observed, “All knowledge is ultimately based on perception.” This paper demonstrates a similar tenet; by fusing multiple, potentially imperfect, sensor readings, the system builds a more reliable, if not absolute, understanding of its environment, effectively mitigating the impact of malicious spoofing attacks. The architecture’s strength lies not in preventing all errors, but in gracefully handling them.

Where the Path Leads

The presented work addresses a critical, if predictable, tension: the increasing reliance of multi-agent systems on radio frequency measurements renders them uniquely vulnerable to spoofing attacks. While covariance intersection offers a statistically grounded approach to mitigating these threats, it merely shifts the locus of concern. The system does not eliminate uncertainty; it redistributes it, creating new, potentially subtle, failure modes. This is, of course, inherent to all optimization – every solved problem introduces a novel set of constraints.

Future work must move beyond reactive defenses. The architecture of resilience lies not in simply detecting inconsistencies, but in designing systems inherently robust to misinformation. This necessitates exploring alternative sensing modalities, incorporating physical constraints into the estimation process, and perhaps most importantly, developing a deeper understanding of the information topology within the collaborative network. How does the structure of communication dictate the system’s susceptibility to attack?

Ultimately, the pursuit of perfect localization is a chimera. The true challenge lies in designing systems that gracefully degrade under uncertainty, prioritizing mission success over absolute positional knowledge. The system’s behavior over time-its adaptability and resilience-will prove far more valuable than any initial accuracy.

Original article: https://arxiv.org/pdf/2511.21432.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-11-29 09:34