Passive Radar Systems for Voluntary National Defense: Leveraging Civilian Infrastructure for Enhanced Situational Awareness

The landscape of national defense is evolving, demanding innovative approaches that complement traditional military capabilities. This report explores the profound potential of passive radar systems, particularly when integrated with existing civilian infrastructure, as a robust and cost-effective solution for voluntary national defense. The inherent strengths of the amateur radio community, with its deep technical expertise and historical commitment to public service, are uniquely positioned to spearhead such initiatives.

I. Introduction: A Legacy of Radio Innovation for National Preparedness

The journey into radio began already in childhood, when we played with a friend’s grandmother’s old tube radio. We stumbled upon mysterious morse code signals and imagined ships in distress out at sea. Later, before ever obtaining an amateur radio license, this playful curiosity evolved into DX-listening — tuning in to distant stations like Radio Luxembourg on 1440 kHz, which broadcast excellent music much like the pirate station Radio Caroline.

The journey then continued by obtaining an amateur radio license (OH4BC) in Finland during the 1980s. This was a period when such activity was not universally accepted, with restrictions or even outright prohibitions in many countries, including Turkey and Albania. This historical context instilled a profound appreciation for the free flow of information and the power of independent communication. Even then, radio amateurs demonstrated a remarkable capacity to gather and disseminate information from around the world, often surpassing what was available through conventional media. This was achieved through diligent listening to Baudot transmissions and, in later years, to satellite feeds and other data traversing the radio waves. The meticulous process of decoding signals, such as the “ryryryryryryryryryry CQ CQ CQ CQ DE OH4BC K K K K” sequence used for baudot frequency locking, and the familiar “The quick brown fox jumps over the lazy dog 1234567890 times” for keyboard testing, highlights the technical ingenuity and persistent dedication characteristic of the amateur radio community.

Memories from the Cold War era, particularly the powerful, often overwhelming, transmissions from the Soviet Union’s “The Buzzer” (UVB-76) (sending URD, URD, URD, URD) or similar stations, serve as a stark reminder of the strategic importance of the radio spectrum and the challenges inherent in operating within it. This era underscored the constant need for vigilance and adaptability in radio communications. The evolution of amateur radio from the mastery of Morse code—a skill honed to High Speed Club standards (my member #1327)—to more modern digital modes, like with Pakratt PK232, and the early 1990s experiments with HF-based TCP/IP nodes while working at a police department, exemplify a continuous engagement with cutting-edge communication technologies for practical, often emergency, purposes. The presence of amateur radio equipment in police departments specifically for “poikkeusolot” (emergency conditions) further solidifies this historical link between the amateur radio community and national resilience. Today, the process of obtaining amateur radio licenses has become significantly easier, no longer requiring Morse code proficiency, which presents a valuable opportunity to broaden participation in radio-based initiatives for public good.

This report delves into the compelling potential of passive radar systems for voluntary national defense. It investigates how existing civilian infrastructure—specifically mobile devices, cellular base stations, and amateur radio equipment—can be innovatively repurposed. These elements can function as “illuminators of opportunity” and distributed sensors, forming a widespread network. The primary focus is on practical applications for detecting airborne objects, such as drone swarms and aircraft, thereby contributing to enhanced situational awareness in emergency or crisis scenarios. The historical context of amateur radio operators, who, even under restrictive conditions, gathered more information than official channels, establishes a compelling precedent for citizen-led intelligence gathering and resilience. This history demonstrates that the capacity and willingness for such contributions are already deeply embedded within this community. This perspective frames the proposal for modern citizen-led surveillance not as a radical new concept, but as a natural evolution of an established tradition, lending it legitimacy and reducing the perceived novelty and risk of citizen involvement in defense. This foundational trust and inherent capability are significant advantages.

II. Passive Radar: Principles and Advantages for Covert Surveillance

Passive radar, also known as passive coherent location (PCL), parasitic radar, or passive surveillance, represents a class of radar systems that detect and track objects by processing reflections from existing, non-cooperative radio sources in the environment.1 Unlike conventional “active” radar systems, which emit their own signals, passive radar operates without a dedicated transmitter, making it inherently covert.1 This fundamental difference provides significant operational advantages.

