[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124339-en":3,"doc-seo-124339-105":30,"detail-sidebar-cat-0-en-105":83},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124339,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",6,"Technology","Machine Learning for Interference Detection and Mitigation on Deep Space Telecom Signals","Detecting anomalies and interference is a critical capability for modern flight radios and ground support equipment. Software-defined radios make real-time monitoring and mitigation feasible by enabling adaptive frequency-band processing. Automated anomaly detection on SDRs can improve spacecraft and ground-station testing by flagging abnormal waveforms and likely interference in communication channels. The work compares machine learning with classical signal processing for SDR-based anomaly detection under limited interference assumptions, then studies mitigation by removing detected anomalies from the received signal.","SSC25-RAI-07  \nMachine Learning for Interference Detection and Mitigation on Deep Space  \nTelecom Signals  \nAdh´emar de Senneville  \nENS Paris-Saclay  \n4 AV des Sciences, 91190 Gif-sur-Yvette  \n[adhemar.de](adhemar.de) [senneville@ens-paris-saclay.fr](senneville@ens-paris-saclay.fr)  \nDennis Ogbe and Zaid Towfic  \nJet Propulsion Laboratory, California Institute of Technology  \n4800 Oak Grove Dr., Pasadena, CA 91109  \n{dennis.ogbe, [zaid.j.towfic](zaid.j.towfic}@jpl.nasa.gov)[}](zaid.j.towfic}@jpl.nasa.gov)[@jpl.nasa.gov](zaid.j.towfic}@jpl.nasa.gov)  \nABSTRACT  \nDetecting anomalies and interference is an essential capability of contemporary flight radios and ground support equipment. The advent of software-defined radios (SDRs) has made this task feasible. These radios allow real-time monitoring and potential mitigation of interference by adjusting frequency bands. This feature is useful as spacecraft components degrade and interference from other instruments may disrupt communications. The implementation of automated anomaly detection systems on SDRs can enhance both spacecraft and ground station testing by identifying abnormal waveforms and potential interference in communication channels. First, this work investigates how machine learning compares against classical signal processing approaches for the anomaly detection task on SDR signals, when no assumptions can be made about the interference signal. By adding more realistic assumptions to the modeled signals, we show cases where classical signal processing methods start to fail in comparison to machine learning approaches. In the second part, we explore the more general problem of mitigating the anomaly by removing it from the received signal. A classical approach (independent component analysis) is shown to be effective if the problem has more receivers than signal sources. This work considers an under-determined setting, i.e., the number of receivers is less than number of sources, where deep learning has been shown to be effective in separating multiple sources from one receiver, as illustrated in the literature for audio source separation.  \nSIGNAL MODELING  \nBinary Phase Shift Keying  \nThe signal from the emitter is modeled as Binary Phase Shift Keying (BPSK) .1 The binary sequence of symbols is represented by the vector b =[b1 , b2 , ... , bn]T , where each bi is an independent and identically distributed (i.i.d.) random variable following a Bernoulli distribution with parameter p = 1/2 . We denote the transpose operation with the ()T notation.  \nBecause we use SDRs to process the signal, the signal is sampled at a frequency Fs , which is higher than the symbol rate Rs. The number of samples per symbol NSPS = Fs /Rs can be used to obtain the total sample length as NSamples = NSymbols × NSPS , where NSymbols denotes the total number of symbols in the simulation while NSamples denotes the total number of samples in the simulation. Let’s now define the discrete data signal:  \nm = 2b ⊗ 1NSPS − 1NSamples (1)  \nwhich denotes the bipolar (NRZ) encoding of the bits b so that each element of the vector m ∈ RNsamples × 1 is either −1 or +1 . We represent the Signal of Interest (SOI) as a complex data signal demodulated with a frequency shift fm and phase shift ϕm . The encoded phase shift is determined by the data signal, with the modulation-index parameter β , controlling the amount of phase modulation applied to the carrier:  \ns = ej (βm+2πfm /Fsn+ϕm) (2)  \nwhere n represents the discrete time vector with entries ni = i corresponding to the i-th sample, sis the Signal of Interest (SOI) complex vector. Because it is desired that anomaly detection and mitigation methods perform well regardless of the frequency/phase shifts, fm and ϕm are treated as ran-  \ndom variables sampled from a uniform distribution. fm ∼ U (−fm,max, fm,max), ϕm ∼ U(0, 2π) (3)  \nwhere fm,max denotes the maximum frequency offset we model in our experiments.  \nReceived Signal Model  \nThe receiver captures ","cbCainubsuv3nzNf","https://ap.wps.com/l/cbCainubsuv3nzNf","pdf",4124137,1,11,"English","en",105,"# Abstract\n# Signal Modeling\n## Binary Phase Shift Keying\n## Received Signal Model\n## Noise\n## Interference\n## Continuous Wave / Single Tone","[{\"question\":\"What is studied in the anomaly mitigation part of the work?\",\"answer\":\"It addresses mitigating anomalies by removing the anomalous component from the received signal. Independent component analysis is effective when there are more receivers than signal sources, while deep learning is considered for under-determined settings with fewer receivers than sources.\"}]","Machine Learning for Interference Detection and Mitigation on Deep Space Telecom Signals | PDF",1785821711,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":78,"head_meta":80,"extra_data":82,"updated_unix":28},"machine-learning-for-interference-detection-and-mitigation-on-deep-space-telecom-signals","",{"@graph":36,"@context":77},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-for-interference-detection-and-mitigation-on-deep-space-telecom-signals/124339/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71],{"name":72,"@type":73,"acceptedAnswer":74},"What is studied in the anomaly mitigation part of the work?","Question",{"text":75,"@type":76},"It addresses mitigating anomalies by removing the anomalous component from the received signal. Independent component analysis is effective when there are more receivers than signal sources, while deep learning is considered for under-determined settings with fewer receivers than sources.","Answer","https://schema.org",{"og:url":52,"og:type":79,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":81,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":84},[85,89,93,97,102,105,110,115,120,123,127],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":98,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":103,"slug":104},50,"technology",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},7,"Healthcare",40,"healthcare",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},8,"Research & Report",30,"research-report",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":98,"slug":130},19,"General","general"]