A typical passive radar system is equipped with two types of receiving channels: a “reference channel” and a “surveillance channel”.1 The reference channel captures the direct signal from the “illuminator of opportunity”—an existing third-party radio source like a broadcast tower or cellular base station. The surveillance channel, conversely, detects echoes of this illuminating signal that have reflected off targets in the environment.3 Target detection and localization are primarily achieved by measuring the time difference of arrival (TDOA) between the direct signal and the reflected signal, which allows for the determination of the bistatic range.1 In addition to bistatic range, passive radar systems typically measure the bistatic Doppler shift of the echo and its direction of arrival (DOA), enabling the calculation of the target’s location, heading, and speed.1

The characteristics of passive radar offer several key advantages particularly relevant for voluntary national defense. First, its covert operation and inherent stealth are paramount. Since the system does not transmit any signals, it remains undetectable to adversaries, providing strong stealth capabilities suitable for military reconnaissance and counter-stealth applications.4 This covertness is especially critical in the context of citizen involvement, as it significantly reduces the risk of exposing individual participants to potential threats. The inherent covertness of passive radar makes it uniquely suited for citizen-led initiatives, as it minimizes personal safety and privacy risks for participants in voluntary defense efforts. This characteristic makes the concept far more practical and appealing for widespread public adoption.

Second, passive radar demonstrates inherent immunity to anti-radiation missiles and is inherently difficult to jam.6 Without an active transmitter to target, it cannot be attacked by anti-radiation missiles. Furthermore, because adversaries are unaware of the specific external signals being exploited by the passive radar, implementing targeted jamming becomes exceedingly difficult. This enhances the system’s resilience in contested electromagnetic environments. Third, the elimination of dedicated onboard transmitters and high-power amplifiers significantly reduces hardware costs compared to conventional active radar systems.2 This cost-effectiveness aligns perfectly with the strategy of leveraging existing, low-cost civilian equipment and infrastructure for defense purposes. The low-cost and non-transmitting nature of passive radar substantially lowers the barrier to entry for citizen participation. It shifts the requirement from specialized, military-grade equipment to readily available consumer electronics and existing networks, making the vision of widespread voluntary national defense more achievable and sustainable. Finally, passive radar’s flexibility and adaptability stem from its ability to utilize various external signal sources, providing a distinct advantage in complex and dynamic environments.7 This flexibility is a key attribute for a distributed, citizen-based network, allowing it to adapt to diverse signal landscapes.

The signal processing workflow in passive radar is intricate, designed to extract minute target returns from a backdrop of strong, continuous interference from the direct signal.1 This demands receiver systems with a low noise figure, high dynamic range, and high linearity.1 Signal conditioning is a crucial preliminary step, involving transmitter-specific processing such as high-quality analog bandpass filtering, channel equalization, and the removal of unwanted structures in digital signals to enhance the quality of the reference signal.1 Following this, adaptive filtering is essential to remove the powerful direct signal from the surveillance channel. This process, akin to active noise control, prevents the sidelobes of the direct signal from obscuring smaller echoes during the subsequent cross-correlation stage.1 After effective clutter rejection, a 2-D cross-correlation is performed between the surveillance and reference signals, with target echoes identified by localizing correlation peaks in the Range-Doppler (RD) map.6 For target detection and tracking, standard radar beamforming techniques, such as amplitude monopulse or more sophisticated adaptive beamforming, are employed with antenna arrays to calculate the direction of arrival (DOA) of echoes.1 Constant False Alarm Rate (CFAR) detection is commonly applied, and multiple detection antennas are often utilized to estimate the target’s angle. The integration of data from multiple coherent processing intervals (CPIs) further aids in associating targets, eliminating false alarms, and constructing accurate trajectories.7

III. WSPR Technology: Global Aircraft Tracking and Beyond

The “MH370: Ground-Breaking Report Reveals Location” report, co-authored by Richard Godfrey, Dr. Hannes Coetzee, and Prof. Simon Maskell, asserts a groundbreaking capability: the tracking of MH370 using WSPRnet technology.8 This report claims that WSPRnet radio signals can reliably detect and track aircraft over vast distances, even across the globe.8 This remarkable feat is achieved by identifying subtle anomalies in the received signal level, received frequency, or frequency drift, which serve as indicators of a disturbance caused by an aircraft.8 The underlying technique ingeniously combines the reflection of radio waves by aircraft, much like conventional radar, with the principles of ionospheric propagation and the unique WSPR protocol.8

WSPRnet functions as a multi-static and multi-frequency system, providing truly global coverage.8 Its extensive network boasts approximately 6 million distinct links between WSPR transmitters and receivers worldwide, with recorded propagation distances often exceeding 3,000 km.8 The high level of detection and reliable tracking achieved by WSPRnet is attributed to several critical factors: the long coherent integration time of WSPRnet receivers, the enhanced radar footprint of modern aircraft in the WSPRnet wavelength bands, and the pervasive global coverage of WSPRnet propagations.8 In the specific case of MH370, the analysis identified 313 anomalies at 130 different points in time, leading to a proposed crash location that lies outside previous search areas.8 While Professor Joe Taylor, the inventor of the WSPR protocol, stated that it was not originally designed for aircraft detection, experts like Richard Godfrey argue compellingly for its utility in this application.8 Independent validation studies, such as those being conducted by Professor Simon Maskell at Liverpool University, are underway to further confirm the efficacy and robustness of this technique.8 WSPRnet serves as a powerful existing model for how a voluntary national defense passive radar system could be organized and operated. It validates the concept of leveraging a global network of amateur enthusiasts for critical, large-scale data collection and analysis, suggesting a pre-existing infrastructure and community for such an endeavor.

Beyond its application in aircraft tracking, WSPR holds broader potential for spectrum monitoring and atmospheric sensing. WSPR (Weak Signal Propagation Reporter) is fundamentally utilized by amateur radio operators to test and visualize RF signal propagation paths.12 It employs a narrowband digital protocol known as MEPT_JT on the HF and MF frequency bands.12 The system leverages computer sound cards for both modulation and digitization, scanning a narrow 200 Hz passband to detect MEPT_JT signals.12 Upon successful decoding of a signal, the program uploads metadata—including callsign, locator, and received signal strength in dBm—to a central database, enabling the recording and visualization of propagation paths.12 The extended duration of WSPR transmissions (110.6 seconds) and its remarkably low occupied bandwidth (around 6 Hz) allow for successful reception at very low signal-to-noise ratios, reportedly around -27 dB.12

The ability of WSPR to detect subtle anomalies in received signal level, frequency, and drift 8 extends its utility beyond mere propagation testing. This capability suggests a broader potential for observing other atmospheric disturbances or objects that interact with radio waves. For instance, VHF passive radar has been successfully used to study the upper atmosphere, facilitating the detection of meteor trails and various ionospheric disturbances.3 While the provided information does not explicitly detail WSPR’s capabilities for environmental monitoring beyond propagation 12, the core principle of identifying signal anomalies caused by the presence of objects or changes in atmospheric conditions aligns directly with the idea of monitoring the entire radio spectrum for “noise” or detecting “signal absorption,” as suggested in the initial inquiry. The MH370 tracking method, which relies on statistical post-processing of metadata and the identification of anomalies in received signal level, frequency, or drift, provides a crucial insight. Professor Maskell’s observation that WSPR, despite being a “noisy sensor,” still demonstrates utility (evidenced by a 67% Receiver Operating Characteristic curve) is particularly significant.8 This implies that even low-fidelity or imperfect data, when aggregated and analyzed statistically across a vast network, can yield substantial insights and detections. This directly supports the concept of monitoring the entire radio spectrum, where seemingly random “noise” could contain valuable patterns when subjected to advanced analytical techniques. This demonstrates that a citizen-based passive radar system does not require individual sensors to be perfectly calibrated or highly precise. Instead, its strength lies in the collective data and the application of advanced statistical and machine learning techniques to extract meaningful patterns from what might otherwise appear as noise or weak signals, making the system more robust and accessible.

IV. Leveraging Cellular Networks for Airborne Object Detection

The ubiquitous presence of cellular networks presents a significant opportunity for passive radar applications. Passive radar systems have been specifically developed to exploit cellular phone base stations as “illuminators of opportunity”.1

4G LTE signals are considered strong candidates for passive radar due to their favorable characteristics. They offer bandwidths ranging from 1.4 to 20 MHz, which translates to high range and velocity resolution.4 Their wide frequency band (800–3500 MHz) and global coverage make passive radar configurations both feasible and accessible.4 Furthermore, LTE utilizes Orthogonal Frequency Division Multiple Access (OFDMA), and its ambiguity function produces lower sidelobes, which is advantageous for target detection.4 The digital signaling in LTE, coupled with its low error-rate decoding, makes it well-suited for passive radar applications. LTE’s well-defined cellular standards, including the presence of pilot signals for synchronization and channel equalization, facilitate the creation of effective matched filters for accurate range and Doppler estimation.4 TD-LTE, a specific 4G standard, offers additional benefits such as large coverage, full-time operation, and precise synchronization through GPS timing.13 Its use of MIMO-OFDM (Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing) technology enables effective multipath clutter mitigation and enhanced detection performance.13

5G radio signals are actively being researched for their potential in sensing the presence or proximity of Unmanned Aerial Vehicles (UAVs) in restricted areas, such as airports and military bases.7 A key component for passive radar illumination in 5G networks is the Synchronization Signal Block (SSB).14 SSBs are periodic, well-defined blocks of Orthogonal Frequency Division Multiplexing (OFDM) symbols, comprising Primary Synchronization Signals (PSS), Secondary Synchronization Signals (SSS), and Physical Broadcast Channel (PBCH) symbols.14 These SSBs are periodically swept across the entire coverage area of a base station, making them an “always-on” signal.14 This characteristic is particularly advantageous for passive radar, as it ensures a consistent illumination source even when there is a lack of data transmission in the network.16

Looking ahead, 6G networks are expected to integrate sensing and imaging capabilities directly into their core design.7 This future generation of wireless communication will utilize high-frequency signals with very short wavelengths, allowing them to double as radar-like sensors for detecting objects, movements, and even materials in the environment.17

The detection of aircraft and drone swarms using cellular signals is a growing area of research. Passive radio-frequency (RF) detection can identify UAVs by analyzing their communication signals, including WiFi fingerprints and transmitted spectral patterns.7 While millimeter-wave bands (e.g., 60 GHz) are often preferred for UAV detection due to their ability to achieve a significantly higher Radar Cross-Section (RCS) 7, signals from 3G, 4G LTE, and 5G networks have also demonstrated effective UAV detection capabilities.7 The initial inquiry specifically raised the question of whether mobile phones and their base stations, by continuously measuring signal strength and other values, could function as “absorbance meters” to reveal the presence of drone swarms, aircraft, or other airborne objects through signal absorption. Research indicates that variations in Received Signal Strength Indicator (RSSI) caused by motion and physical obstacles can indeed be harnessed for passive motion detection.18 Physical obstructions, such as an object passing through the signal path, can cause signal power drops due to attenuation, shadowing, and absorption.18 For drone detection specifically, a novel simulation framework has been developed that uses RSSI to identify “alien agent drones” by their distinct signal characteristics compared to an authorized swarm, enabling real-time threat detection.22 This “absorbance meter” approach broadens the scope of passive detection beyond traditional reflection-based radar. It suggests that a network of mobile devices, continuously monitoring local signal strength from multiple base stations, could create a “radio transparency map” of the airspace. Anomalies in this map, such as localized drops in signal strength across multiple receivers, could indicate the presence of objects, even if they do not produce strong radar echoes. This could be a powerful tool for detecting drone swarms or stealthy aircraft, where traditional reflection-based radar might struggle.

Processing cellular signals for passive radar presents unique challenges and requires advanced techniques. Passive radar receivers must contend with detecting very small target returns amidst the strong, continuous interference from the direct signal.1 This necessitates receiver systems with exceptionally low noise figures, high dynamic range, and high linearity.1 Effective clutter suppression is paramount, as strong direct waves and multipath clutter signals can easily mask the faint target echoes, leading to high ambiguity floors and false alarms.7 Algorithms such as the Extensive Cancellation Algorithm (ECA), Generalized Subband Cancellation (GSC), and Matching Pursuit (MP) are employed to suppress this clutter.7

A critical step in the processing chain is the accurate recovery of a high-quality reference signal from the illuminating source.4 For LTE signals, this involves precise time and frequency synchronization, channel equalization utilizing pilot signals, and a reverse processing procedure to reconstruct the original transmitted signal.4 In the context of 5G, the Synchronization Signal Block (SSB) can be detected, decoded, and digitally re-synthesized to create a perfect time-domain reference signal.16 A particular challenge with 5G SSBs is velocity ambiguity, as their periodicity can limit the maximum unambiguous bistatic velocity. Solutions to this involve decreasing the SSB periodicity or employing multiple Range-Doppler maps for single-target scenarios.16 Furthermore, 5G’s advanced beamforming, while efficient for communication, can pose a challenge for passive radar. Transmitting beams are often focused at random terminal locations using precoders, which can create “gaps of weak signal” in surveillance areas, limiting their availability as continuous illuminators.14 However, the periodic sweeping of SSB beams, some of which can be directed towards higher altitudes, offers a potential solution by providing a consistent, albeit lower-power, illumination source for surveillance.14 The transition to 5G thus presents both a challenge (less consistent high-power illumination from data beams) and an opportunity (reliable, periodic SSB illumination). The design of a 5G-based passive radar system would need to specifically leverage the SSB, potentially by adapting its processing to the SSB’s characteristics and focusing on the “surveillance beams” directed at higher altitudes. This requires sophisticated signal processing to extract useful information from these specific signals. Future advancements also point towards multi-signal fusion, combining various types of passive signals (e.g., DVB-T for long-range early warning and WiFi for high-precision short-range tracking) and integrating non-electromagnetic signals like video and audio data to optimize UAV detection performance.7

V. Mobile Devices as Distributed Passive Radar Sensors

Mobile phones, despite their primary function as communication devices, possess capabilities that make them intriguing candidates for distributed passive radar sensors. Mobile phones measure signal strength in decibel milliwatts (dBm), which provides a more accurate assessment than the often-unreliable signal bars displayed on the phone’s interface.23 A signal strength closer to 0 dBm indicates a stronger cellular signal; for example, -77 dBm is a stronger signal than -86 dBm.23 Both Apple iOS and Android devices offer hidden field test modes that can display these actual dBm values, allowing for more precise measurement of cellular signal strength.23 Signal strength is influenced by several factors, including the distance from the cell tower, physical obstructions such as building materials, terrain, and dense vegetation, as well as weather conditions and high network traffic.23 The strength of an RF signal is known to be inversely proportional to the distance it has traveled, a fundamental principle that can be leveraged to derive distance information from Received Signal Strength Indicator (RSSI) measurements.19 However, it is important to note that RSSI fingerprinting, while useful, can be unreliable at low bandwidths.18

A significant hurdle in utilizing smartphones as direct passive radar sensors lies in the limitations of Android APIs for raw cellular signal access. Direct access to raw cellular baseband data or low-level RF signals from a smartphone’s internal radio is severely restricted by manufacturers and operating systems.25 Smartphone hardware is highly optimized for specific frequency bands and operates with complex, proprietary firmware.25 Achieving full Software-Defined Radio (SDR) functionality would necessitate modifying the Layer 1 (physical layer) firmware, which is generally not feasible for consumer devices.25 Android’s CellInfo API, for instance, provides only limited data, primarily for the serving tower, with crucial information often stripped out for non-serving (camped) towers. Furthermore, the Physical Cell Identity (PCI), while available, is frequently duplicated across different cell sites, and the true Global Cell Identity (GCI) is not directly accessible via the API.27 Google’s Android Management API is explicitly restricted to commercial Enterprise Mobility Management (EMM) developers and Device Trust providers, and its usage is prohibited for “device or user monitoring, fingerprinting, or eavesdropping independent of enterprise management”.26 This policy explicitly limits the direct use of Android APIs for passive surveillance applications.

Given these internal API limitations, alternative approaches become necessary. Leveraging external Software-Defined Radio (SDR) dongles, such as RTL-SDRs, connected to Android smartphones via OTG adapters, presents a viable and practical solution.25 These RTL-SDR dongles are remarkably affordable, costing as little as $20-$70, and are capable of receiving a wide range of frequencies.25 Drivers are readily available that allow Android phones to interface with and utilize these dongles.30 Existing Android applications like “ADS-B Radar (RTL-SDR)” 31 and “PassiveRadar” 32 already demonstrate the feasibility of using external SDRs with smartphones for applications such as aircraft tracking (ADS-B) or for implementing general passive radar principles based on ambiguity functions.

For citizen science application development, despite the direct API limitations, a smartphone app could still collect valuable higher-level signal data. This could involve analyzing RSSI variations from the phone’s internal Wi-Fi or cellular modem (if permitted by the operating system), or more comprehensively, processing data streamed from external SDRs. Platforms like SPOTTERON Citizen Science 33 offer customizable solutions for developing citizen science apps, providing features such as data input with GEO coordinates and pictures, interactive maps, and community functionalities. This demonstrates a robust framework for developing such an application. The RADAR-CNS app 34, while focused on health monitoring using wearable devices, illustrates the broader concept of a distributed data collection application. An app could also periodically retrieve and display amateur radio signal propagation reports from WSPRnet 35, indicating current propagation conditions and potentially identifying anomalies relevant to passive radar. A critical aspect of any such citizen science initiative is ensuring privacy and data safety.20 RSSI measurements are inherently anonymous because they do not decode information, thereby avoiding privacy concerns related to identifying individuals.20 The technical limitations of smartphone APIs mean that a citizen-led passive radar application would likely need to rely on external SDR dongles for full spectrum analysis or focus on aggregated, anonymized RSSI data from the phone’s internal radios. This necessitates a design that either seamlessly integrates external hardware or develops sophisticated algorithms to extract meaningful patterns from limited internal sensor data, while strictly adhering to privacy-by-design principles. The success of such an endeavor hinges on making external hardware accessible and the software user-friendly, or on demonstrating the utility of the more limited internal data. This represents a paradigm shift: from the smartphone primarily collecting user-specific data to its role as a powerful environmental sensor.

VI. Elevated Sensing: The Role of Tethered Drones in Enhancing Coverage

Elevating passive radar receivers significantly enhances their operational capabilities, particularly for detecting low-altitude targets like drones and aircraft. Placing receivers at higher altitudes dramatically improves the line-of-sight (LoS) and extends coverage by effectively eliminating ground clutter.1 While typical terrestrial base stations are positioned at heights of around 10-20 meters in urban environments, aerial base stations (ABSs) or Unmanned Aerial Vehicles (UAVs) can hover at altitudes up to 100-120 meters, achieving substantially broader coverage and reducing interference from ground-level obstacles.37 This elevated perspective also contributes to more accurate alerts by minimizing clutter interference.36

Tethered drones are multirotor aerial platforms that are physically connected to a ground station via a cable.38 This tether provides several critical advantages for persistent aerial platforms in a passive radar context. The primary benefit is unlimited flight time; the tether continuously supplies power from the ground station, eliminating the need for disruptive battery swaps and enabling indefinite flight durations, potentially 24/7 operation.38 This continuous operation is crucial for persistent surveillance missions. Furthermore, the tether ensures robust communication. It often integrates optical fibers, providing a wired backhaul connection that enables high-bandwidth, secure, interference-resistant, and low-latency data transfer between the drone and the ground station.38 This “Data-over-Power” (DoP) or integrated fiber capability is essential for real-time processing and analysis of sensor data collected from the elevated platform.

The removal of heavy onboard batteries also significantly increases the drone’s payload capacity, allowing it to carry heavier and more sophisticated sensor equipment.36 Tethered drones can typically carry payloads ranging from 10 to 30 pounds.36 The physical tether can also contribute to the drone’s stability, which is beneficial for maintaining clear imaging and consistent RF sensing.42 Many tethered drone systems are designed for rapid deployment, with some capable of being assembled and launched within minutes.39 Moreover, these platforms are engineered to operate reliably in harsh weather conditions and GPS-denied or contested environments, enhancing their utility in diverse operational scenarios.40

The payload-agnostic design common to many tethered drones 40 allows for flexible integration of various sensors. A mobile phone, perhaps augmented with an external SDR dongle, could be mounted as a low-cost, distributed receiver on a tethered drone. This setup would leverage the phone’s existing processing power and connectivity for initial data handling and transmission to a central processing unit. Alternatively, dedicated SDR receivers specifically designed for passive radar applications, such as those mentioned in research 31, could be integrated as payloads for more specialized and higher-performance data collection. The tether’s inherent ability to transfer data 38 is indispensable for the real-time backhaul of collected RF data from the elevated receiver to a ground station for comprehensive processing and analysis. Tethered drones are not merely a means to elevate a receiver; they represent a significant tactical advantage for passive radar, particularly for counter-drone operations. Their persistence and ability to overcome ground-based line-of-sight issues make them ideal for establishing a continuous “RF fence” or surveillance bubble over critical areas or borders, thereby substantially enhancing the detection capabilities of a voluntary national defense network against low-flying threats.

VII. Citizen Science and Voluntary National Defense: A Collaborative Framework

The concept of citizen science, which involves public participation in scientific discovery and research to address real-world problems 44, aligns exceptionally well with the objectives of voluntary national defense. This model offers a framework for integrating civilian capabilities into broader security efforts.

Existing models demonstrate the viability and effectiveness of citizen participation in national defense. The U.S. Army Military Auxiliary Radio System (AMARS) is an exemplary group of dedicated citizen volunteers who support the Department of Defense (DoD) in various critical circumstances, including complex catastrophes and cyber-denied or impaired conditions.45 AMARS members, who are licensed amateur radio operators, provide essential contingency communications support and are expected to possess expertise in RF communications and information technology, including cybersecurity.45 Similarly, the Radio Amateur Civil Emergency Service (RACES), a protocol established by FEMA and the FCC, trains Auxiliary Communications Service (ACS) volunteers.46 RACES operators are licensed radio amateurs, certified by civil defense agencies, and are authorized to communicate on amateur frequencies during drills, exercises, and actual emergencies, activated by local, county, and state jurisdictions.46 Notably, they are the only amateur radio operators authorized to transmit during declared emergencies when the President of the United States specifically invokes the War Powers Act.46 These programs collectively demonstrate a proven and robust framework for integrating citizen volunteers into national defense communications and support structures.

However, any citizen-led surveillance initiative must navigate critical legal and ethical considerations. Government use of surveillance technologies must strictly adhere to democratic principles, human rights, and fundamental freedoms, consistent with international obligations.47 Robust safeguards are essential for the collection, handling, and disclosure of any material obtained through surveillance to protect individual privacy and personal data.47 Surveillance technologies should not be used to unjustifiably interfere with freedom of expression, discourage the exercise of human rights, or perpetuate discrimination.47 Furthermore, effective oversight, transparency, and redress processes must be clearly defined, regularly reviewed for unintended consequences, and consistently enforced.47 Programs like the U.S. National Security Agency’s (NSA) PRISM 48 and other intelligence community activities 49 serve as cautionary examples, highlighting the potential for mass surveillance and the collection of vast amounts of data, including from citizens, which raises significant privacy concerns. While these programs operate under specific legal authorities (e.g., FISA, Executive Order 12333) and are subject to oversight, criticisms persist regarding their scope and impact on privacy.48

For citizen science initiatives, particularly those involving data collection, prioritizing privacy and data safety is paramount.33 The use of anonymized data collection methods, such as RSSI measurements that do not decode information and thus prevent individual identification, offers a key advantage in mitigating privacy concerns.20 Crowdsourced security initiatives 51 also provide valuable models for managing community contributions in sensitive areas, emphasizing ethical hacking and responsible disclosure. For a voluntary national defense passive radar system to gain public trust and widespread adoption, it must be built on a foundation of explicit legal protections and ethical guidelines. This includes clear policies on data collection, storage, access, and anonymization, as well as independent oversight. Without these safeguards, the initiative risks being perceived as an invasion of privacy rather than a patriotic contribution, undermining its very purpose. This is a critical non-technical challenge that must be addressed proactively.

A structured citizen science initiative for voluntary national defense could be proposed, building upon the organizational successes of AMARS and RACES. This would involve developing a dedicated Android application (and potentially iOS) for data collection, leveraging either external SDRs or carefully designed RSSI analysis from internal phone sensors. The application would focus on collecting anonymized RF environmental data, ensuring no personally identifiable information is gathered. The initiative would require clear guidelines for participation, robust data submission protocols, and a centralized, secure data processing and analysis hub. A strong emphasis would be placed on transparency regarding data use, strict privacy safeguards, and a robust oversight mechanism to ensure continuous adherence to legal and ethical standards. Comprehensive training for citizen participants, similar to the rigorous programs provided by MARS 45, would be crucial for maintaining data quality and ensuring operational consistency across the network.

VIII. Challenges and Future Directions

The implementation of a widespread passive radar system for voluntary national defense, leveraging civilian infrastructure, presents a multifaceted array of challenges that span technical, operational, and regulatory domains.

Technical Challenges are significant. Passive radar systems inherently face the challenge of detecting very weak target echoes in the presence of extremely strong direct signals and pervasive multipath clutter.1 Developing and implementing effective adaptive filtering and clutter suppression algorithms is therefore essential to isolate the faint target signals.1 Achieving precise target localization with passive radar typically requires accurate knowledge of transmitter locations 6 and, ideally, data from multiple receiver-transmitter pairs.6 While Time Difference of Arrival (TDOA) and Direction of Arrival (DOA) techniques can be employed, achieving high precision in dynamic and complex environments remains a considerable challenge.1 A distributed network of citizen sensors would generate massive amounts of RF data, necessitating robust infrastructure and advanced algorithms for real-time processing, transmission, and storage.7 Furthermore, distinguishing genuine target reflections or absorptions from other environmental factors (such as weather, terrain, or other radio signals) or from false alarms is a complex signal processing problem.7 For mobile passive radar receivers, such as those deployed on tethered drones, recovering a stable and accurate reference signal can be difficult due to platform motion-induced Doppler shifts.7 Finally, integrating data from diverse and heterogeneous sources—including WSPR, cellular RSSI, external SDRs, and potentially visual or audio data from drones—requires sophisticated multi-signal fusion techniques to create a coherent and comprehensive picture.7

Operational Challenges are equally critical. Establishing a centralized or distributed system capable of collecting, aggregating, and analyzing data from numerous citizen sensors in real-time is a substantial undertaking. This requires significant investment in backend infrastructure and data pipelines. Ensuring reliable and secure data transmission from citizen devices to processing centers is crucial, especially during emergency conditions when traditional communication networks might be degraded or unavailable. Organizing, training, and maintaining a large, distributed network of volunteers demands robust management and communication structures, drawing lessons from existing successful programs like MARS and RACES. Maintaining quality control and validating the data submitted by volunteers is paramount to avoid false positives or missed detections, which could have serious implications in a defense context.

Regulatory and Policy Considerations form a foundational layer for such an initiative. Developing clear legal frameworks that permit and regulate citizen involvement in defense-related surveillance is essential, carefully balancing national security needs with individual privacy rights. Addressing potential international implications, particularly concerning cross-border signal reception and data sharing, will require diplomatic engagement and adherence to international law. Establishing clear lines of authority and responsibility between civilian volunteers and official defense agencies is also crucial to ensure effective coordination and accountability.

Future Research Avenues and Technological Advancements hold promise for overcoming these challenges. Developing advanced Artificial Intelligence (AI) and Machine Learning (ML) algorithms is critical for sophisticated pattern recognition, anomaly detection, and target classification from complex and noisy RF data. This includes distinguishing between different types of airborne objects, such as drones versus birds. Exploring how future 6G networks, with their inherent Integrated Sensing and Communication (ISAC) capabilities, can be directly leveraged for passive radar without significant modification is another promising direction.7 Research into distributed aperture radar concepts, where a network of geographically dispersed, low-cost receivers collectively forms a “virtual” large aperture, could lead to enhanced detection and imaging capabilities. Finally, implementing edge computing for on-device processing could reduce bandwidth requirements and latency for data transmission by performing initial signal processing and anomaly detection directly on the mobile device itself. The success of a citizen-driven passive radar system will depend on abstracting away the technical complexity from the end-user. The Android application would need to be extremely user-friendly, focusing on simple data collection and transmission, while the heavy lifting of signal processing, anomaly detection, and target identification occurs on powerful, centralized servers utilizing advanced AI/ML. This model allows for both widespread participation and high-fidelity results, bridging the gap between amateur capability and professional-grade defense requirements.

IX. Conclusion: Empowering Citizens in Modern Defense

Passive radar, by leveraging existing civilian infrastructure such as mobile networks, amateur radio equipment, and tethered drones, offers a compelling and cost-effective approach to significantly enhance national defense capabilities, particularly for airborne object detection. The demonstrated ability of WSPR technology to track aircraft over long distances through statistical analysis of signal anomalies provides a scalable and proven model for citizen involvement in large-scale surveillance. Cellular signals, especially 4G LTE and the 5G Synchronization Signal Block (SSB), offer ubiquitous illumination sources, and their unique characteristics can be exploited for detecting aircraft and drone swarms, potentially through both traditional reflection-based methods and novel signal absorption/attenuation analysis. While direct access to raw cellular signals on smartphones is currently limited by API restrictions, the use of external Software-Defined Radio (SDR) dongles presents a viable and accessible pathway for citizen participation in data collection, supported by user-friendly Android applications. Furthermore, tethered drones serve as invaluable platforms for elevating receivers, effectively overcoming ground-based line-of-sight limitations and providing persistent, high-bandwidth data collection from critical airspace.

The amateur radio community, with its deep technical expertise, long-standing commitment to public service, and existing global networks (such as WSPRnet, the U.S. Army Military Auxiliary Radio System (AMARS), and the Radio Amateur Civil Emergency Service (RACES)), represents an unparalleled asset for developing and deploying such a voluntary national defense system. Their intrinsic motivation and technical prowess are key to the success and sustainability of such an endeavor.

To realize this potential, several recommendations are put forth:

  • Pilot Programs: Initiate targeted pilot programs to test the practical feasibility of integrating mobile device-based passive radar receivers (utilizing external SDRs or advanced RSSI analysis) and tethered drone platforms in specific geographical areas.

  • Technology Development: Invest strategically in research and development of advanced signal processing algorithms, with a particular focus on exploiting cellular signals (including SSB and absorption effects), and in Artificial Intelligence/Machine Learning for automated anomaly detection and precise target classification.

  • Citizen Science Platform: Develop a secure, user-friendly citizen science application and a robust backend infrastructure for efficient data aggregation, processing, and visualization, ensuring that data anonymization and privacy-by-design principles are strictly adhered to from the outset.

  • Framework and Training: Establish a formal collaborative framework with clear operational protocols, comprehensive legal guidelines, and thorough training programs for citizen volunteers, drawing upon the successful organizational models of AMARS and RACES, to ensure both data quality and operational security.

  • Policy Dialogue: Engage in proactive and open policy discussions to address the complex legal and ethical considerations surrounding citizen involvement in defense initiatives, fostering public trust and securing the necessary regulatory support for these vital voluntary national defense efforts.

Note! When discussing the functionality of AI, the term “user’s query” refers to the input or request provided by the user to the AI system. In the context of this report, a “user’s query” specifically denotes my (the author’s) prompts, questions, or instructions given to the generative AI model during the research and drafting process. It’s the mechanism through which I guided the AI to retrieve information, analyze data, and generate content.

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Process: Initial idea -> LM Studio -> Several local LLM -> Google Gemini 2.5 DeepResearch